diff --git a/doc/LectureNotes/E1.ipynb b/doc/LectureNotes/E1.ipynb
index 81041b4af..8fb96d5b1 100644
--- a/doc/LectureNotes/E1.ipynb
+++ b/doc/LectureNotes/E1.ipynb
@@ -125,7 +125,7 @@
     "\n",
     "There are many ways to manage your packages in Python, and you are free to use any approach you want, but in this course we encourage you to use something called a virtual environment. A virtual environemnt is a folder in your project which contains a Python runtime executable as well as all the packages you are using in the current project. In this way, each of your projects has its required set of packages installed in the same folder, so that if anything goes wrong while managing your packages it only affects the one project, and if multiple projects require different versions of the same package, you don't need to worry about messing up old projects. Also, it's easy to just delete the folder and start over if anything goes wrong.\n",
     "\n",
-    "Virtual environments are typically created, activated, managed and updated using terminal commands, but for now we recommend that you let VS Code handle it for you to make the coding experience much easier.\n"
+    "Virtual environments are typically created, activated, managed and updated using terminal commands, but for now we recommend that you let VS Code handle it for you to make the coding experience much easier. If you are familiar with another approach for virtual environments that works for you, feel free to keep doing it that way.\n"
    ]
   },
   {
@@ -178,7 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 1,
    "id": "b58fb9bf",
    "metadata": {},
    "outputs": [],
@@ -192,13 +192,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 2,
    "id": "0208e9ca",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -214,10 +214,12 @@
     "\n",
     "line_model = LinearRegression().fit(x, y)\n",
     "line_predict = line_model.predict(x)\n",
+    "#line_mse = ...\n",
     "\n",
     "#poly_features = ...\n",
     "#poly_model = LinearRegression().fit(..., y)\n",
     "#poly_predict = ...\n",
+    "#poly_mse = ...\n",
     "\n",
     "plt.scatter(x, y, label = \"Data\")\n",
     "plt.scatter(x, line_predict, label = \"Line model\")\n",
@@ -243,7 +245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": null,
    "id": "0f8d75fb",
    "metadata": {},
    "outputs": [],
@@ -269,14 +271,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": null,
    "id": "a03e0388",
    "metadata": {},
    "outputs": [],
    "source": [
     "polynomial_features = ...\n",
     "\n",
-    "#X_train, X_test, y_train, y_test = train_test_split(polynomial_features, y, test_size=0.2)"
+    "#X_train, X_test, y_train, y_test = train_test_split(polynomial_features, y, test_size=0.2)\n",
+    "\n"
    ]
   },
   {
diff --git a/doc/LectureNotes/E2.ipynb b/doc/LectureNotes/E2.ipynb
index 99e855700..f7405e0df 100644
--- a/doc/LectureNotes/E2.ipynb
+++ b/doc/LectureNotes/E2.ipynb
@@ -224,12 +224,16 @@
    "source": [
     "With the expression for $\\boldsymbol{\\hat{\\beta}_{OLS}}$, you now have what you need to implement OLS regression with your input data and target data $\\boldsymbol{y}$. But before you can do that, you need to set up you input data as a feature matrix $\\boldsymbol{X}$.\n",
     "\n",
-    "In a feature matrix, each row is a datapoint and each column is a feature of that data. If you want to predict someones spending based on their income and number of children, for instance, you would create a row for each person in your dataset, and in each column put a 1 for the intercept, the montly income and the number of children."
+    "In a feature matrix, each row is a datapoint and each column is a feature of that data. If you want to predict someones spending based on their income and number of children, for instance, you would create a row for each person in your dataset, with the montly income and the number of children as columns.\n",
+    "\n",
+    "We typically also include an intercept in our models. The intercept is a value that is added to our prediction regardless of the value of the other features. The intercept tries to account for constant effects in our data that are not dependant on anything else. In our current example, the intercept could account for living expenses which are typical regardless of income or childcare expenses.\n",
+    "\n",
+    "We calculate the optimal intercept by including a feature with the constant value of 1 in our model, which is then multplied by some parameter $\\beta_0$ from the OLS method into the optimal intercept value (which will be $\\beta_0$). In practice, we include the intercept in our model by adding a column of ones to the start of our feature matrix."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": null,
    "id": "e5ff2a69",
    "metadata": {},
    "outputs": [],
@@ -239,7 +243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": null,
    "id": "a3cf2792",
    "metadata": {},
    "outputs": [],
@@ -260,7 +264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": null,
    "id": "5ad87a65",
    "metadata": {},
    "outputs": [],
@@ -281,7 +285,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "id": "8f3f68aa",
    "metadata": {},
    "outputs": [],
@@ -312,7 +316,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "id": "d7476c84",
    "metadata": {},
    "outputs": [],
@@ -332,7 +336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": null,
    "id": "91496e40",
    "metadata": {},
    "outputs": [],
@@ -358,7 +362,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": null,
    "id": "034f502c",
    "metadata": {},
    "outputs": [],
@@ -376,7 +380,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": null,
    "id": "29171358",
    "metadata": {},
    "outputs": [],
@@ -396,21 +400,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": null,
    "id": "1e346f4c",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Ellipsis"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "..."
    ]
@@ -425,21 +418,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
    "id": "ceb57457",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Ellipsis"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "..."
    ]
diff --git a/doc/LectureNotes/_build/.doctrees/E1.doctree b/doc/LectureNotes/_build/.doctrees/E1.doctree
index b45298ac8..f5fd3f1e5 100644
Binary files a/doc/LectureNotes/_build/.doctrees/E1.doctree and b/doc/LectureNotes/_build/.doctrees/E1.doctree differ
diff --git a/doc/LectureNotes/_build/.doctrees/E2.doctree b/doc/LectureNotes/_build/.doctrees/E2.doctree
index 140bfe335..5360976aa 100644
Binary files a/doc/LectureNotes/_build/.doctrees/E2.doctree and b/doc/LectureNotes/_build/.doctrees/E2.doctree differ
diff --git a/doc/LectureNotes/_build/.doctrees/environment.pickle b/doc/LectureNotes/_build/.doctrees/environment.pickle
index 6639ceffe..366514268 100644
Binary files a/doc/LectureNotes/_build/.doctrees/environment.pickle and b/doc/LectureNotes/_build/.doctrees/environment.pickle differ
diff --git a/doc/LectureNotes/_build/html/E1.html b/doc/LectureNotes/_build/html/E1.html
index d36af1be7..a48d143a7 100644
--- a/doc/LectureNotes/_build/html/E1.html
+++ b/doc/LectureNotes/_build/html/E1.html
@@ -446,7 +446,7 @@ document.write(`
 <h2>Exercise 3 - Setting up a Python virtual environment<a class="headerlink" href="#exercise-3-setting-up-a-python-virtual-environment" title="Link to this heading">#</a></h2>
 <p>Following the theme of the previous exercises, another way of improving the reproducibility of your results and shareability of your code is having a good handle on which python packages you are using.</p>
 <p>There are many ways to manage your packages in Python, and you are free to use any approach you want, but in this course we encourage you to use something called a virtual environment. A virtual environemnt is a folder in your project which contains a Python runtime executable as well as all the packages you are using in the current project. In this way, each of your projects has its required set of packages installed in the same folder, so that if anything goes wrong while managing your packages it only affects the one project, and if multiple projects require different versions of the same package, you don’t need to worry about messing up old projects. Also, it’s easy to just delete the folder and start over if anything goes wrong.</p>
-<p>Virtual environments are typically created, activated, managed and updated using terminal commands, but for now we recommend that you let VS Code handle it for you to make the coding experience much easier.</p>
+<p>Virtual environments are typically created, activated, managed and updated using terminal commands, but for now we recommend that you let VS Code handle it for you to make the coding experience much easier. If you are familiar with another approach for virtual environments that works for you, feel free to keep doing it that way.</p>
 <p><strong>a)</strong> Open this notebook in VS Code (<a class="reference external" href="https://code.visualstudio.com/Download">https://code.visualstudio.com/Download</a>). Download the Python and Jupyter extensions.</p>
 <p><strong>b)</strong> Press ´Cmd + Shift + P´, then search and run ´Python: Create Environment…´</p>
 <p><strong>c)</strong> Select ´Venv´</p>
@@ -483,10 +483,12 @@ document.write(`
 
 <span class="n">line_model</span> <span class="o">=</span> <span class="n">LinearRegression</span><span class="p">()</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
 <span class="n">line_predict</span> <span class="o">=</span> <span class="n">line_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="c1">#line_mse = ...</span>
 
 <span class="c1">#poly_features = ...</span>
 <span class="c1">#poly_model = LinearRegression().fit(..., y)</span>
 <span class="c1">#poly_predict = ...</span>
+<span class="c1">#poly_mse = ...</span>
 
 <span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s2">&quot;Data&quot;</span><span class="p">)</span>
 <span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">line_predict</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s2">&quot;Line model&quot;</span><span class="p">)</span>
@@ -496,7 +498,7 @@ document.write(`
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="_images/fbd74da3fcd21613c4c425622594644bbfe45b6a7fd50ac62da4e3136a737128.png" src="_images/fbd74da3fcd21613c4c425622594644bbfe45b6a7fd50ac62da4e3136a737128.png" />
+<img alt="_images/c62786c19b580c6638248aa3cfe7dea30bdbc00a922e7e45a45a5a7a053bdb38.png" src="_images/c62786c19b580c6638248aa3cfe7dea30bdbc00a922e7e45a45a5a7a053bdb38.png" />
 </div>
 </div>
 </section>
diff --git a/doc/LectureNotes/_build/html/E2.html b/doc/LectureNotes/_build/html/E2.html
index 593a86433..378605a2f 100644
--- a/doc/LectureNotes/_build/html/E2.html
+++ b/doc/LectureNotes/_build/html/E2.html
@@ -497,7 +497,9 @@ f_i =\sum_{j=0}^{n-1}a_{ij}x_j,
 <section id="exercise-3-creating-feature-matrix-and-implementing-ols-using-the-analytical-expression">
 <h2>Exercise 3 - Creating feature matrix and implementing OLS using the analytical expression<a class="headerlink" href="#exercise-3-creating-feature-matrix-and-implementing-ols-using-the-analytical-expression" title="Link to this heading">#</a></h2>
 <p>With the expression for <span class="math notranslate nohighlight">\(\boldsymbol{\hat{\beta}_{OLS}}\)</span>, you now have what you need to implement OLS regression with your input data and target data <span class="math notranslate nohighlight">\(\boldsymbol{y}\)</span>. But before you can do that, you need to set up you input data as a feature matrix <span class="math notranslate nohighlight">\(\boldsymbol{X}\)</span>.</p>
-<p>In a feature matrix, each row is a datapoint and each column is a feature of that data. If you want to predict someones spending based on their income and number of children, for instance, you would create a row for each person in your dataset, and in each column put a 1 for the intercept, the montly income and the number of children.</p>
+<p>In a feature matrix, each row is a datapoint and each column is a feature of that data. If you want to predict someones spending based on their income and number of children, for instance, you would create a row for each person in your dataset, with the montly income and the number of children as columns.</p>
+<p>We typically also include an intercept in our models. The intercept is a value that is added to our prediction regardless of the value of the other features. The intercept tries to account for constant effects in our data that are not dependant on anything else. In our current example, the intercept could account for living expenses which are typical regardless of income or childcare expenses.</p>
+<p>We calculate the optimal intercept by including a feature with the constant value of 1 in our model, which is then multplied by some parameter <span class="math notranslate nohighlight">\(\beta_0\)</span> from the OLS method into the optimal intercept value (which will be <span class="math notranslate nohighlight">\(\beta_0\)</span>). In practice, we include the intercept in our model by adding a column of ones to the start of our feature matrix.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
@@ -592,11 +594,6 @@ f_i =\sum_{j=0}^{n-1}a_{ij}x_j,
 </pre></div>
 </div>
 </div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Ellipsis
-</pre></div>
-</div>
-</div>
 </div>
 <p><strong>e)</strong> Do the same for each polynomial degree from 2 to 10, and plot the MSE on both the training and test data as a function of polynomial degree. The aim is to reproduce Figure 2.11 of <a class="reference external" href="https://github.com/CompPhysics/MLErasmus/blob/master/doc/Textbooks/elementsstat.pdf">Hastie et al</a>. Feel free to read the discussions leading to figure 2.11 of Hastie et al.</p>
 <div class="cell docutils container">
@@ -605,11 +602,6 @@ f_i =\sum_{j=0}^{n-1}a_{ij}x_j,
 </pre></div>
 </div>
 </div>
-<div class="cell_output docutils container">
-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Ellipsis
-</pre></div>
-</div>
-</div>
 </div>
 <p><strong>f)</strong> Interpret the graph. Why do the lines move as they do? What does it tell us about model performance and generalizability?</p>
 </section>
diff --git a/doc/LectureNotes/_build/html/_images/c62786c19b580c6638248aa3cfe7dea30bdbc00a922e7e45a45a5a7a053bdb38.png b/doc/LectureNotes/_build/html/_images/c62786c19b580c6638248aa3cfe7dea30bdbc00a922e7e45a45a5a7a053bdb38.png
new file mode 100644
index 000000000..000d16539
Binary files /dev/null and b/doc/LectureNotes/_build/html/_images/c62786c19b580c6638248aa3cfe7dea30bdbc00a922e7e45a45a5a7a053bdb38.png differ
diff --git a/doc/LectureNotes/_build/html/_images/fbd74da3fcd21613c4c425622594644bbfe45b6a7fd50ac62da4e3136a737128.png b/doc/LectureNotes/_build/html/_images/fbd74da3fcd21613c4c425622594644bbfe45b6a7fd50ac62da4e3136a737128.png
deleted file mode 100644
index 6c951d90d..000000000
Binary files a/doc/LectureNotes/_build/html/_images/fbd74da3fcd21613c4c425622594644bbfe45b6a7fd50ac62da4e3136a737128.png and /dev/null differ
diff --git a/doc/LectureNotes/_build/html/_sources/E1.ipynb b/doc/LectureNotes/_build/html/_sources/E1.ipynb
index 81041b4af..8fb96d5b1 100644
--- a/doc/LectureNotes/_build/html/_sources/E1.ipynb
+++ b/doc/LectureNotes/_build/html/_sources/E1.ipynb
@@ -125,7 +125,7 @@
     "\n",
     "There are many ways to manage your packages in Python, and you are free to use any approach you want, but in this course we encourage you to use something called a virtual environment. A virtual environemnt is a folder in your project which contains a Python runtime executable as well as all the packages you are using in the current project. In this way, each of your projects has its required set of packages installed in the same folder, so that if anything goes wrong while managing your packages it only affects the one project, and if multiple projects require different versions of the same package, you don't need to worry about messing up old projects. Also, it's easy to just delete the folder and start over if anything goes wrong.\n",
     "\n",
-    "Virtual environments are typically created, activated, managed and updated using terminal commands, but for now we recommend that you let VS Code handle it for you to make the coding experience much easier.\n"
+    "Virtual environments are typically created, activated, managed and updated using terminal commands, but for now we recommend that you let VS Code handle it for you to make the coding experience much easier. If you are familiar with another approach for virtual environments that works for you, feel free to keep doing it that way.\n"
    ]
   },
   {
@@ -178,7 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 1,
    "id": "b58fb9bf",
    "metadata": {},
    "outputs": [],
@@ -192,13 +192,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 2,
    "id": "0208e9ca",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -214,10 +214,12 @@
     "\n",
     "line_model = LinearRegression().fit(x, y)\n",
     "line_predict = line_model.predict(x)\n",
+    "#line_mse = ...\n",
     "\n",
     "#poly_features = ...\n",
     "#poly_model = LinearRegression().fit(..., y)\n",
     "#poly_predict = ...\n",
+    "#poly_mse = ...\n",
     "\n",
     "plt.scatter(x, y, label = \"Data\")\n",
     "plt.scatter(x, line_predict, label = \"Line model\")\n",
@@ -243,7 +245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": null,
    "id": "0f8d75fb",
    "metadata": {},
    "outputs": [],
@@ -269,14 +271,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": null,
    "id": "a03e0388",
    "metadata": {},
    "outputs": [],
    "source": [
     "polynomial_features = ...\n",
     "\n",
-    "#X_train, X_test, y_train, y_test = train_test_split(polynomial_features, y, test_size=0.2)"
+    "#X_train, X_test, y_train, y_test = train_test_split(polynomial_features, y, test_size=0.2)\n",
+    "\n"
    ]
   },
   {
diff --git a/doc/LectureNotes/_build/html/_sources/E2.ipynb b/doc/LectureNotes/_build/html/_sources/E2.ipynb
index 99e855700..f7405e0df 100644
--- a/doc/LectureNotes/_build/html/_sources/E2.ipynb
+++ b/doc/LectureNotes/_build/html/_sources/E2.ipynb
@@ -224,12 +224,16 @@
    "source": [
     "With the expression for $\\boldsymbol{\\hat{\\beta}_{OLS}}$, you now have what you need to implement OLS regression with your input data and target data $\\boldsymbol{y}$. But before you can do that, you need to set up you input data as a feature matrix $\\boldsymbol{X}$.\n",
     "\n",
-    "In a feature matrix, each row is a datapoint and each column is a feature of that data. If you want to predict someones spending based on their income and number of children, for instance, you would create a row for each person in your dataset, and in each column put a 1 for the intercept, the montly income and the number of children."
+    "In a feature matrix, each row is a datapoint and each column is a feature of that data. If you want to predict someones spending based on their income and number of children, for instance, you would create a row for each person in your dataset, with the montly income and the number of children as columns.\n",
+    "\n",
+    "We typically also include an intercept in our models. The intercept is a value that is added to our prediction regardless of the value of the other features. The intercept tries to account for constant effects in our data that are not dependant on anything else. In our current example, the intercept could account for living expenses which are typical regardless of income or childcare expenses.\n",
+    "\n",
+    "We calculate the optimal intercept by including a feature with the constant value of 1 in our model, which is then multplied by some parameter $\\beta_0$ from the OLS method into the optimal intercept value (which will be $\\beta_0$). In practice, we include the intercept in our model by adding a column of ones to the start of our feature matrix."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": null,
    "id": "e5ff2a69",
    "metadata": {},
    "outputs": [],
@@ -239,7 +243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": null,
    "id": "a3cf2792",
    "metadata": {},
    "outputs": [],
@@ -260,7 +264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": null,
    "id": "5ad87a65",
    "metadata": {},
    "outputs": [],
@@ -281,7 +285,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "id": "8f3f68aa",
    "metadata": {},
    "outputs": [],
@@ -312,7 +316,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "id": "d7476c84",
    "metadata": {},
    "outputs": [],
@@ -332,7 +336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": null,
    "id": "91496e40",
    "metadata": {},
    "outputs": [],
@@ -358,7 +362,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": null,
    "id": "034f502c",
    "metadata": {},
    "outputs": [],
@@ -376,7 +380,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": null,
    "id": "29171358",
    "metadata": {},
    "outputs": [],
@@ -396,21 +400,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": null,
    "id": "1e346f4c",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Ellipsis"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "..."
    ]
@@ -425,21 +418,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
    "id": "ceb57457",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Ellipsis"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "..."
    ]
diff --git a/doc/LectureNotes/_build/html/searchindex.js b/doc/LectureNotes/_build/html/searchindex.js
index e1c82f28d..b34be884b 100644
--- a/doc/LectureNotes/_build/html/searchindex.js
+++ b/doc/LectureNotes/_build/html/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"alltitles": {"A Classification Tree": [[11, "a-classification-tree"]], "A Frequentist approach to data analysis": [[2, "a-frequentist-approach-to-data-analysis"], [23, "a-frequentist-approach-to-data-analysis"]], "A better approach": [[10, "a-better-approach"]], "A first summary": [[23, "a-first-summary"]], "A quick Reminder on Lagrangian Multipliers": [[10, "a-quick-reminder-on-lagrangian-multipliers"]], "A simple example": [[6, "a-simple-example"]], "A soft classifier": [[10, "a-soft-classifier"]], "A top-down perspective on Neural networks": [[3, "a-top-down-perspective-on-neural-networks"]], "ADAM optimizer": [[15, "adam-optimizer"]], "Activation functions": [[14, "activation-functions"]], "Adaptive boosting: AdaBoost, Basic Algorithm": [[12, "adaptive-boosting-adaboost-basic-algorithm"]], "Adding error analysis and training set up": [[23, "adding-error-analysis-and-training-set-up"]], "Adjust hyperparameters": [[3, "adjust-hyperparameters"]], "Algorithms for Setting up Decision Trees": [[11, "algorithms-for-setting-up-decision-trees"]], "An Overview of Ensemble Methods": [[12, "an-overview-of-ensemble-methods"]], "An extrapolation example": [[6, "an-extrapolation-example"]], "An optimization/minimization problem": [[23, "an-optimization-minimization-problem"]], "And what about using neural networks?": [[23, "and-what-about-using-neural-networks"]], "Another example, the moons again": [[11, "another-example-the-moons-again"]], "Applied Data Analysis and Machine Learning": [[17, null]], "Autocorrelation function": [[20, "autocorrelation-function"]], "Automatic differentiation": [[15, "automatic-differentiation"]], "Back to the Cancer Data": [[13, "back-to-the-cancer-data"]], "Bagging": [[12, "bagging"]], "Bagging Examples": [[12, "bagging-examples"]], "Basic Matrix Features": [[18, "basic-matrix-features"]], "Basic ideas of the Principal Component Analysis (PCA)": [[13, null]], "Basic math of the SVD": [[7, "basic-math-of-the-svd"]], "Basics": [[9, "basics"]], "Basics of a tree": [[11, "basics-of-a-tree"]], "Batch Normalization": [[3, "batch-normalization"]], "Bayes\u2019 Theorem and Ridge and Lasso Regression": [[7, "bayes-theorem-and-ridge-and-lasso-regression"]], "Boosting, a Bird\u2019s Eye View": [[12, "boosting-a-bird-s-eye-view"]], "Bootstrap": [[8, "bootstrap"]], "Bringing it together, first back propagation equation": [[14, "bringing-it-together-first-back-propagation-equation"]], "Building a Feed Forward Neural Network": [[3, null]], "Building a tree, regression": [[11, "building-a-tree-regression"]], "Building neural networks in Tensorflow and Keras": [[3, "building-neural-networks-in-tensorflow-and-keras"]], "CNNs in more detail, building convolutional neural networks in Tensorflow and Keras": [[5, "cnns-in-more-detail-building-convolutional-neural-networks-in-tensorflow-and-keras"]], "Cancer Data again now with Decision Trees and other Methods": [[11, "cancer-data-again-now-with-decision-trees-and-other-methods"]], "Choose cost function and optimizer": [[3, "choose-cost-function-and-optimizer"]], "Classical PCA Theorem": [[13, "classical-pca-theorem"]], "Clustering and Unsupervised Learning": [[16, null]], "Code for SVD and Inversion of Matrices": [[7, "code-for-svd-and-inversion-of-matrices"]], "Codes and Approaches": [[16, "codes-and-approaches"]], "Codes for the SVD": [[7, "codes-for-the-svd"]], "Coding Setup and Linear Regression": [[0, "coding-setup-and-linear-regression"]], "Collect and pre-process data": [[3, "collect-and-pre-process-data"]], "Communication channels": [[23, "communication-channels"]], "Compare  Bagging on Trees with Random Forests": [[12, "compare-bagging-on-trees-with-random-forests"]], "Comparing with a numerical scheme": [[4, "comparing-with-a-numerical-scheme"]], "Computing the Gini index": [[11, "computing-the-gini-index"]], "Conjugate gradient method": [[15, "conjugate-gradient-method"]], "Convex functions": [[15, "convex-functions"]], "Convolution Examples: Polynomial multiplication": [[5, "convolution-examples-polynomial-multiplication"]], "Convolution Examples: Principle of Superposition and Periodic Forces (Fourier Transforms)": [[5, "convolution-examples-principle-of-superposition-and-periodic-forces-fourier-transforms"]], "Convolutional Neural Network": [[14, "convolutional-neural-network"]], "Convolutional Neural Networks": [[5, null]], "Correlation Matrix": [[13, "correlation-matrix"]], "Course Format": [[23, "course-format"]], "Course setting": [[19, null]], "Cross-validation": [[8, "cross-validation"]], "Deadlines for projects (tentative)": [[23, "deadlines-for-projects-tentative"]], "Decision trees, overarching aims": [[11, null]], "Deep learning methods": [[23, "deep-learning-methods"]], "Define model and architecture": [[3, "define-model-and-architecture"]], "Defining the cost function": [[3, "defining-the-cost-function"]], "Deliverables": [[0, "deliverables"], [1, "deliverables"]], "Derivatives and the chain rule": [[14, "derivatives-and-the-chain-rule"]], "Deriving OLS from a probability distribution": [[7, "deriving-ols-from-a-probability-distribution"]], "Deriving and Implementing Ordinary Least Squares": [[1, "deriving-and-implementing-ordinary-least-squares"]], "Deriving the back propagation code for a multilayer perceptron model": [[14, "deriving-the-back-propagation-code-for-a-multilayer-perceptron-model"]], "Developing a code for doing neural networks with back propagation": [[3, "developing-a-code-for-doing-neural-networks-with-back-propagation"]], "Diagonalize the sample covariance matrix to obtain the principal components": [[13, "diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components"]], "Different kernels and Mercer\u2019s theorem": [[10, "different-kernels-and-mercer-s-theorem"]], "Disadvantages": [[11, "disadvantages"]], "Domains and probabilities": [[20, "domains-and-probabilities"]], "Dropout": [[3, "dropout"]], "Elements of Probability Theory and Statistical Data Analysis": [[20, null]], "Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods": [[12, null]], "Entropy and the ID3 algorithm": [[11, "entropy-and-the-id3-algorithm"]], "Essential elements of ML": [[23, "essential-elements-of-ml"]], "Evaluate model performance on test data": [[3, "evaluate-model-performance-on-test-data"]], "Example: Exponential decay": [[4, "example-exponential-decay"]], "Example: Population growth": [[4, "example-population-growth"]], "Example: The diffusion equation": [[4, "example-the-diffusion-equation"]], "Example: binary classification problem": [[3, "example-binary-classification-problem"]], "Examples": [[23, "examples"]], "Examples of likelihood functions used in logistic regression and neural networks": [[9, "examples-of-likelihood-functions-used-in-logistic-regression-and-neural-networks"]], "Exercise 1 - Finding the derivative of Matrix-Vector expressions": [[1, "exercise-1-finding-the-derivative-of-matrix-vector-expressions"]], "Exercise 1 - Github Setup": [[0, "exercise-1-github-setup"]], "Exercise 1: Setting up various Python environments": [[2, "exercise-1-setting-up-various-python-environments"], [23, "exercise-1-setting-up-various-python-environments"]], "Exercise 2 - Deriving the expression for OLS": [[1, "exercise-2-deriving-the-expression-for-ols"]], "Exercise 2 - Setting up a Github repository": [[0, "exercise-2-setting-up-a-github-repository"]], "Exercise 2: making your own data and exploring scikit-learn": [[2, "exercise-2-making-your-own-data-and-exploring-scikit-learn"], [23, "exercise-2-making-your-own-data-and-exploring-scikit-learn"]], "Exercise 3 - Creating feature matrix and implementing OLS using the analytical expression": [[1, "exercise-3-creating-feature-matrix-and-implementing-ols-using-the-analytical-expression"]], "Exercise 3 - Fitting an OLS model to data": [[0, "exercise-3-fitting-an-ols-model-to-data"]], "Exercise 3 - Setting up a Python virtual environment": [[0, "exercise-3-setting-up-a-python-virtual-environment"]], "Exercise 3: Normalizing our data": [[2, "exercise-3-normalizing-our-data"]], "Exercise 3: Split data in test and training data": [[23, "exercise-3-split-data-in-test-and-training-data"]], "Exercise 4 - Fitting a polynomial": [[1, "exercise-4-fitting-a-polynomial"]], "Exercise 4 - The train-test split": [[0, "exercise-4-the-train-test-split"]], "Exercise 4: Adding Ridge Regression": [[2, "exercise-4-adding-ridge-regression"]], "Exercise 5 - Comparing your code with sklearn": [[1, "exercise-5-comparing-your-code-with-sklearn"]], "Exercise 5: Analytical exercises": [[2, "exercise-5-analytical-exercises"]], "Exercise:  Cross-validation as resampling techniques, adding more complexity": [[8, "exercise-cross-validation-as-resampling-techniques-adding-more-complexity"]], "Exercise: Analysis of real data": [[8, "exercise-analysis-of-real-data"]], "Exercise: Bias-variance trade-off and resampling techniques": [[8, "exercise-bias-variance-trade-off-and-resampling-techniques"]], "Exercise: Lasso Regression on the Franke function  with resampling": [[8, "exercise-lasso-regression-on-the-franke-function-with-resampling"]], "Exercise: Ordinary Least Square (OLS) on the Franke function": [[8, "exercise-ordinary-least-square-ols-on-the-franke-function"]], "Exercise: Ridge Regression on the Franke function  with resampling": [[8, "exercise-ridge-regression-on-the-franke-function-with-resampling"]], "Exercises": [[2, "exercises"], [23, "exercises"]], "Exercises and Projects": [[8, "exercises-and-projects"]], "Exercises week 34": [[0, null]], "Exercises week 35": [[1, null]], "Expectation values": [[20, "expectation-values"]], "Extremely useful tools, strongly recommended": [[23, "extremely-useful-tools-strongly-recommended"]], "Feed-forward neural networks": [[14, "feed-forward-neural-networks"]], "Feed-forward pass": [[3, "feed-forward-pass"]], "Final back propagating equation": [[14, "final-back-propagating-equation"]], "Fine-tuning neural network hyperparameters": [[3, "fine-tuning-neural-network-hyperparameters"]], "Fitting an Equation of State for Dense Nuclear Matter": [[2, "fitting-an-equation-of-state-for-dense-nuclear-matter"], [23, "fitting-an-equation-of-state-for-dense-nuclear-matter"]], "From one to many layers, the universal approximation theorem": [[14, "from-one-to-many-layers-the-universal-approximation-theorem"]], "Further Dimensionality Remarks": [[5, "further-dimensionality-remarks"]], "Further properties (important for our analyses later)": [[7, "further-properties-important-for-our-analyses-later"]], "Gaussian Elimination": [[18, "gaussian-elimination"]], "General Features": [[11, "general-features"]], "General linear models and linear algebra": [[23, "general-linear-models-and-linear-algebra"]], "Generalizing the fitting procedure as a linear algebra problem": [[23, "generalizing-the-fitting-procedure-as-a-linear-algebra-problem"], [23, "id1"]], "Generative Adversarial Networks": [[6, "generative-adversarial-networks"]], "Generative Models": [[6, "generative-models"]], "Geometric Interpretation and link with Singular Value Decomposition": [[13, "geometric-interpretation-and-link-with-singular-value-decomposition"]], "Gradient Boosting, Classification Example": [[12, "gradient-boosting-classification-example"]], "Gradient Boosting, Examples of Regression": [[12, "gradient-boosting-examples-of-regression"]], "Gradient Clipping": [[3, "gradient-clipping"]], "Gradient boosting: Basics with Steepest Descent/Functional Gradient Descent": [[12, "gradient-boosting-basics-with-steepest-descent-functional-gradient-descent"]], "Gradient descent": [[4, "gradient-descent"]], "Grading": [[21, "grading"], [21, "id2"], [23, "grading"]], "Housing data, the code": [[2, "housing-data-the-code"]], "How to take derivatives of Matrix-Vector expressions": [[1, "how-to-take-derivatives-of-matrix-vector-expressions"]], "Hyperplanes and all that": [[10, "hyperplanes-and-all-that"]], "Important Matrix and vector handling packages": [[18, "important-matrix-and-vector-handling-packages"]], "Improving performance": [[3, "improving-performance"]], "In summary": [[21, "in-summary"]], "Including Stochastic Gradient Descent with Autograd": [[15, "including-stochastic-gradient-descent-with-autograd"]], "Incremental PCA": [[13, "incremental-pca"]], "Installing R, C++, cython or Julia": [[23, "installing-r-c-cython-or-julia"]], "Installing R, C++, cython, Numba etc": [[23, "installing-r-c-cython-numba-etc"]], "Instructor information": [[21, "instructor-information"]], "Interpretations and optimizing our parameters": [[23, "interpretations-and-optimizing-our-parameters"], [23, "id2"], [23, "id3"]], "Introducing JAX": [[15, "introducing-jax"]], "Introducing the Covariance and Correlation functions": [[13, "introducing-the-covariance-and-correlation-functions"]], "Introduction": [[2, "introduction"], [8, "introduction"], [17, "introduction"], [18, "introduction"]], "Iterative Fitting, Classification and AdaBoost": [[12, "iterative-fitting-classification-and-adaboost"]], "Iterative Fitting, Regression and Squared-error Cost Function": [[12, "iterative-fitting-regression-and-squared-error-cost-function"]], "Kernel PCA": [[13, "kernel-pca"]], "Kernels and non-linearity": [[10, "kernels-and-non-linearity"]], "LU Decomposition, the inverse of a matrix": [[18, "lu-decomposition-the-inverse-of-a-matrix"]], "Layers": [[3, "layers"]], "Layers used to build CNNs": [[5, "layers-used-to-build-cnns"]], "Learning goals": [[0, "learning-goals"], [1, "learning-goals"]], "Learning outcomes": [[17, "learning-outcomes"], [23, "learning-outcomes"]], "Lectures and ComputerLab": [[23, "lectures-and-computerlab"]], "Limitations of supervised learning with deep networks": [[3, "limitations-of-supervised-learning-with-deep-networks"]], "Linear Algebra, Handling of Arrays and more Python Features": [[18, null]], "Linear Regression": [[2, null]], "Linear Regression, basic elements": [[2, "linear-regression-basic-elements"]], "Linking Bayes\u2019 Theorem with Ridge and Lasso Regression": [[7, "linking-bayes-theorem-with-ridge-and-lasso-regression"]], "Linking the regression analysis with a statistical interpretation": [[7, "linking-the-regression-analysis-with-a-statistical-interpretation"]], "Linking with the SVD": [[7, "linking-with-the-svd"]], "Links to relevant courses at the University of Oslo": [[22, "links-to-relevant-courses-at-the-university-of-oslo"]], "Logistic Regression": [[9, null], [9, "id1"]], "MNIST and GANs": [[6, "mnist-and-gans"]], "Machine Learning": [[23, "machine-learning"]], "Machine learning": [[17, "machine-learning"]], "Main textbooks": [[23, "main-textbooks"]], "Making a tree": [[11, "making-a-tree"]], "Making your own Bootstrap: Changing the Level of the Decision Tree": [[12, "making-your-own-bootstrap-changing-the-level-of-the-decision-tree"]], "Mathematical Interpretation of Ordinary Least Squares": [[7, "mathematical-interpretation-of-ordinary-least-squares"]], "Mathematical optimization of convex functions": [[10, "mathematical-optimization-of-convex-functions"]], "Mathematics of CNNs": [[5, "mathematics-of-cnns"]], "Mathematics of the SVD and implications": [[7, "mathematics-of-the-svd-and-implications"]], "Matrices in Python": [[23, "matrices-in-python"]], "Matrix  multiplication": [[3, "matrix-multiplication"]], "Matrix-vector notation  and activation": [[14, "matrix-vector-notation-and-activation"]], "Meet the  covariance!": [[20, "meet-the-covariance"]], "Meet the Covariance Matrix": [[7, "meet-the-covariance-matrix"]], "Meet the Pandas": [[23, "meet-the-pandas"]], "Momentum based GD": [[15, "momentum-based-gd"]], "More complicated Example: The Ising model": [[8, "more-complicated-example-the-ising-model"]], "More on Dimensionalities": [[5, "more-on-dimensionalities"]], "More on Rescaling data": [[8, "more-on-rescaling-data"]], "Multilayer perceptrons": [[14, "multilayer-perceptrons"]], "Network requirements": [[4, "network-requirements"]], "Neural Networks vs CNNs": [[5, "neural-networks-vs-cnns"]], "Neural networks": [[14, null]], "Numerical experiments and the covariance, central limit theorem": [[20, "numerical-experiments-and-the-covariance-central-limit-theorem"]], "Numpy and arrays": [[18, "numpy-and-arrays"], [23, "numpy-and-arrays"]], "Numpy examples and Important Matrix and vector handling packages": [[23, "numpy-examples-and-important-matrix-and-vector-handling-packages"]], "Optimization, the central part of any Machine Learning algortithm": [[15, null]], "Optimizing our parameters": [[23, "optimizing-our-parameters"]], "Optimizing our parameters, more details": [[23, "optimizing-our-parameters-more-details"]], "Optimizing the cost function": [[3, "optimizing-the-cost-function"]], "Organizing our data": [[2, "organizing-our-data"], [23, "organizing-our-data"]], "Other Matrix and Vector Operations": [[18, "other-matrix-and-vector-operations"]], "Other Types of Recurrent Neural Networks": [[6, "other-types-of-recurrent-neural-networks"]], "Other courses on Data science and Machine Learning  at UiO": [[23, "other-courses-on-data-science-and-machine-learning-at-uio"]], "Other courses on Data science and Machine Learning  at UiO, contn": [[23, "other-courses-on-data-science-and-machine-learning-at-uio-contn"]], "Other popular texts": [[23, "other-popular-texts"]], "Other techniques": [[13, "other-techniques"]], "Other types of networks": [[14, "other-types-of-networks"]], "Other ways of visualizing the trees": [[11, "other-ways-of-visualizing-the-trees"]], "Our model for the nuclear binding energies": [[23, "our-model-for-the-nuclear-binding-energies"]], "Overview of first week": [[23, "overview-of-first-week"]], "Own code for Ordinary Least Squares": [[23, "own-code-for-ordinary-least-squares"]], "PCA and scikit-learn": [[13, "pca-and-scikit-learn"]], "Pandas AI": [[23, "pandas-ai"]], "Partial Differential Equations": [[4, "partial-differential-equations"]], "Practical tips": [[15, "practical-tips"]], "Practicalities": [[21, "practicalities"], [21, "id1"]], "Predicting New Points With A Trained Recurrent Neural Network": [[6, "predicting-new-points-with-a-trained-recurrent-neural-network"]], "Prerequisites": [[23, "prerequisites"]], "Prerequisites and background": [[17, "prerequisites-and-background"]], "Prerequisites: Collect and pre-process data": [[5, "prerequisites-collect-and-pre-process-data"]], "Probability Distribution Functions": [[20, "probability-distribution-functions"]], "Program for stochastic gradient": [[15, "program-for-stochastic-gradient"]], "Properties of PDFs": [[20, "properties-of-pdfs"]], "Pros and cons of trees, pros": [[11, "pros-and-cons-of-trees-pros"]], "Python installers": [[17, "python-installers"], [23, "python-installers"]], "RMS prop": [[15, "rms-prop"]], "Random Numbers": [[20, "random-numbers"]], "Random forests": [[12, "random-forests"]], "Randomized PCA": [[13, "randomized-pca"]], "Reading material": [[23, "reading-material"]], "Reading suggestions week 34": [[23, "reading-suggestions-week-34"]], "Recurrent neural networks": [[14, "recurrent-neural-networks"]], "Recurrent neural networks: Overarching view": [[6, null]], "Reducing the number of degrees of freedom, overarching view": [[2, "reducing-the-number-of-degrees-of-freedom-overarching-view"]], "Reformulating the problem": [[4, "reformulating-the-problem"]], "Regression Case": [[12, "regression-case"]], "Regression analysis, overarching aims": [[23, "regression-analysis-overarching-aims"]], "Regression analysis, overarching aims II": [[23, "regression-analysis-overarching-aims-ii"]], "Regularization": [[3, "regularization"]], "Reminder on Statistics": [[8, "reminder-on-statistics"]], "Replace or not": [[15, "replace-or-not"]], "Required Technologies": [[17, "required-technologies"]], "Resampling Methods": [[8, null]], "Resampling methods": [[8, "id1"]], "Resources on differential equations and deep learning": [[4, "resources-on-differential-equations-and-deep-learning"]], "Revisiting our Linear Regression Solvers": [[15, "revisiting-our-linear-regression-solvers"]], "Rewriting the fitting procedure as a linear algebra problem": [[23, "rewriting-the-fitting-procedure-as-a-linear-algebra-problem"]], "Rewriting the fitting procedure as a linear algebra problem, more details": [[23, "rewriting-the-fitting-procedure-as-a-linear-algebra-problem-more-details"]], "Ridge and Lasso Regression": [[7, null], [7, "id1"]], "Same code but now with momentum gradient descent": [[15, "same-code-but-now-with-momentum-gradient-descent"]], "Schedule first week": [[23, "schedule-first-week"]], "Schematic Regression Procedure": [[11, "schematic-regression-procedure"]], "Setting up the Back propagation algorithm": [[14, "setting-up-the-back-propagation-algorithm"]], "Setting up the network using Autograd; The full program": [[4, "setting-up-the-network-using-autograd-the-full-program"]], "Similar (second order function now) problem but now with AdaGrad": [[15, "similar-second-order-function-now-problem-but-now-with-adagrad"]], "Simple Python Code to read in Data and perform Classification": [[11, "simple-python-code-to-read-in-data-and-perform-classification"]], "Simple linear regression model using scikit-learn": [[2, "simple-linear-regression-model-using-scikit-learn"], [23, "simple-linear-regression-model-using-scikit-learn"]], "Software and needed installations": [[23, "software-and-needed-installations"]], "Solving Differential Equations  with Deep Learning": [[4, null]], "Solving the one dimensional Poisson equation": [[4, "solving-the-one-dimensional-poisson-equation"]], "Solving the wave equation with Neural Networks": [[4, "solving-the-wave-equation-with-neural-networks"]], "Some famous Matrices": [[18, "some-famous-matrices"]], "Some simple problems": [[15, "some-simple-problems"]], "Splitting our Data in Training and Test data": [[2, "splitting-our-data-in-training-and-test-data"], [23, "splitting-our-data-in-training-and-test-data"]], "Standard steepest descent": [[15, "standard-steepest-descent"]], "Statistical analysis and optimization of data": [[17, "statistical-analysis-and-optimization-of-data"], [23, "statistical-analysis-and-optimization-of-data"]], "Steepest descent": [[15, "steepest-descent"]], "Stochastic Gradient Descent (SGD)": [[15, "stochastic-gradient-descent-sgd"]], "Stochastic variables and the main concepts, the discrete case": [[20, "stochastic-variables-and-the-main-concepts-the-discrete-case"]], "Support Vector Machines, overarching aims": [[10, null]], "Systematic reduction": [[5, "systematic-reduction"]], "Teachers": [[23, "teachers"]], "Teachers and Grading": [[21, null]], "Teaching Assistants Fall semester 2023": [[21, "teaching-assistants-fall-semester-2023"]], "Tentative deadllines for projects": [[21, "tentative-deadllines-for-projects"]], "Testing the Means Squared Error as function of Complexity": [[2, "testing-the-means-squared-error-as-function-of-complexity"]], "Textbooks": [[22, null]], "The Algorithm before theorem": [[13, "the-algorithm-before-theorem"]], "The Boston housing data example": [[2, "the-boston-housing-data-example"]], "The Breast Cancer Data, now with Keras": [[3, "the-breast-cancer-data-now-with-keras"]], "The CART algorithm for Classification": [[11, "the-cart-algorithm-for-classification"]], "The CART algorithm for Regression": [[11, "the-cart-algorithm-for-regression"]], "The CIFAR01 data set": [[5, "the-cifar01-data-set"]], "The MNIST dataset again": [[5, "the-mnist-dataset-again"]], "The RELU function family": [[3, "the-relu-function-family"]], "The Softmax function": [[3, "the-softmax-function"]], "The \\chi^2 function": [[2, "the-chi-2-function"], [23, "the-chi-2-function"], [23, "id4"], [23, "id5"], [23, "id6"], [23, "id7"], [23, "id8"]], "The bias-variance tradeoff": [[8, "the-bias-variance-tradeoff"]], "The code": [[23, "the-code"]], "The code for solving the ODE": [[4, "the-code-for-solving-the-ode"]], "The course has two central parts": [[17, "the-course-has-two-central-parts"]], "The logistic function": [[9, "the-logistic-function"]], "The moons example": [[10, "the-moons-example"]], "The multilayer  perceptron (MLP)": [[14, "the-multilayer-perceptron-mlp"]], "The network with one input layer, specified number of hidden layers, and one output layer": [[4, "the-network-with-one-input-layer-specified-number-of-hidden-layers-and-one-output-layer"]], "The singular value decomposition": [[7, "the-singular-value-decomposition"]], "The two-dimensional case": [[10, "the-two-dimensional-case"]], "To our real data: nuclear binding energies. Brief reminder on masses and binding energies": [[23, "to-our-real-data-nuclear-binding-energies-brief-reminder-on-masses-and-binding-energies"]], "Topics covered in this course: Statistical analysis and optimization of data": [[23, "topics-covered-in-this-course-statistical-analysis-and-optimization-of-data"]], "Towards the PCA theorem": [[13, "towards-the-pca-theorem"]], "Train and test datasets": [[3, "train-and-test-datasets"]], "Two-dimensional Objects": [[5, "two-dimensional-objects"]], "Type of problem": [[4, "type-of-problem"]], "Types of Machine Learning": [[23, "types-of-machine-learning"]], "Useful Python libraries": [[17, "useful-python-libraries"], [23, "useful-python-libraries"]], "Using Autograd": [[15, "using-autograd"]], "Using forward Euler to solve the ODE": [[4, "using-forward-euler-to-solve-the-ode"]], "Using gradient descent methods, limitations": [[15, "using-gradient-descent-methods-limitations"]], "Visualization": [[3, "visualization"], [3, "id1"]], "Visualizing the Tree, Classification": [[11, "visualizing-the-tree-classification"]], "Week 34: Introduction to the course, Logistics and Practicalities": [[23, null]], "What is Machine Learning?": [[2, "what-is-machine-learning"]], "What is a good model?": [[2, "what-is-a-good-model"], [23, "what-is-a-good-model"]], "What is a good model? Can we define it?": [[23, "what-is-a-good-model-can-we-define-it"]], "Which activation function should I use?": [[3, "which-activation-function-should-i-use"]], "Why Linear Regression (aka Ordinary Least Squares and family)": [[23, "why-linear-regression-aka-ordinary-least-squares-and-family"]], "Wisconsin Cancer Data": [[9, "wisconsin-cancer-data"]], "Writing Our First Generative Adversarial Network": [[6, "writing-our-first-generative-adversarial-network"]], "Writing our own PCA code": [[13, "writing-our-own-pca-code"]], "XGBoost: Extreme Gradient Boosting": [[12, "xgboost-extreme-gradient-boosting"]], "scikit-learn implementation": [[3, "scikit-learn-implementation"]]}, "docnames": ["E1", "E2", "chapter1", "chapter10", "chapter11", "chapter12", "chapter13", "chapter2", "chapter3", "chapter4", "chapter5", "chapter6", "chapter7", "chapter8", "chapter9", "chapteroptimization", "clustering", "intro", "linalg", "schedule", "statistics", "teachers", "textbooks", "week34"], "envversion": {"sphinx": 62, "sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.intersphinx": 1}, "filenames": ["E1.ipynb", "E2.ipynb", "chapter1.ipynb", "chapter10.ipynb", "chapter11.ipynb", "chapter12.ipynb", "chapter13.ipynb", "chapter2.ipynb", "chapter3.ipynb", "chapter4.ipynb", "chapter5.ipynb", "chapter6.ipynb", "chapter7.ipynb", "chapter8.ipynb", "chapter9.ipynb", "chapteroptimization.ipynb", "clustering.ipynb", "intro.md", "linalg.ipynb", "schedule.md", "statistics.ipynb", "teachers.md", "textbooks.md", "week34.ipynb"], "indexentries": {}, "objects": {}, "objnames": {}, "objtypes": {}, "terms": {"": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 13, 14, 15, 17, 18, 20, 21, 23], "0": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 21, 23], "00": [2, 3, 7, 13, 23], "000": [3, 5], "001": [4, 10, 15], "004": 7, "00727646693": [2, 23], "0086649156": [2, 23], "01": [2, 3, 4, 7, 11, 13, 15, 22, 23], "0110": 20, "02": [2, 6, 9, 14, 23], "02857": 6, "02f": 8, "03077640549": 6, "031": 7, "04": 13, "0458": 11, "05": [6, 8], "062292565": 6, "0713": [2, 23], "07285": 5, "08": 20, "08336233266": 6, "0917": 11, "0n": [2, 23], "1": [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22], "10": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 19, 20, 21, 23], "100": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 15, 16, 18, 20, 21, 23], "1000": [2, 3, 4, 6, 7, 10, 13, 15, 16, 17, 20, 23], "10000": [4, 7, 8, 12, 13, 15, 20], "100000": 10, "10001": 12, "1001": 20, "1002": 20, "1003": 20, "1005": 20, "1009": 20, "101": 1, "1011": 20, "1013": 20, "1013904243": 20, "1015": 20, "102": 1, "1023": 20, "1024": 5, "1026": 20, "1027": 20, "103": 3, "1030": 20, "1037": 20, "1038": 20, "1040": 20, "1047": 20, "107": 1, "10th": 11, "10x": [2, 23], "11": [1, 2, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 22, 23], "1100": 20, "1101": 20, "111": [3, 9, 14], "112": 1, "116": 1, "117": 1, "118": 1, "12": [2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 14, 18, 20, 22, 23], "120": 5, "121": [1, 10, 11, 12], "1215pm": [21, 23], "122": [10, 11, 12], "124": [2, 23], "125": 1, "127": [1, 6], "128": [5, 6, 15], "129": 1, "1298": 11, "12pm": [21, 23], "13": [2, 4, 11, 14, 18, 20, 23], "131": 1, "133": 9, "135": 1, "136": 1, "14": [2, 4, 6, 8, 10, 11, 12, 14, 18, 20, 22], "141": 1, "143": 1, "1446729567": 6, "149": 1, "14g": 8, "15": [2, 4, 6, 8, 9, 10, 11, 14, 15, 20, 23], "150": [6, 10], "152": 1, "156": 1, "159": 1, "15g": 8, "15pm": 23, "16": [3, 4, 5, 6, 7, 10, 11, 12, 20, 23], "160": 1, "1603": 5, "161": 1, "162": 1, "16231451": 6, "163": 1, "16384": 5, "164": 1, "167": 1, "17": [3, 4, 10, 20], "172": 1, "173": 1, "176": 1, "178": 1, "179": 1, "1797": 3, "18": [4, 8, 9, 10, 11, 12, 20, 23], "1807": 6, "19": [4, 20, 23], "1940": 2, "1943": 14, "1970": [18, 23], "1973": 11, "1979": 8, "1_1": 14, "1_2": 14, "1_3": 14, "1cm": [2, 10, 12, 20, 23], "1d": [3, 4, 5], "1e": [4, 6, 15, 16], "1e10": 16, "1e4": 8, "1f": 3, "1k": 18, "1n": [2, 23], "1x": [2, 23], "2": [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22], "20": [1, 2, 3, 4, 8, 9, 10, 20, 21, 23], "200": [2, 4, 5, 6, 10, 11, 12], "2000": 2, "2004": 15, "2006": 22, "2008": 23, "2010": 3, "2011": 3, "2014": 6, "2015": 3, "2016": [2, 23], "2018": [2, 8], "2021": [8, 16], "2022": 23, "2025": 23, "21": [2, 3, 7, 9, 11, 14, 18, 23], "2116753732": 6, "215pm": [21, 23], "22": [2, 3, 7, 14, 15, 18, 23], "221": 10, "225": 6, "23": [3, 14, 18], "24": [2, 3, 18, 23], "25": [4, 5, 6, 7, 8, 10, 11, 13], "250": [4, 6, 9, 11], "25000": 2, "255": 5, "256": 6, "265109911": 6, "27": [2, 3], "27n_": 20, "28": [3, 5, 6], "2830637392": 6, "2861": 20, "2873": 11, "2882": 20, "2886": 20, "2890": [2, 23], "2892": 20, "2915": 20, "2931": 23, "2968": 23, "2980": 23, "2990": 23, "2_": 14, "2_1": 14, "2_2": 14, "2_3": 14, "2_i": 14, "2_m": [8, 20], "2_t": 15, "2_x": 20, "2b": 20, "2cm": 10, "2d": [3, 5, 13, 14, 17, 23], "2e": 8, "2f": [2, 9, 11, 12, 13, 14, 23], "2g": 4, "2g_i": 4, "2k": 5, "2m": 8, "2n": [2, 4, 5, 23], "2nd": 11, "2p": 20, "2pt": 6, "2x": [2, 5, 10, 15, 23], "2x_ix_jy_iy_j": 10, "2x_j": 10, "2y_i": 12, "2y_j": 10, "3": [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21], "30": [2, 3, 6, 8, 9, 12, 15, 21], "30000": [2, 23], "3072": 5, "31": [14, 18, 20], "315": 8, "3155": [2, 7, 8], "32": [5, 6, 8, 14, 15, 18, 20], "3200": 3, "3250": 3, "33": [14, 18, 21], "333": 9, "34": 18, "3436": [2, 23], "3437": [2, 23], "35": [2, 8, 23], "3581341341": 6, "359": 7, "36": [2, 7, 8, 20], "370782966": 6, "38": 20, "39": [2, 21, 23], "3d": [1, 4, 5, 6, 8, 15], "3f": [3, 5, 11], "3n": 18, "3x": [4, 10], "3x_i": 4, "3y": 10, "4": [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "40": [3, 8, 21, 23], "400": 6, "4000": 23, "4050": [22, 23], "41": 18, "4155": [0, 4], "42": [3, 6, 10, 11, 12, 18], "43": [2, 9, 18], "4310": 23, "436462435": 6, "44": [2, 18], "45": [21, 23], "46": [21, 23], "462": 9, "47": [21, 23], "479465113": 6, "48257387": [21, 23], "49": [7, 8, 13], "49152": 5, "4940954": [2, 23], "4990": 20, "4992": 20, "4997": 20, "4c4c7f": [11, 12], "4d": 5, "4f": 8, "4pm": [21, 23], "4y": 10, "4y_i": 12, "5": [0, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "50": [3, 4, 5, 6, 8, 9, 10, 12, 15, 23], "500": [3, 5, 6, 8, 11, 12, 15], "5018": 20, "506": 2, "507d50": [11, 12], "50j": 15, "50x10": 3, "51": 12, "510": 3, "5177783846": 6, "53": 11, "54": [8, 20], "55": 3, "56": 3, "56536": [2, 23], "569": 3, "57": [2, 10, 21, 23], "571": 7, "58": [12, 21, 23], "591317992": 6, "5cm": 20, "5f": 10, "5x": 10, "5y": 10, "6": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 18, 20, 21, 23], "60": [3, 5], "60000": 6, "6019067271": 6, "625": 9, "63": [2, 3], "64": [3, 5, 6, 15, 18, 23], "64x50": 3, "65": [3, 10, 11], "6887363571": 6, "69": [1, 20], "69069n_": 20, "6n_": 20, "6pm": 23, "7": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 18, 20, 22, 23], "70": [3, 9], "70653767": 6, "71": 3, "724": 5, "75": [7, 8, 10, 13], "76": [21, 23], "765": 9, "77": [21, 23], "7718": 11, "7782028952": 6, "7d7d58": [11, 12], "8": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 18, 20, 21, 23], "80": [2, 3, 7, 10], "800": [6, 9], "81": 3, "815am": [21, 23], "85": 3, "8702784034": 6, "88": 23, "8f": 8, "8g": 8, "8n": 18, "8x8": 3, "9": [2, 3, 4, 6, 7, 8, 9, 10, 11, 13, 14, 15, 18, 20, 23], "90": 3, "9040": 11, "91": [21, 23], "92": [21, 23], "93": 1, "931": [2, 23], "933": 7, "937": 20, "938": 20, "939": [2, 20, 23], "94": 20, "95": [3, 13], "954": 20, "955820c21e8b": 6, "96": 8, "960": 20, "961": 20, "962": 20, "9649652536": 6, "9780387310732": 22, "9780387848570": 22, "9781098134174": 23, "9781492032632": 22, "9781801819312": 23, "98": [1, 2, 3], "985": 20, "986": 20, "989": 20, "9898ff": [11, 12], "99": [1, 15], "991": 20, "992": 20, "993": 20, "996": 7, "999": [11, 20], "9x": 8, "9y": 8, "A": [0, 1, 4, 5, 7, 8, 9, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22], "AND": 4, "And": [2, 5, 6, 7, 8, 11, 15, 17, 20], "As": [1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 14, 15, 18, 20, 23], "At": [2, 6, 8, 15, 23], "BE": [2, 23], "Be": [4, 17, 23], "Being": 15, "But": [1, 2, 3, 4, 5, 7, 8, 11, 12, 20], "By": [2, 5, 7, 8, 14, 15, 18, 23], "For": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22, 23], "IF": 8, "IN": 22, "If": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 20, 23], "In": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22, 23], "Ising": [7, 14], "It": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "Its": [3, 4, 6, 13], "No": [8, 11, 23], "Not": [2, 3, 7, 8], "OR": 20, "Of": 20, "On": [2, 5, 20, 21, 22, 23], "One": [2, 3, 5, 6, 7, 8, 9, 10, 13, 14, 15, 20], "Or": [2, 3, 8, 23], "Such": [1, 2, 8, 14, 20], "That": [2, 7, 9, 12, 13, 14, 16, 20, 23], "The": [1, 6, 12, 15, 16, 18, 19, 20, 21, 22], "Then": [0, 1, 2, 3, 8, 10, 11, 12, 13, 14, 15, 16, 18, 23], "There": [0, 2, 5, 6, 7, 8, 10, 11, 13, 14, 16, 18, 20, 21, 23], "These": [2, 5, 6, 7, 10, 11, 12, 13, 14, 15, 16, 18, 20, 21, 23], "To": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20], "With": [1, 2, 7, 8, 10, 11, 12, 13, 14, 16, 18, 20, 23], "_": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 23], "_0": [7, 10, 12, 13, 15], "_1": [4, 7, 8, 10, 12, 13, 14, 15, 16, 18], "_2": [4, 7, 10, 13, 14, 15, 18], "_3": 18, "_4": 18, "_9": 15, "__class__": 12, "__doc__": 8, "__future__": [10, 11], "__init__": 3, "__main__": 4, "__name__": [4, 12], "_auto1": [4, 5, 6, 7, 8, 9, 14, 15, 18, 20], "_auto10": [8, 14], "_auto11": 8, "_auto12": 8, "_auto2": [4, 5, 6, 7, 8, 14, 15, 18, 20], "_auto3": [5, 6, 7, 8, 14, 15, 18], "_auto4": [6, 8, 14, 15, 18], "_auto5": [6, 8, 14, 15, 18], "_auto6": [6, 8, 14, 18], "_auto7": [6, 8, 14, 18], "_auto8": [8, 14], "_auto9": [8, 14], "_build": [2, 17, 22, 23], "_c": 3, "_compon": 13, "_depth": 11, "_export": [0, 1], "_fraction": 11, "_i": [2, 3, 4, 7, 8, 9, 10, 13, 14, 15, 23], "_j": [2, 3, 4, 5, 7, 8, 10, 15], "_k": 15, "_l": 14, "_lambda": [8, 23], "_leaf": 11, "_m": 12, "_n": [4, 7, 10, 13, 15], "_node": 11, "_p": [7, 10], "_ratio": 13, "_sampl": 11, "_split": [8, 11], "_t": 15, "_test": 8, "_varianc": 13, "_weight": 11, "a0": 5, "a0faa0": [11, 12], "a1": [2, 23], "a2": [2, 23], "a3": [2, 23], "a4": [2, 23], "a_": [1, 2, 3, 18, 23], "a_0": [2, 23], "a_1a": [2, 23], "a_2a": [2, 23], "a_3": [2, 23], "a_3a": [2, 23], "a_4": [2, 23], "a_4a": [2, 23], "a_h": 3, "a_i": [2, 3, 4, 14, 23], "a_j": [3, 14], "a_k": [2, 3, 14], "aaron": 22, "ab": [2, 4, 7, 15, 16, 23], "ab_channel": 17, "abandon": 3, "abid": 20, "abil": [2, 12], "abl": [1, 2, 3, 6, 7, 8, 9, 12, 14, 15], "about": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 21], "abov": [1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 22, 23], "abovement": [8, 23], "abscissa": 15, "absolut": [2, 4, 7, 8, 15, 23], "abstract": 3, "acceler": 15, "accept": [2, 5, 8, 11, 23], "access": [2, 5, 13, 20, 23], "accid": [6, 8], "accompani": [2, 23], "accomplish": [10, 11, 15], "accord": [2, 3, 4, 7, 8, 11, 14, 15, 16, 20, 23], "accordingli": 13, "account": [0, 2, 5, 7, 15, 20, 23], "accumul": [14, 15, 20], "accur": [2, 5, 6, 8, 12, 15], "accuraci": [2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 14, 23], "accuracy_scor": [2, 3, 12, 23], "accuracy_score_numpi": 3, "achiev": [2, 3, 7, 8, 10, 14, 18, 23], "aco": 20, "acquaint": 17, "acquir": [3, 17, 23], "acr": 2, "across": [3, 5, 8, 11, 17, 23], "act": [3, 5, 18], "action": 20, "activ": [0, 2, 4, 5, 6, 11, 19, 21, 23], "actual": [0, 1, 2, 3, 6, 7, 8, 10, 13, 18, 20, 23], "ad": [0, 3, 5, 6, 7, 10, 15, 18], "ada_clf": 12, "adaboostclassifi": 12, "adadelta": 15, "adam": [3, 5, 6, 23], "adapt": [6, 8, 15, 22], "add": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 13, 14, 20, 21, 23], "add_subplot": [3, 9, 14, 16], "addendum": 7, "addit": [2, 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 17, 18, 20, 21, 22, 23], "addition": [14, 15], "address": [3, 11, 13, 15, 23], "adjac": [5, 14], "adjoint": 7, "adjust": [2, 7, 14, 15], "admir": [2, 23], "advanc": [6, 8, 14, 22, 23], "advantag": [3, 5, 7, 8, 12, 15, 18], "adversari": 23, "afecionado": 23, "affect": [0, 5], "affin": [2, 5, 10, 13], "afford": 5, "aficionado": 23, "aforement": 16, "african": 2, "after": [0, 1, 2, 3, 4, 6, 7, 8, 11, 13, 14, 15, 17, 18, 20, 23], "afterward": [2, 23], "ag": [2, 9, 23], "ag_0": 4, "again": [2, 3, 6, 7, 8, 9, 10, 12, 13, 14, 15, 20, 23], "against": [3, 6, 9, 12], "agegroup": 9, "agegroupmean": 9, "aggreg": [11, 12], "agorithm": 12, "agre": [7, 8, 20], "agreement": 15, "ahead": 11, "ai": [2, 22], "aid": 13, "aim": [1, 2, 3, 6, 8, 9, 13, 16, 17, 18], "ainv": 7, "airplan": 5, "aka": 7, "al": [1, 2, 4, 6, 22, 23], "alarm": [7, 9], "algebra": [2, 5, 7, 15, 17], "algorithm": [1, 2, 3, 4, 6, 7, 8, 9, 10, 15, 16, 17, 18, 20, 22, 23], "align": [2, 4, 7, 8, 9, 10, 15, 20, 23], "all": [0, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], "allevi": [3, 15], "alloc": [5, 18], "allow": [2, 3, 4, 5, 7, 8, 10, 12, 15, 17, 18, 23], "almost": [2, 3, 8, 10, 13, 15, 20], "alon": [4, 11], "along": [0, 4, 5, 6, 7, 8, 11, 12, 13, 17, 18, 23], "alpha": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 15, 16, 20, 23], "alpha_": 12, "alpha_0": 5, "alpha_1": 5, "alpha_2": 5, "alpha_i": [5, 15], "alpha_k": 15, "alpha_m": 12, "alpha_n": 5, "alpha_opt": 15, "alreadi": [0, 4, 5, 6, 7, 8, 12, 14, 17, 18, 20, 23], "also": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "alter": 3, "altern": [2, 3, 6, 7, 8, 10, 11, 13, 15, 18, 23], "although": [1, 2, 3, 7, 8, 10, 12, 15, 23], "alwai": [1, 2, 5, 7, 8, 14, 15, 20, 23], "am": 6, "ame2016": [2, 23], "american": 2, "among": [2, 5, 7, 11, 12, 14, 18, 23], "amongst": 7, "amount": [2, 3, 5, 6, 8, 10, 12, 16, 17], "an": [1, 3, 4, 5, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 20, 21, 22], "an_": 20, "anaconda": [2, 3, 17, 23], "analogi": 15, "analys": 8, "analysi": [3, 5, 6, 9, 16, 18, 22], "analyt": [4, 5, 7, 8, 9, 14, 15, 17, 23], "analyz": [1, 2, 3, 5, 6, 7, 8, 20], "andrew": 3, "angl": [2, 5, 11], "anharmon": 5, "ani": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 20, 23], "anim": [6, 14], "ann": 14, "annot": [2, 3, 5, 9, 10, 23], "announc": 23, "anoth": [0, 2, 3, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 18, 20, 23], "ansatz": [2, 23], "answer": [2, 3, 5, 7, 8, 18, 21, 23], "antialias": [4, 8], "anticip": 6, "anymor": [3, 10], "anyon": [0, 6, 10], "anyth": [0, 3, 20], "anytim": [21, 23], "apach": 3, "apart": [13, 15], "api": [3, 17, 23], "appar": 4, "appear": [2, 3, 5, 15, 18, 20], "append": [3, 5, 6, 10, 11, 15, 23], "appli": [2, 3, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 20, 22, 23], "applic": [1, 2, 3, 5, 6, 7, 8, 9, 11, 14, 15, 18, 20, 22, 23], "apply_gradi": 6, "approach": [0, 1, 3, 4, 6, 7, 8, 11, 12, 13, 14, 15, 17, 20, 22], "appropri": [4, 8, 11, 14, 15, 17, 20], "approv": 23, "approx": [2, 4, 5, 8, 12, 13, 15, 20, 23], "approxim": [2, 3, 4, 5, 6, 7, 8, 9, 12, 13, 15, 20, 23], "apt": [2, 17, 23], "aq": 20, "ar": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], "aragorn": 23, "arang": [3, 5, 6, 8, 9, 11, 12, 14, 15, 23], "arbitrari": [3, 6, 8, 10, 14, 15, 20], "arbitrarili": [2, 3, 13, 23], "arc": 8, "architectur": [5, 6, 14], "area": [2, 5, 8, 22, 23], "argmax": [3, 13], "argmin": [6, 12, 16], "argsort": 13, "argu": [3, 15], "argument": [2, 4, 5, 7, 13, 14, 15, 23], "aris": [2, 8, 14, 15, 20, 23], "arithmet": [2, 15, 18, 23], "arm": 8, "armadillo": 18, "around": [2, 3, 6, 7, 8, 13, 20, 23], "arrai": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 20], "arrang": [5, 23], "arraybox": 15, "arriv": [2, 8, 11, 13, 18, 20, 23], "arrow": 14, "arrowprop": 10, "art": [2, 3, 17, 23], "articl": [2, 5, 6, 8, 12, 23], "artifici": [2, 4, 9, 14, 22, 23], "artificialneuron": 14, "arug": 15, "arxiv": [5, 6], "asarrai": [2, 8, 11], "ask": [0, 7, 8, 13, 14], "aspect": [2, 8, 17, 23], "assembl": 5, "assembli": [2, 23], "assert": 6, "assess": [2, 8, 23], "assici": 6, "assign": [0, 2, 9, 10, 11, 14, 15, 16, 19, 21, 22, 23], "associ": [2, 8, 11, 14, 16, 20, 23], "assum": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "assumpt": [2, 5, 7, 8, 11, 13, 20, 23], "ast": [2, 7, 8, 23], "astronomi": 23, "astyp": [6, 11, 12], "asymmetri": [2, 23], "asymptot": [6, 8], "atom": [2, 23], "attempt": [2, 6, 8, 9, 10, 12, 23], "attend": 23, "attent": [2, 18, 23], "attract": [2, 12, 23], "attribut": [2, 11, 23], "audi": [2, 23], "audio": [5, 6], "august": 23, "aurelien": [2, 22, 23], "austfjel": 8, "auth": 0, "authent": 0, "author": [2, 3, 12, 20], "authour": 23, "auto": [11, 12, 20], "autocor": 20, "autocorrelation_tim": 20, "autocorrelform": 20, "autocovari": 20, "autoencod": [6, 17, 23], "autoencond": 17, "autograd": [17, 23], "autom": [2, 17, 22, 23], "automac": 18, "automag": 23, "automat": [1, 2, 3, 4, 5, 6, 13, 17, 18, 23], "automobil": 5, "autonom": 6, "avail": [2, 3, 6, 8, 12, 13, 17, 18, 19, 21, 22, 23], "averag": [2, 3, 5, 8, 11, 12, 15, 16, 20, 21, 23], "avoid": [2, 6, 7, 8, 11, 13, 15, 18, 23], "awai": [4, 5, 8], "awar": [4, 12], "award": [21, 23], "ax": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 23], "axes3d": [4, 8, 15], "axes_grid1": 8, "axhlin": 10, "axi": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "axiom": 7, "axlabel": [2, 23], "axvlin": [6, 10], "axvspan": 6, "b": [0, 1, 2, 3, 5, 6, 7, 8, 10, 11, 12, 14, 15, 16, 20, 21, 23], "b1": 10, "b2": 10, "b3": 10, "b_": [2, 3, 18], "b_0": 2, "b_1": [2, 4, 14, 15], "b_2": [2, 15], "b_5": 15, "b_group": 11, "b_i": [2, 3, 4, 14, 23], "b_ia_": [2, 23], "b_ia_i": 2, "b_index": 11, "b_j": [3, 14], "b_k": [2, 3, 14, 15], "b_m": 14, "b_score": 11, "b_valu": 11, "babcock": 23, "bachelor": [19, 21], "back": [0, 1, 2, 5, 6, 7, 8, 10, 11, 12, 18, 20, 23], "backbon": 18, "backend": [3, 6], "background": [22, 23], "backpropag": 3, "backtrack": 11, "backup": 18, "backward": [3, 4, 6, 14, 18], "bad": 8, "badli": 20, "bag": [11, 17, 23], "bag_clf": 12, "baggin": 23, "baggingboot": 12, "baggingclassifi": 12, "baggingtre": 12, "balanc": 8, "band": 18, "bandwidth": 18, "bar": [2, 8, 13, 23], "barber": 22, "bare": [6, 12], "base": [1, 2, 3, 5, 6, 7, 9, 10, 11, 12, 16, 17, 20, 21, 22, 23], "basi": [7, 9, 10, 12, 13, 14, 15, 18], "basic": [0, 8, 10, 14, 15, 16, 17, 20, 23], "batch": [5, 6, 13, 14, 15], "batch_shap": 6, "batch_siz": [3, 5, 6], "batchnorm": 6, "bay": 9, "bayesian": [7, 17, 22, 23], "beam": 23, "becaus": [2, 3, 4, 5, 6, 7, 8, 10, 11, 14, 15, 16, 23], "becom": [2, 3, 4, 7, 8, 9, 11, 14, 15, 20, 23], "been": [2, 3, 4, 5, 6, 7, 8, 13, 14, 15, 17, 18, 23], "befor": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 14, 15, 16, 18, 20, 23], "beforehand": [2, 20, 23], "begin": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 18, 20, 21, 23], "behav": [3, 8, 15], "behavior": [2, 3, 15, 23], "behaviour": 14, "behind": [2, 3, 8, 10, 15, 23], "being": [2, 3, 4, 5, 6, 7, 9, 10, 12, 13, 14, 15, 20, 23], "believ": [11, 18], "belong": [9, 10, 11, 15, 16], "below": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "benchmark": 12, "benefici": [3, 15], "benefit": [2, 3, 6, 13, 15, 17, 23], "bengio": [3, 22, 23], "benign": [3, 9], "besid": [6, 7], "bessel": 7, "best": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 21, 23], "beta": [1, 2, 3, 5, 7, 8, 9, 12, 13, 15, 23], "beta_": [2, 5, 8, 9, 15, 23], "beta_0": [2, 3, 5, 7, 8, 9, 15, 23], "beta_0x_": [2, 23], "beta_1": [2, 3, 5, 7, 8, 9, 12, 15, 23], "beta_1x_": [2, 23], "beta_1x_0": [2, 23], "beta_1x_1": [2, 9, 23], "beta_1x_2": [2, 23], "beta_1x_i": [9, 15], "beta_2": [2, 5, 15, 23], "beta_2x_": [2, 23], "beta_2x_0": [2, 23], "beta_2x_1": [2, 23], "beta_2x_2": [2, 9, 23], "beta_3": 5, "beta_i": [2, 5, 7, 23], "beta_j": [2, 7, 8, 15, 23], "beta_k": 15, "beta_linreg": 15, "beta_m": 12, "beta_mg_m": 12, "beta_n": 5, "beta_p": 9, "beta_px_p": 9, "betavalu": 7, "better": [2, 3, 4, 5, 6, 8, 11, 12, 13, 14, 15, 23], "between": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 20, 23], "beyond": [2, 3, 7, 8, 10, 15, 23], "bf": [15, 16, 18, 20], "bg": 23, "bgd": 15, "bia": [2, 3, 4, 5, 7, 10, 11, 12, 14, 15, 23], "bias": [3, 4, 5, 7, 8, 11, 14], "big": [2, 3, 4, 7, 8, 16], "bigger": [3, 8], "bigr": 14, "bike": 11, "bilbo": 23, "billion": [5, 14, 17], "bin": [2, 9, 20], "binari": [2, 5, 7, 9, 11, 12, 14, 23], "binarycrossentropi": 6, "bind": 2, "binomi": [17, 20, 23], "binsboot": 8, "bioinformat": 2, "biolog": [3, 14], "bios1100": [17, 23], "bird": [2, 5], "birth": 23, "bishop": [22, 23], "bit": [3, 6, 18, 20, 23], "bitwis": 20, "bivari": 4, "bk": [2, 15], "bla": [18, 23], "black": [10, 11, 16], "block": [8, 12, 17, 18, 20, 23], "blog": 23, "blogpost": 6, "blue": [2, 5], "bmatrix": [2, 3, 5, 7, 9, 10, 13, 15, 18, 23], "bmi": 3, "bodi": [2, 3, 6, 14, 23], "bold": 3, "boldfac": [1, 2, 7], "boldsymbol": [1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 15, 16, 23], "boltzmann": [14, 17, 23], "book": [22, 23], "book1": 22, "boolean": 6, "boost": [3, 11, 17, 23], "boostrap": 12, "bootstrap": [3, 15, 17, 23], "borrow": 23, "boston_dataset": 2, "bot": 10, "both": [0, 1, 2, 3, 6, 7, 8, 10, 11, 12, 15, 16, 17, 18, 20, 21, 23], "bottl": 9, "bound": [2, 10, 14], "boundari": [4, 6, 10, 13, 14], "box": [6, 11], "boyd": [10, 15], "bracket": [6, 20], "brain": [3, 9, 14], "branch": 11, "break": [2, 6, 8, 13, 16, 23], "breast": [7, 9, 13], "breviti": 15, "brew": [2, 17, 23], "brg": 10, "briefli": [1, 2, 23], "bring": [2, 7, 8, 12], "britt": [21, 23], "broad": 2, "brought": [15, 17, 23], "brownle": 6, "browser": [0, 23], "brute": [5, 7, 13], "buffer_s": 6, "bui": 6, "build": [1, 2, 6, 7, 8, 12, 18, 20, 23], "built": [2, 3, 5, 6, 8], "bunch": 13, "busi": 2, "byte": [18, 23], "c": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22], "c1": [10, 13], "c2": [10, 13], "c_": [2, 10, 11, 12, 15, 20], "c_0": 20, "c_1": 14, "c_2": 14, "c_3": 14, "c_4": 14, "c_i": [14, 15], "c_k": 20, "ca": [3, 23], "cach": 12, "cal": [2, 10, 12, 14, 15, 23], "calcul": [1, 2, 3, 4, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "call": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23], "calor": 2, "cambridg": [15, 22], "can": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22], "cancel": [2, 15, 23], "cancer": [7, 12], "cancerpd": 9, "candid": [10, 11, 12], "cannot": [2, 3, 6, 7, 8, 9, 10, 11, 20], "canopi": [2, 17, 23], "canva": [0, 1, 23], "cap": 7, "capabl": [2, 3, 10, 15, 17, 23], "capac": [4, 21], "capita": 2, "captur": [6, 13, 14], "car": [5, 6], "card": [2, 9, 23], "cardin": 3, "care": [0, 13], "carefulli": 15, "carlo": [2, 8, 17, 20, 22, 23], "carri": [4, 8, 9], "cart": 12, "case": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 13, 14, 15, 16, 17, 18, 23], "casella": 22, "cast": 3, "cat": [5, 6], "catch": 2, "categor": [2, 3, 5, 11, 13, 23], "categori": [2, 3, 5, 9, 12, 14, 16, 23], "categorical_crossentropi": [3, 5], "caus": [2, 7, 8, 20, 23], "causal": 2, "causat": [2, 23], "cax": 3, "cb": [8, 23], "cbar": 3, "cc": [2, 3, 7, 15, 23], "ccc": [7, 14], "cdf": 20, "cdot": [2, 4, 8, 14, 15, 16, 18, 20, 23], "celebr": 15, "cell": 6, "center": [2, 3, 8, 9, 10, 11, 13, 16, 20, 23], "central": [1, 2, 5, 7, 8, 10, 18, 23], "centroid": [16, 20], "centroid_differ": 16, "centuri": 5, "certain": [2, 5, 8, 9, 11, 20, 23], "cg": 15, "cha": 2, "chain": [2, 3, 15, 17, 20, 23], "challeng": 0, "chanc": [3, 7, 15, 20], "chang": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 14, 15, 16, 18, 20, 23], "channel": 5, "chapter": [1, 2, 8, 12, 13, 18, 22, 23], "chapter3": 2, "charact": [2, 5, 7, 23], "character": [10, 11, 12, 14, 20], "characterist": [2, 3, 5, 12, 15, 23], "charg": [2, 23], "charl": 2, "chase": 6, "chatgpt": 0, "chd": 9, "chddata": 9, "cheap": 7, "cheaper": [3, 15], "check": [0, 1, 2, 3, 5, 6, 7, 13, 15, 18, 23], "checkmark": 5, "checkpoint": 6, "checkpoint_dir": 6, "checkpoint_prefix": 6, "chen": 12, "chiaramont": 4, "children": 1, "choic": [2, 3, 4, 5, 6, 8, 11, 14, 15, 16, 18, 23], "choleski": [7, 18], "choos": [0, 4, 5, 8, 11, 12, 13, 15, 16], "chosen": [1, 2, 3, 4, 8, 10, 11, 12, 15, 20, 23], "chosen_datapoint": 3, "christian": 22, "christoph": [22, 23], "cifar": 5, "cifar10": 5, "circ": [3, 14], "circl": [2, 10, 14], "circuit": 5, "circumfer": 11, "circumv": [3, 7, 15], "ckpt": 6, "clariti": 20, "class": [2, 3, 5, 6, 8, 9, 10, 11, 13, 14, 15, 20, 23], "class_nam": [5, 11], "class_val": 11, "class_valu": 11, "classic": [9, 11, 15], "classif": [2, 5, 7, 8, 9, 10, 13, 14, 17, 22, 23], "classifi": [2, 3, 6, 9, 11, 12, 13, 23], "classificaton": 3, "classifii": 12, "clean": 3, "clear": [3, 7, 12, 14, 15], "clearli": [2, 5, 7, 8, 9, 10, 20], "clever": [3, 12], "clf": [2, 8, 10, 11, 12, 23], "clf3": 2, "clf_lasso": 8, "clf_ridg": [8, 23], "cli": 0, "clip": [5, 20], "clone": [0, 21], "close": [2, 3, 4, 6, 8, 10, 11, 13, 14, 15, 16, 20, 22, 23], "closer": [5, 7, 15], "closest": [10, 13, 15, 16], "closur": [17, 23], "cloud": [17, 23], "cluster": [2, 3, 6, 8, 13, 17, 23], "cluster_label": 16, "cm": [3, 4, 5, 8, 10, 15], "cmap": [2, 3, 4, 5, 6, 8, 10, 11, 12, 23], "cmap_arg": 8, "cmd": [0, 11], "cn_": 20, "cnn": 14, "cnn_kera": 5, "cntk": [17, 23], "co": [2, 4, 5, 8, 11, 15, 23], "code": [5, 6, 8, 9, 10, 17, 18, 20, 22], "coef": [2, 23], "coef0": 10, "coef_": [1, 2, 7, 8, 10, 11, 15, 23], "coeff": 7, "coeffici": [2, 5, 7, 8, 9, 10, 11, 15, 18, 23], "coerc": [2, 8, 23], "coin": [12, 20], "coin_toss": 12, "col": [2, 13, 23], "colab": [17, 23], "cold": 11, "colinear": 2, "collaps": 10, "collect": [2, 4, 8, 12, 13, 17, 20, 22, 23], "collinear": 7, "color": [2, 5, 6, 8, 10, 11, 12, 20], "color_channel": 5, "color_cod": 8, "colorbar": [3, 8], "colsample_bytre": 12, "colsaobject": 12, "column": [1, 2, 3, 4, 7, 8, 9, 10, 11, 13, 14, 18, 23], "columntransform": 11, "com": [0, 1, 6, 8, 17, 22, 23], "combin": [3, 4, 7, 8, 9, 12, 20], "come": [0, 2, 3, 5, 6, 7, 14, 15, 16, 23], "command": [0, 2, 3, 23], "comment": [2, 6, 7, 8, 23], "commerci": [2, 17, 23], "commit": 0, "commod": [2, 23], "common": [1, 2, 3, 5, 7, 8, 9, 11, 13, 15, 16, 20, 23], "commonli": [2, 3, 6, 8, 9, 11, 15, 16], "commun": [2, 14], "commut": 5, "commutatitav": 5, "compact": [2, 3, 5, 7, 8, 9, 11, 13, 14, 15, 16, 23], "compair": 2, "compar": [2, 5, 6, 7, 8, 13, 15, 18, 23], "comparison": [4, 6, 15], "compat": 9, "compet": 2, "competit": 12, "compil": [2, 3, 5, 6, 15, 17, 18, 23], "complet": [0, 1, 2, 4, 5, 6, 11, 14, 23], "completenn": 14, "complex": [1, 3, 7, 10, 11, 13, 14, 15, 23], "complic": [2, 3, 11, 15, 23], "compon": [1, 2, 3, 5, 6, 7, 8, 9, 11, 16, 17, 23], "components_": 13, "compos": [11, 14, 15, 16, 17, 23], "compphys": [1, 2, 8, 17, 19, 21, 22, 23], "compress": [2, 23], "compris": 8, "compromis": 7, "compulsori": [17, 23], "comput": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 17, 18, 19, 20, 22, 23], "computation": [2, 5, 8, 11, 15, 20, 23], "computationalscienceuio": 23, "concaten": [4, 6, 8, 16], "concav": [3, 15], "concentr": [2, 12], "concept": [2, 4, 17, 23], "conceptu": [14, 15], "concern": [2, 3, 6, 9, 23], "concic": 23, "conclud": [2, 7, 15], "conclus": 3, "cond": 4, "conda": [2, 3, 17, 23], "condit": [2, 4, 6, 7, 8, 10, 11, 13, 15, 20, 23], "conduct": 17, "condwav": 4, "confid": [2, 7, 8, 9, 10, 23], "configur": 5, "confirm": [7, 14], "confus": [7, 8, 9, 12, 18], "confusion_matrix": 11, "congruenti": 20, "conjug": [6, 10], "conjugaci": 15, "conjunct": 5, "connect": [2, 3, 5, 6, 11, 13, 14, 15, 18, 23], "consequ": [7, 8, 10, 12, 14, 15], "conserv": [7, 16], "consid": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 18, 20, 23], "consider": [2, 3, 7, 15, 23], "consist": [2, 3, 4, 5, 6, 8, 14, 15, 20], "constant": [2, 4, 6, 7, 8, 10, 14, 15, 20, 23], "constitu": [2, 23], "constitut": [4, 8], "constrain": [3, 5, 7, 9, 13], "constraint": [7, 8, 10, 15], "construct": [2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 18, 20, 23], "contact": [2, 23], "contain": [0, 2, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 18, 20, 22, 23], "contemporari": 23, "content": [0, 3, 17, 18, 23], "context": [8, 12, 15], "contigu": 18, "continu": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 18, 20, 23], "contour": [11, 12, 15], "contourf": [10, 11, 12], "contrast": [3, 6, 11, 12, 14], "contribut": [2, 5, 7, 15, 20, 23], "contributor": 2, "control": [0, 2, 3, 5, 11, 15, 17, 23], "conv": [5, 6], "conv2d": [5, 6], "conv2dtranspos": 6, "convei": 23, "conveni": [2, 7, 8, 14, 15, 18, 23], "convent": 14, "converg": [3, 4, 6, 7, 10, 15, 16], "convert": [2, 3, 6, 7, 11, 13, 15, 18, 23], "converttomatrix": 6, "convex": [6, 7, 9], "convinc": 15, "convolut": [3, 6, 17, 23], "cool": [6, 11], "coolwarm": 8, "coordin": [7, 14, 16], "coorel": 2, "copi": [0, 2, 3, 16], "core": 12, "corel": 23, "coronari": 9, "corr": [2, 7, 9, 13], "correalt": [13, 17], "correct": [0, 2, 3, 4, 5, 6, 7, 9, 15, 18, 20, 23], "correctli": [3, 4, 8, 9, 12], "correl": [2, 3, 5, 7, 8, 9, 12, 14, 15, 17, 20, 23], "correlation_matrix": [2, 7, 9, 13], "correspond": [2, 5, 7, 8, 10, 11, 13, 14, 17, 18, 20, 23], "cortex": 14, "cosin": [5, 8], "cost": [1, 2, 4, 5, 7, 8, 9, 10, 11, 14, 15, 23], "cost_deep_grad": 4, "cost_funct": 4, "cost_function_deep": 4, "cost_function_deep_grad": 4, "cost_function_grad": 4, "cost_grad": 4, "cost_sum": 4, "costol": 15, "could": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "coulomb": [2, 23], "count": [2, 11, 19, 20, 21, 23], "countor": 15, "coupl": [6, 7, 8], "cours": [0, 1, 2, 3, 5, 7, 13, 21], "coursework": 0, "courvil": [22, 23], "cov": [7, 8, 13, 18, 20, 23], "cov_xi": [7, 13], "cov_xx": [7, 13], "cov_yi": [7, 13], "covari": [2, 9, 17, 18, 23], "covariance_matrix": [7, 13, 16], "cover": [2, 7, 17, 21, 22], "covert": [2, 23], "covxi": 20, "covxx": 20, "covxz": 20, "covyi": 20, "covyz": 20, "covzz": 20, "cpu": 3, "craft": 5, "creat": [0, 3, 5, 6, 7, 11, 12, 13, 14, 17, 23], "create_biases_and_weight": 3, "create_convolutional_neural_network_kera": 5, "create_neural_network_kera": 3, "create_x": [7, 13], "credit": [2, 9, 21, 23], "crim": 2, "crime": 2, "criteria": [2, 6, 11, 12, 16, 20, 23], "criterion": [11, 12, 15], "critic": 8, "cross": [2, 3, 5, 9, 11, 12, 15, 17, 20, 23], "cross_entropi": 6, "cross_val_scor": 8, "cross_valid": [9, 12], "crossvalid": 8, "crucial": [3, 20], "cs231": 5, "csr_matrix": [18, 23], "csv": [2, 6, 8, 9, 11, 23], "ctnk": 3, "cubic": 2, "cumbersom": 7, "cumsum": [12, 13, 23], "cumul": [9, 12, 20], "cumulative_heads_ratio": 12, "cup": 7, "current": [0, 3, 4, 5, 6, 15, 16, 22], "curs": 2, "curv": [8, 9, 12, 14], "curvatur": 15, "custom": [8, 16], "custom_cmap": [11, 12], "custom_cmap2": [11, 12], "cutpoint": 11, "cv": [8, 9, 12], "cvxbook": 15, "cvxopt": [7, 10], "cycl": [3, 14], "d": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 21, 23], "d2_g_t": 4, "d_f": 15, "d_g_t": 4, "d_net_out": 4, "da": 5, "dagger": [7, 18], "dai": [3, 11, 17], "damp": 5, "darget": 11, "darkr": 20, "dat": [2, 23], "dat_id": [2, 8, 9, 11, 23], "data": [1, 4, 6, 7, 10, 12, 14, 15, 16, 18, 22], "data1": 16, "data2": 16, "data3": 16, "data4": 16, "data_id": [2, 8, 9, 11, 23], "data_indic": 3, "data_panda": 23, "data_path": [2, 8, 9, 11, 23], "databas": 3, "datafil": [2, 8, 9, 11, 23], "datafram": [2, 6, 7, 9, 11, 13, 23], "datapoint": [1, 3, 7, 8, 9, 13, 15], "datasci": [0, 1], "dataset": [1, 2, 6, 8, 9, 10, 11, 12, 13, 15, 16, 23], "datatyp": 6, "date": [0, 23], "daughter": 12, "david": [22, 23], "dbh": 3, "dbo": 3, "dcomposit": 18, "ddot": 4, "dead": 3, "deal": [2, 3, 5, 7, 8, 10, 13, 15, 16, 18, 20, 23], "dealt": 2, "debt": 9, "debug": [2, 7, 8], "decad": [2, 5], "decai": [2, 15, 20, 23], "decemb": [21, 23], "decent": 12, "decid": [2, 4, 5, 7, 8, 11], "decim": [2, 23], "decis": [2, 3, 10, 13, 17, 22, 23], "decision_funct": 10, "decision_tre": 11, "decisiontreeclassifi": [11, 12], "decisiontreeregressor": [2, 11, 12], "declar": [2, 6, 18, 23], "decompos": [7, 8, 18], "decomposit": [2, 8, 14, 23], "decompost": 7, "deconvolut": 5, "decorrel": [12, 15], "decreas": [3, 4, 6, 7, 8, 12, 13, 15], "deduc": [2, 23], "deep": [5, 9, 14, 15, 17, 22], "deep_neural_network": 4, "deep_param": 4, "deep_tree_clf": [11, 12], "deep_tree_clf1": 11, "deep_tree_clf2": 11, "deepen": [7, 17, 23], "deeper": [2, 5, 6, 23], "deeplearningbook": [22, 23], "deer": 5, "def": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 20, 23], "def_covari": 20, "default": [2, 3, 4, 6, 8, 9, 18, 23], "default_tim": 6, "defect": 7, "defici": 7, "defin": [1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20], "definit": [3, 4, 7, 8, 9, 10, 12, 13, 14, 15, 18, 20], "defint": 20, "degre": [0, 1, 5, 7, 8, 10, 11, 12, 13, 20, 23], "del": 3, "delet": [0, 8], "delimit": 6, "deliv": [0, 19, 23], "delta": [2, 4, 5, 8, 10, 14, 15, 16, 23], "delta_": [3, 18], "delta_0": 5, "delta_1": 5, "delta_2": 5, "delta_3": 5, "delta_4": 5, "delta_5": 5, "delta_h": [2, 3, 23], "delta_j": [5, 14], "delta_k": 14, "delta_l": [3, 5], "delta_momentum": 15, "delta_n": [2, 5, 23], "delug": 17, "delv": 2, "demand": 15, "demonstr": [2, 5, 7, 8, 9, 13, 14, 17, 23], "den": 6, "denomin": [3, 7], "denot": [3, 4, 8, 9, 15, 20], "dens": [3, 5, 6], "densiti": [2, 4, 8, 20, 23], "depart": [21, 23], "depend": [0, 2, 3, 4, 6, 7, 8, 9, 10, 13, 14, 15, 17, 18, 20, 23], "depict": 20, "deploy": [2, 17, 23], "depth": [2, 5, 11, 12, 18], "deriv": [2, 3, 4, 8, 9, 10, 12, 13, 15, 17, 23], "derivati": 15, "derivative_fn": 15, "descend": [7, 11, 13], "descent": [2, 3, 5, 9, 10, 14, 23], "describ": [2, 4, 6, 7, 8, 10, 12, 13, 14, 15, 18, 23], "descript": [2, 10, 11, 23], "design": [2, 3, 5, 6, 7, 8, 9, 12, 13, 14, 15, 23], "designmatrix": [2, 23], "desir": [2, 4, 6, 7, 15, 16, 23], "desktop": 0, "despit": [3, 14], "destroi": 18, "det": [7, 18], "detail": [2, 8, 13, 15, 16, 18], "detect": [5, 10, 14], "determin": [2, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 18, 20, 23], "determinist": [9, 15, 20], "dev": 3, "develop": [2, 5, 7, 10, 12, 13, 14, 17, 18, 23], "deviat": [2, 3, 4, 6, 7, 8, 20, 23], "devis": 14, "df": [6, 10, 13, 15, 23], "df1": 23, "di": 2, "diag": [7, 10], "diagnost": [3, 12], "diagon": [2, 7, 9, 15, 18, 20, 23], "diagonaliz": 7, "diagram": 12, "diagsvd": 8, "dice": [8, 20], "dict": [8, 10], "dictionari": 2, "did": [1, 2, 3, 7, 8, 9, 12, 13, 16, 23], "die": 3, "diff": 4, "diff1": 4, "diff2": 4, "diff_ag": 4, "diffeent": 10, "differ": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22, 23], "differenti": [1, 2, 5, 17, 18, 23], "difficult": [2, 3, 8, 12, 15, 20, 23], "difficulti": [2, 3, 15, 23], "diffonedim": 4, "digit": [2, 3, 5, 6, 8, 21, 23], "dilemma": 15, "dilut": 3, "dim": [6, 13, 16, 18], "dimens": [1, 2, 3, 4, 5, 6, 7, 10, 13, 16, 18, 23], "dimension": [2, 6, 7, 8, 11, 13, 15, 16, 17, 18, 23], "dimensionless": [2, 5, 23], "diment": 18, "dimnsion": 6, "diod": 5, "direct": [2, 3, 4, 6, 13, 14, 15, 16, 23], "directli": [3, 6, 7, 8, 20], "disadvantag": [2, 23], "disappear": [5, 8], "disc_loss": 6, "disc_tap": 6, "discard": [8, 13], "disciplin": [2, 5, 14], "disclaim": 20, "discord": 23, "discourag": [0, 15], "discov": [2, 23], "discover": 7, "discret": [3, 5, 7, 9, 15], "discrimin": [6, 9, 12, 13], "discriminator_loss": 6, "discriminator_loss_list": 6, "discriminator_model": 6, "discriminator_optim": 6, "discuss": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22, 23], "diseas": 9, "disguis": 8, "disord": [3, 9], "displai": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 20, 23], "displaystyl": [2, 7, 23], "disregard": [2, 23], "dissimilar": [13, 16], "dist": 16, "distanc": [2, 10, 11, 13, 16, 20], "distance_list": 11, "distinct": [5, 9, 10, 11, 12, 16], "distinctli": 10, "distinguish": [2, 6, 9, 10, 20, 23], "distplot": 2, "distribut": [2, 3, 6, 8, 9, 12, 13, 15, 16, 17, 18, 23], "distrubut": [2, 17, 23], "dive": [2, 10, 18, 23], "diverg": [3, 15], "divid": [2, 3, 5, 7, 8, 9, 10, 11, 13, 14, 20, 23], "divis": [8, 10, 11, 15, 18, 20], "dna": 9, "dnn": [2, 3, 4, 6, 14, 23], "dnn1": 6, "dnn2_gru2": 6, "dnn_kera": 3, "dnn_model": 3, "dnn_numpi": 3, "dnn_scikit": [2, 3, 23], "do": [0, 1, 2, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 18, 23], "doc": [0, 1, 2, 17, 19, 21, 22, 23], "document": [0, 6, 15], "doe": [1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 13, 14, 15, 18, 20, 23], "doesn": [5, 11, 14], "dog": [3, 5, 6], "domain": [7, 10, 15], "domin": [2, 23], "don": [0, 1, 2, 3, 5, 7, 8, 10, 13, 15, 17, 23], "done": [1, 2, 4, 5, 6, 7, 8, 11, 12, 13, 15, 18, 23], "dot": [2, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "doubl": [1, 5, 6, 18, 23], "doubli": 3, "down": [2, 5, 8, 11, 13, 14, 15], "download": [0, 2, 3, 5, 7, 8, 18, 22, 23], "downsampl": 5, "dozen": 3, "dq": 8, "drag": 15, "dramat": 13, "drastic": 6, "draw": [6, 8, 12, 15], "drawback": [2, 3, 5, 15], "drawn": [3, 6, 8, 9, 13, 20, 23], "drive": [5, 6], "driven": 5, "drop": [2, 3, 7, 8, 13, 15, 20, 23], "dropna": [2, 8, 23], "dropout": 6, "dt": [4, 5, 15, 20], "dtype": [2, 3, 5, 6, 16, 18, 23], "dub": [2, 23], "due": [3, 4, 7, 8, 10, 12, 14, 15, 21, 23], "dummi": 2, "dure": [2, 3, 5, 6, 10, 11, 13, 17, 23], "dwell": 2, "dwh": 3, "dwo": 3, "dx": [4, 5, 10, 20], "dx_1": 20, "dx_1p": 8, "dx_2p": 8, "dx_mp": 8, "dx_n": 20, "dxp": 8, "dy": [3, 10, 20], "dynam": 6, "dz": 10, "e": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 21, 23], "e_": [2, 4, 23], "each": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23], "eapprox": [2, 23], "earli": [3, 15], "earlier": [2, 7, 9, 10, 11, 13, 14, 15, 23], "earthexplor": 8, "eas": [8, 11, 16], "easi": [0, 2, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 23], "easier": [0, 7, 8, 10, 11, 15, 20, 23], "easiest": 15, "easili": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 23], "eastern": [21, 23], "ebind": [2, 23], "eblock": 11, "econometr": 23, "economi": 7, "ecosystem": [17, 23], "ect": 19, "edg": 5, "edgecolor": 8, "edu": 15, "educ": [2, 23], "eff": 20, "effect": [3, 6, 12, 15, 20], "effic": 3, "effici": [2, 5, 12, 15, 17, 18, 20, 23], "efron": 8, "egrad": 15, "eig": [7, 13, 15, 18, 20, 23], "eigen": 20, "eigenpair": [7, 13], "eigenvalu": [2, 7, 10, 13, 15, 18, 23], "eigenvector": [7, 13, 15], "eight": [18, 23], "eigval": [18, 20, 23], "eigvalu": [13, 15], "eigvec": [18, 20, 23], "eigvector": [13, 15], "eir": [21, 23], "eispack": [18, 23], "either": [3, 7, 8, 9, 10, 11, 12, 13, 15, 20, 23], "eivind": [21, 23], "eivinsto": [21, 23], "ekstr\u00f8m": 6, "elabor": 20, "elarn": 5, "electr": [2, 5, 14, 23], "electron": 23, "eleg": 13, "element": [3, 4, 5, 6, 7, 8, 9, 10, 13, 14, 15, 17, 18, 22], "elementari": [12, 15, 18], "elementwis": [5, 15], "elementwise_grad": [4, 15], "elessar": 23, "elif": 16, "elim": 18, "elimin": [5, 10], "elin": [21, 23], "ellipsi": 1, "els": [3, 5, 6, 9, 11, 14, 15, 18], "elu": 3, "elus": [2, 23], "email": [19, 21, 23], "embed": [2, 13], "embodi": 8, "emit": 20, "emner": 22, "emphas": [2, 12, 17, 23], "emphasi": [2, 17, 22, 23], "empir": [3, 13, 20], "emploi": [2, 3, 7, 8, 13, 15, 20, 23], "employ": 2, "empti": [0, 8, 12], "emul": 14, "en": [17, 22], "enabl": 13, "enbodi": 8, "encod": [2, 5, 7, 11, 13, 16, 23], "encompass": [2, 20], "encount": [0, 2, 3, 7, 9, 15, 20, 23], "encourag": 0, "end": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 21, 23], "endpoint": [5, 8], "energi": [2, 6, 8], "enforc": 14, "eng": 22, "engin": [2, 3, 5, 6, 17, 23], "enorm": 5, "enough": [2, 8, 15, 23], "ensembl": [3, 11], "ensur": [2, 3, 4, 5, 7, 8, 13, 15, 20], "entail": 23, "enter": [7, 8], "enthought": [2, 17, 23], "entir": [3, 5, 9, 11, 17, 20, 23], "entiti": [11, 14, 18, 23], "entri": [2, 7, 10, 13, 14, 18, 23], "entropi": [3, 5, 9, 12, 15, 23], "enumer": [2, 3, 4, 5, 6, 8, 10, 23], "env": 20, "environ": [4, 17], "environemnt": 0, "eo": [2, 8, 23], "eol": [2, 23], "eosfit": [2, 23], "epoch": [2, 3, 5, 6, 14, 15, 23], "epsilon": [2, 7, 8, 9, 15, 23], "epsilon_": [2, 23], "epsilon_0": [2, 23], "epsilon_1": [2, 23], "epsilon_2": [2, 23], "epsilon_i": [2, 23], "eq": [5, 15, 16, 18, 20], "eqnarrai": [5, 7, 8], "equal": [1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 14, 15, 16, 18, 20, 23], "equat": [3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20], "equilibrium": [4, 14], "equiv": [5, 15, 18, 20], "equival": [2, 3, 7, 9, 10, 13, 15, 17, 18, 23], "erf": 20, "eriador": 23, "eridg": 23, "err": [2, 12], "err_": 8, "err_sqr": 4, "errat": 15, "erron": 4, "error": [0, 1, 3, 4, 6, 7, 8, 9, 11, 13, 14, 15, 17, 18, 20], "error_estimate_corr_tim": 20, "error_hidden": 3, "error_output": 3, "escap": 15, "especi": [0, 3, 5, 11, 14, 15], "essenti": [0, 2, 7, 8, 11, 12, 14, 16, 20], "establish": [1, 2, 8, 12, 13, 23], "estim": [2, 3, 7, 8, 9, 12, 13, 15, 17, 20, 23], "estimated_mse_fold": 8, "estimated_mse_kfold": 8, "estimated_mse_sklearn": 8, "et": [1, 2, 4, 6, 22, 23], "eta": [2, 3, 5, 10, 14, 15, 23], "eta0": [10, 15], "eta_": 15, "eta_t": 15, "eta_v": [2, 3, 5, 23], "etc": [2, 3, 5, 7, 9, 10, 11, 13, 14, 15, 16, 17, 18, 20], "ethic": 17, "euclidean": [2, 16], "evalu": [0, 1, 2, 4, 5, 6, 7, 8, 11, 15, 20, 23], "evalut": 15, "even": [2, 3, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "evenli": 6, "event": [7, 9, 12, 20], "eventu": [2, 7, 8, 13, 14, 15, 21], "everi": [0, 2, 3, 4, 5, 6, 7, 8, 11, 12, 13, 14, 15, 16, 17, 20, 21, 23], "everyth": [1, 6, 14], "everywher": [6, 15], "evolv": 2, "exact": [2, 7, 13, 14, 15, 18, 20, 23], "exactli": [2, 5, 6, 8, 14, 17], "exam": 23, "examin": 8, "exampl": [1, 7, 13, 14, 15, 17, 18, 20, 22], "exce": [3, 14, 15], "excel": [2, 3, 6, 7, 12, 23], "except": [5, 6, 8, 10, 11, 18], "excess": [2, 23], "excit": 2, "exclud": [3, 8, 14], "exclus": [2, 3, 5, 8, 20, 23], "execut": [0, 4, 7, 15], "exemplifi": 15, "exercic": [21, 23], "exercis": [7, 17, 19, 21], "exhaust": 8, "exhibit": [2, 7, 8, 10, 23], "exist": [2, 3, 4, 5, 7, 8, 9, 10, 11, 15, 18, 23], "exit": [7, 18], "exp": [1, 2, 3, 4, 7, 8, 9, 10, 12, 13, 14, 15, 20, 23], "exp_term": 3, "expand": [7, 9, 13, 15], "expans": [2, 5, 7, 10, 12, 14, 15, 23], "expect": [0, 2, 3, 7, 8, 9, 13, 14, 15, 17, 23], "expectation_value_of_h_wrt_p": 20, "expens": [8, 12, 15], "experi": [0, 2, 3, 8, 10, 15, 17, 23], "experiment": [2, 6, 8, 11, 20, 23], "expert": [3, 11], "explain": [1, 2, 8, 11, 12, 13, 15, 23], "explained_variance_ratio_": 13, "explanatori": [2, 23], "explicit": [2, 5, 8, 15, 18, 23], "explicitli": [2, 6], "explod": 3, "exploit": [2, 5, 14, 15, 23], "explor": [3, 6, 8, 10, 15, 17], "expon": 3, "exponenti": [2, 3, 7, 8, 12, 15, 20, 23], "export": [0, 1, 11], "export_graphviz": 11, "export_text": 11, "exporttext": 11, "expos": 17, "express": [2, 4, 5, 7, 8, 9, 12, 14, 15, 18, 20, 23], "exptmean": 20, "exptvari": 20, "extend": [2, 4, 9, 13, 15, 17, 23], "extens": [0, 2, 14, 17, 23], "extent": [2, 3, 8, 22], "extern": [5, 8, 11], "extra": [0, 3, 5, 7, 21, 23], "extract": [1, 2, 5, 7, 8, 9, 10, 13, 15, 18, 23], "extrapol": [2, 23], "extrem": [0, 1, 2, 3, 6, 7, 8, 9, 10, 11, 15, 18], "extremum": 15, "extrins": 13, "ey": [2, 7, 8, 15, 16, 18, 23], "f": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 15, 16, 18, 20, 21, 23], "f1": 15, "f11": [2, 23], "f12": [2, 23], "f13": [2, 23], "f1_grad": 15, "f1d": 15, "f2": 15, "f2_grad_x1": 15, "f2_grad_x1_analyt": 15, "f2_grad_x2": 15, "f2_grad_x2_analyt": 15, "f3": 15, "f3_grad": 15, "f3_grad_analyt": 15, "f4": 15, "f4_grad": 15, "f4_grad_analyt": 15, "f5": 15, "f5_grad": 15, "f6": 15, "f6_for": 15, "f6_for_grad": 15, "f6_grad_analyt": 15, "f6_while": 15, "f6_while_grad": 15, "f7": 15, "f7_grad": 15, "f7_grad_analyt": 15, "f8": 15, "f8_grad": 15, "f9": [2, 15, 23], "f9_altern": 15, "f9_alternative_grad": 15, "f9_grad": 15, "f_": 12, "f_0": [5, 12], "f_1": [12, 15], "f_2": [14, 15], "f_3": 14, "f_d": 20, "f_grad": 15, "f_grad_analyt": 15, "f_i": [1, 2, 8, 14], "f_m": [5, 12], "f_n": 5, "f_vec": 4, "face": 15, "facecolor": [8, 10, 20], "facil": [2, 17, 23], "facilit": 14, "fact": [2, 3, 5, 7, 11, 13, 14, 15, 23], "factor": [2, 3, 5, 7, 8, 11, 12, 13, 15, 18, 20, 23], "factori": 15, "fade": 8, "fafab0": [11, 12], "fail": [2, 8, 15, 21, 23], "failur": 9, "fairli": [3, 4, 20], "faisal": 1, "fake": 6, "fake_loss": 6, "fake_output": 6, "fall": [10, 11, 19], "fals": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 16, 18, 23], "famili": [2, 9, 10, 20], "familiar": [2, 5, 7, 8, 10, 17, 18, 20, 23], "famou": [8, 14], "far": [1, 2, 5, 6, 7, 8, 10, 13, 14, 15, 16, 23], "fashion": [2, 11, 12, 23], "fast": [3, 5, 8, 12, 14, 15, 17, 20, 23], "faster": [3, 13, 15], "fastest": [15, 18], "favor": 9, "favorit": 20, "fc": 5, "featur": [0, 2, 3, 5, 7, 8, 9, 10, 12, 13, 14, 15, 17, 20, 23], "feature_nam": [2, 3, 9, 11], "feautur": 11, "fed": 3, "feed": [2, 4, 5, 13, 17, 23], "feed_forward": 3, "feed_forward_out": 3, "feed_forward_train": 3, "feedback": [6, 23], "feeddorward": 6, "feedforward": [3, 6, 14], "feel": [1, 2, 7, 8, 13, 15, 17, 21, 23], "feet": 2, "fetch": [0, 8], "few": [3, 5, 6, 7, 11, 20, 23], "fewer": [2, 11, 13, 23], "ffnn": [3, 14], "field": [2, 5, 8, 14, 17], "fifth": [2, 8, 23], "fig": [2, 3, 4, 5, 6, 8, 9, 14, 15, 16, 23], "fig_id": [2, 8, 9, 11, 23], "figaxi": 20, "figsiz": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 23], "figur": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 23], "figure_id": [2, 8, 9, 11, 23], "figurefil": [2, 8, 9, 11, 23], "file": [0, 2, 6, 7, 8, 9, 11, 23], "file_prefix": 6, "filenam": 23, "fill": [7, 11], "filter": [5, 6], "final": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 19, 20, 21, 23], "financ": 2, "find": [0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20, 23], "fine": [2, 16, 23], "finish": 4, "finit": [5, 7, 8, 14, 15, 20], "finnicki": 0, "first": [0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 21, 22], "firsteigvector": 13, "fit": [3, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 20], "fit_beta": 8, "fit_intercept": [1, 2, 7, 8], "fit_mod": 11, "fit_transform": [0, 2, 8, 10, 11, 13], "fiti": [2, 23], "five": [2, 11, 23], "fix": [2, 5, 6, 8, 12, 13, 14, 15, 23], "flag": 6, "flat": [14, 15], "flatten": [3, 5, 6, 7, 18], "flexibl": [3, 8, 10, 12, 14, 23], "flip": [21, 23], "float": [2, 5, 6, 7, 11, 13, 15, 16, 18, 23], "float32": [6, 11], "float64": [6, 18, 23], "flop": [7, 18], "flow": [3, 6, 14], "fluctuat": 7, "fly": 13, "fm": [2, 23], "fmax": 5, "fmesh": 15, "fn": 9, "focu": [0, 2, 5, 6, 7, 8, 17, 22, 23], "focus": [3, 8, 9, 18], "fold": [8, 11], "folder": [0, 2, 6, 8, 23], "follow": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23], "font": [2, 9, 20, 23], "fontdict": 20, "fontsiz": [3, 8, 10, 11, 12, 20], "fontweight": 3, "footprint": 5, "foral": 10, "forc": [2, 7, 8, 12, 13, 23], "forcast": 6, "forecast": [6, 14], "forest": [2, 3, 11, 17, 23], "forget": 13, "form": [1, 2, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 17, 18, 20, 23], "formal": [5, 6, 16, 20], "format": [2, 3, 5, 6, 8, 9, 10, 11, 12, 13, 17, 20, 22], "format_data": 6, "formatstrformatt": [8, 15], "formul": [6, 8, 13, 16], "formula": [5, 15, 20], "forth": [6, 14], "fortran": [2, 17, 18, 23], "fortran2003": [17, 23], "fortran90": 20, "fortun": [2, 13], "forward": [2, 5, 8, 17, 18, 23], "foster": 23, "found": [3, 4, 6, 7, 8, 14, 15, 23], "foundat": [17, 23], "four": [6, 7, 8, 10, 14, 18, 19, 21, 23], "fourier": [2, 23], "fourierdef1": 5, "fourierdef2": 5, "fourierseriessign": 5, "fourth": [14, 23], "fp": 9, "frac": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "fraction": 11, "frame": 9, "framework": [3, 10, 12, 20], "frank": [7, 13], "frankefunct": [7, 8, 13], "fredli": [21, 23], "free": [0, 1, 2, 8, 13, 15, 17, 18, 20, 21, 22, 23], "freecodecamp": 17, "freedom": 7, "freeli": 2, "freez": 0, "frequenc": [5, 8, 9, 20], "frequent": [2, 10, 11, 15], "frequentist": 17, "fresh": 12, "fridai": [21, 23], "friedman": [8, 22, 23], "friendli": 6, "frodo": 23, "frog": 5, "from": [0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 13, 15, 16, 17, 18, 20, 21, 22, 23], "from_cod": 11, "from_logit": [5, 6], "from_tensor_slic": 6, "front": [2, 6, 7, 23], "frustrat": 0, "fulfil": [4, 7, 14], "full": [2, 3, 5, 7, 9, 11, 12, 15, 20, 23], "full_matric": 7, "fulli": [5, 8, 14, 20], "fun": [17, 23], "func": 4, "function": [0, 1, 4, 5, 6, 7, 11, 16, 17, 18], "functionali": 13, "fundament": [2, 8, 17, 23], "funtion": 4, "further": [4, 9, 11, 23], "furthermor": [2, 5, 7, 8, 9, 13, 14, 15, 17, 23], "futur": [2, 6, 10, 11, 23], "fy": [0, 19, 21, 22, 23], "fys5419": [22, 23], "fys5429": [22, 23], "f\u00f8470": [21, 23], "g": [0, 2, 3, 4, 5, 6, 8, 10, 11, 12, 13, 15, 20, 23], "g0": 4, "g_": [4, 11, 12], "g_0": 4, "g_1": [4, 12], "g_2": [4, 12], "g_analyt": 4, "g_dnn_ag": 4, "g_euler": 4, "g_i": 4, "g_m": [5, 12], "g_n": 5, "g_re": 4, "g_t": 4, "g_t_d2t": 4, "g_t_d2x": 4, "g_t_dt": 4, "g_t_hessian": 4, "g_t_hessian_func": 4, "g_t_jacobian": 4, "g_t_jacobian_func": 4, "g_trial": 4, "g_trial_deep": 4, "g_vec": 4, "gain": [3, 7, 9, 11, 12, 15], "galleri": [2, 23], "game": 6, "gamge": 23, "gamma": [2, 4, 10, 11, 12, 13, 15, 23], "gamma1": 10, "gamma2": 10, "gamma_": [2, 23], "gamma_0": 12, "gamma_1": 12, "gamma_1x": 12, "gamma_i": [2, 10, 20, 23], "gamma_j": 15, "gamma_k": 15, "gamma_m": 12, "gamma_x": [2, 23], "gap": 10, "gate": [6, 14], "gather": [2, 3, 14], "gaug": 14, "gaussbacksub": 18, "gaussian": [6, 7, 8, 10, 16, 20], "gaussian_point": 16, "gaussian_rbf": 10, "gave": 15, "gavra": 23, "gbc": 23, "gca": [4, 8, 10, 15], "gd": 3, "gd_clf": 12, "gdclassiffiercgain": 12, "gdclassiffierconfus": 12, "gdclassiffierroc": 12, "gdm": 15, "gdregress": 12, "ge": [3, 7, 9, 20], "gen_loss": 6, "gen_tap": 6, "gender": [2, 23], "genener": 6, "gener": [0, 1, 2, 3, 4, 5, 7, 8, 10, 12, 13, 14, 15, 16, 18, 20, 22], "generaliz": 1, "generallay": 14, "generate_and_save_imag": 6, "generate_imag": 6, "generate_latent_point": 6, "generate_simple_clustering_dataset": 16, "generated_imag": 6, "generator_loss": 6, "generator_loss_list": 6, "generator_model": 6, "generator_optim": 6, "genom": 17, "geodes": 13, "geometr": [2, 15, 23], "geometri": 7, "georg": 22, "geotif": 8, "geq": [4, 7, 10, 11, 15], "geron": [2, 22, 23], "get": [0, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 15, 17, 18, 20, 21, 23], "get_dummi": 11, "get_paramet": 4, "get_split": 11, "get_yaxi": 10, "get_yticklabel": 8, "gg": 23, "gh": 0, "gibb": [17, 23], "gif": 6, "gini": 12, "gini_index": 11, "ginvers": 15, "git": [0, 2, 17, 23], "giter": 15, "github": [2, 17, 19, 21, 22, 23], "gitignor": 0, "gitlab": [2, 17, 23], "give": [2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 20, 23], "given": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "global": [8, 9, 15], "glorot": 3, "gnew": 15, "go": [0, 1, 2, 3, 5, 7, 8, 10, 11, 13, 14, 15, 23], "goal": [2, 9, 11, 23], "goe": [0, 2, 3, 4, 7, 8, 15, 16, 18, 23], "golden": 15, "gone": 7, "gong": 3, "good": [0, 3, 5, 6, 7, 8, 11, 12, 13, 15, 17, 20, 22], "goodfellow": [6, 22, 23], "googl": [3, 6, 17, 23], "got": [3, 8], "gotten": 23, "gov": 8, "gp": 22, "gpu": [3, 15, 17, 23], "grad": [4, 15], "grad_analyt": 15, "grade": 19, "gradient": [2, 5, 6, 9, 10, 11, 14, 17, 23], "gradientboostingclassifi": 12, "gradientboostingregressor": 12, "gradients_of_discrimin": 6, "gradients_of_gener": 6, "gradienttap": 6, "gradual": [3, 16], "grai": [6, 8], "graph": [1, 3, 11, 13, 14, 15], "graph_from_dot_data": 11, "graphic": [2, 3, 11, 23], "grasp": 2, "gray_r": [3, 5], "grayscal": 5, "great": [0, 7, 15], "greater": [3, 9, 20], "greatli": 15, "greedi": 11, "green": [2, 5, 11, 20], "grei": 6, "grid": [3, 5, 8, 9, 10, 14, 20], "grossli": 15, "ground": [2, 23], "group": [0, 2, 8, 9, 11, 16, 17, 19, 21, 23], "groupbi": [2, 23], "grow": [3, 5, 11, 12], "growth": [2, 23], "gru": 6, "guarante": [2, 6, 15, 20, 23], "guess": [3, 6, 12, 15, 16], "guestrin": 12, "guid": 3, "h": [0, 2, 3, 7, 8, 10, 15, 20, 21, 22, 23], "h1": 4, "h_": [2, 15, 23], "h_1": [4, 15], "h_2": [4, 15], "h_m": 12, "ha": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "haanen": [21, 23], "habit": 2, "had": [2, 3, 8, 9, 15, 23], "hadamard": [3, 14, 15], "half": [3, 10, 11], "halv": 12, "hand": [2, 3, 4, 5, 7, 13, 14, 15, 17, 18, 20, 21, 22, 23], "handi": 5, "handl": [0, 2, 3, 4, 7, 11, 13, 17], "handle_unknown": 11, "handsid": 14, "handwrit": 14, "handwritten": [3, 7], "happen": [3, 4, 5, 6, 7, 8, 12, 15, 20], "hard": [3, 9, 10, 12, 15], "hardcopi": [17, 23], "harder": [2, 3], "harmon": 5, "hassl": [2, 17, 23], "hast": [17, 23], "hasti": [1, 2, 8, 22, 23], "hat": [1, 2, 3, 7, 8, 9, 11, 12, 13, 14, 15, 18], "have": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23], "haven": 3, "he": 9, "head": [2, 6, 12, 20], "header": [2, 23], "heads_proba": 12, "health": 2, "hear": [2, 15, 23], "heart": [2, 9, 23], "heatmap": [2, 3, 5, 9, 23], "heavili": 2, "heavisid": 3, "height": [3, 5, 8], "held": 15, "help": [0, 1, 2, 3, 6, 14, 15, 23], "helper": [6, 16], "henc": [2, 7, 8, 10, 11, 12, 14, 15, 23], "henrik": [21, 23], "her": 9, "here": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "hereaft": [2, 10, 14, 23], "hermitian": 18, "hessenberg": 18, "hessian": [2, 4, 7, 15], "heterogen": [11, 12], "hi": 9, "hidden": [3, 5, 6, 14], "hidden_bia": 3, "hidden_bias_gradi": 3, "hidden_layer_s": [2, 3, 23], "hidden_neuron": 6, "hidden_weight": 3, "hidden_weights_gradi": 3, "hierarch": 7, "high": [2, 3, 4, 5, 6, 7, 8, 11, 12, 13, 15, 16, 17, 18, 23], "higher": [2, 3, 5, 7, 8, 10, 15, 23], "highest": [3, 4], "highli": [2, 5, 6, 12, 17, 18, 22, 23], "highwai": 2, "hing": 10, "hint": [0, 1, 15], "hip": 17, "hire": 2, "hist": [6, 8, 9, 20], "histogram": [2, 8, 9, 20], "histor": [9, 13], "histori": [0, 5, 6, 14], "hitherto": 7, "hjorth": [21, 23], "hobbi": 20, "hoc": 7, "hoff": 22, "hold": [3, 5, 8, 15, 16], "holder": [2, 23], "home": 2, "homepag": 23, "homework": [8, 15], "homogen": [3, 5, 11, 12, 15], "honchar": 4, "hopefulli": [0, 2, 13, 20, 23], "horizont": 13, "horlyk": [21, 23], "hors": [5, 9], "hot": [3, 11], "hour": [3, 17, 19, 20, 21, 23], "how": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "howev": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "hspace": [2, 6, 10, 12, 20, 23], "hstack": 3, "htf": 23, "html": [1, 2, 17, 19, 21, 22, 23], "http": [0, 1, 2, 5, 6, 8, 15, 17, 18, 19, 21, 22, 23], "huang": [2, 23], "huber": [2, 23], "huge": [3, 5, 6, 17], "human": [2, 3, 5, 8, 11, 14], "humid": 11, "hundr": 3, "hungri": 3, "hybrid": 19, "hydrogen": [2, 23], "hyperbol": [3, 6, 14], "hyperparam": 10, "hyperparamet": [5, 6, 7, 8, 11, 15, 23], "hyperplan": 13, "h\u00f8rlyk": [21, 23], "i": [0, 1, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22], "i0": [2, 23], "i1": [2, 8, 10, 14, 23], "i2": [2, 10, 14, 23], "i3": [2, 14, 23], "i5": [2, 23], "i_": 15, "i_1": [7, 8], "i_2": [7, 8], "ian": 22, "ic": 3, "id": [9, 15], "ida": [21, 23], "idea": [2, 3, 4, 5, 6, 8, 11, 12, 14, 15, 18], "ideal": [2, 4, 8, 10, 15, 20, 23], "idem": 8, "ident": [7, 8, 14, 15, 18], "identifi": [2, 3, 9, 11, 13, 14, 15, 16, 23], "ieor": 20, "ifi": 22, "ifs": [17, 23], "ignor": [0, 2, 3, 5, 11], "ii": [18, 20], "iii": [18, 23], "ij": [1, 2, 3, 5, 8, 10, 14, 16, 18, 20, 23], "ik": [2, 18, 23], "illustr": [7, 9, 12, 14, 15, 16, 17, 23], "im": 8, "imag": [3, 5, 6, 8, 11, 13, 14, 16, 22, 23], "image_at_epoch_": 6, "image_batch": 6, "image_height": 5, "image_path": [2, 8, 9, 11, 23], "image_width": 5, "imageio": 8, "images_from_seed_imag": 6, "imagin": 3, "immedi": [2, 5, 6, 8, 17, 23], "implement": [2, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 20, 23], "impli": [5, 7, 8, 9, 15, 18], "implicit": 5, "implicitli": [13, 20], "import": [0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20], "importantli": 5, "impos": [2, 8, 13, 14, 23], "imposs": [2, 7, 23], "impress": [2, 14, 23], "improv": [0, 2, 6, 7, 11, 12, 13, 15], "impur": 11, "imread": 8, "imshow": [3, 5, 6, 8], "in3050": [22, 23], "in3310": 23, "in4080": [22, 23], "in4300": [22, 23], "in4310": 22, "in5400": 5, "in5550": 22, "in_out_neuron": 6, "inaccur": 15, "inact": 14, "inadequ": [2, 23], "inch": 8, "includ": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 13, 14, 17, 20, 21, 22, 23], "include_bia": [8, 11], "incom": [1, 14], "incorrect": 3, "incoveni": 10, "increas": [2, 3, 5, 6, 7, 8, 11, 14, 15, 20, 23], "increasingli": 20, "ind": 8, "inde": [2, 4, 6, 7, 8, 15, 23], "indefinit": 6, "independ": [2, 7, 8, 9, 10, 14, 15, 20, 23], "index": [2, 3, 5, 6, 12, 16, 17, 18, 20, 22, 23], "index_col": [2, 23], "indic": [1, 2, 3, 5, 6, 7, 8, 11, 12, 13, 15, 23], "indispens": 8, "individu": [3, 8, 9, 12, 14, 20, 23], "indu": 2, "indx": 18, "indx1": 4, "indx2": 4, "indx3": 4, "ineffici": [5, 15], "inequ": [10, 15], "inertia": 15, "inf1000": [17, 23], "inf1100": [17, 23], "inf1100l": [17, 23], "inf1110": [17, 23], "inf3000": 23, "infeas": 11, "infer": [2, 3, 6, 8, 22, 23], "inferenc": 3, "infil": [2, 8, 9, 11, 23], "infin": [7, 8, 9, 13], "infinit": 5, "infinitesim": 20, "influenc": [8, 12], "influenti": 3, "info": 23, "inform": [2, 3, 5, 6, 8, 11, 13, 14, 15, 16, 18, 22, 23], "inforom": 0, "infti": [5, 8, 15, 20], "ingeni": 15, "ingredi": [2, 11, 23], "inher": 8, "inherit": [18, 23], "initi": [2, 3, 4, 8, 12, 15, 16, 18, 20, 23], "inject": 16, "inlin": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "inner": [2, 15], "inp": 6, "inplac": 15, "input": [1, 2, 3, 5, 6, 7, 8, 9, 10, 14, 15, 16, 20, 23], "input_dim": 3, "input_shap": [5, 6], "inputs": 3, "inputs_shuffl": [2, 3], "insert": [5, 7, 8, 10, 12, 20], "insid": [2, 6, 9], "insight": [2, 3, 7, 17, 23], "insist": [8, 15], "inspir": [2, 3, 14, 23], "instabl": 4, "instal": [0, 2, 3, 7, 8, 11], "instanc": [1, 2, 3, 4, 6, 8, 11, 13, 15, 23], "instanti": 12, "instead": [2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 15, 16, 18, 20, 23], "institut": 3, "instruct": [2, 3, 23], "int": [2, 3, 4, 5, 6, 7, 8, 13, 15, 16, 18, 20], "int32": 12, "int_": [5, 8, 20], "int_0": 20, "int_a": 20, "intak": 2, "integ": [3, 4, 15, 16, 18, 20, 23], "integer_vector": 3, "integr": [5, 8, 20, 23], "intellig": [2, 16, 22, 23], "intend": 12, "intens": 3, "intention": 16, "interact": [2, 8, 11, 14, 17, 23], "intercept": [1, 2, 8, 10, 13, 15, 23], "intercept_": [2, 8, 10, 11, 15, 23], "interchang": [7, 14, 18], "interconnect": 3, "interest": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 17, 20, 23], "interfac": [2, 3, 18], "interior": [2, 11, 23], "intermedi": 18, "intern": [3, 12, 14], "interpol": [3, 5, 6, 8, 14], "interpr": 7, "interpret": [0, 1, 2, 3, 8, 11, 12, 14, 15, 18, 20], "interv": [2, 5, 7, 8, 9, 15, 20, 23], "intial": 15, "intract": [2, 6], "intrins": [5, 13, 18, 20, 23], "intro": [17, 22, 23], "introduc": [2, 3, 7, 8, 10, 12, 14, 18, 20, 23], "introduct": [3, 4, 6, 15, 22], "introductori": [2, 6, 18, 22, 23], "intuit": [2, 7, 8, 10, 14, 15, 23], "inv": [2, 7, 15, 23], "invalu": [2, 15, 17, 23], "invari": 3, "invd": 7, "inver": 10, "invers": [2, 5, 8, 15, 23], "inverse_transform": 10, "invert": [1, 2, 7, 9, 12, 15, 23], "invh": 15, "invok": [2, 10], "involv": [2, 4, 8, 9, 13, 14], "io": [2, 17, 19, 21, 22, 23], "ip": [2, 10, 20, 23], "ipca": 13, "ipynb": [17, 23], "ipython": [2, 7, 9, 11, 13, 16, 17, 23], "iq": 8, "iri": [10, 11], "irreduc": 8, "irrelev": 7, "irrespect": [2, 23], "isn": 7, "isnul": 2, "isomap": 13, "isotop": 23, "issu": [0, 3, 11, 18], "it_arrai": 15, "item": [2, 15, 23], "items": [18, 23], "iter": [3, 4, 6, 8, 10, 15, 16, 20], "its": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "itself": [7, 8, 14, 20], "j": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 14, 15, 16, 18, 20, 22, 23], "j1": 18, "j_": 8, "j_lasso_sk": 8, "j_ridge_sk": 8, "j_sk": 8, "jackknif": [8, 17, 23], "jacobian": [4, 15], "jason": 6, "jax": [17, 23], "jensen": [21, 23], "jerom": 22, "ji": [14, 18], "jit": 15, "jj": [2, 7, 8, 23], "jk": [2, 3, 8, 14, 18, 23], "jl": [2, 23], "jm": 18, "jnp": 15, "job": [0, 4, 10, 12], "join": [2, 6, 8, 9, 11, 23], "joint": [6, 7], "judg": 15, "judgement": 8, "julia": [17, 18], "jump": 20, "junk": 6, "jupit": 23, "jupyt": [0, 1, 2, 17, 22, 23], "just": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20, 23], "justif": 2, "justifi": [5, 12], "k": [2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23], "k0": 9, "k1": 9, "kaggl": 8, "kappa_d": 20, "karl": [21, 23], "karush": 10, "katrin": [21, 23], "keep": [0, 2, 3, 6, 7, 8, 13, 15, 16, 18, 23], "keepdim": [3, 8, 12, 18], "kei": [2, 3, 5, 8, 14], "kept": [6, 8, 16], "kera": [2, 6, 17, 23], "kernel": [2, 3, 5, 17, 23], "kernel_regular": [3, 5], "kernel_s": 6, "kernelpca": 13, "kev": [2, 23], "kevin": [22, 23], "keyword": [18, 23], "kfold": 8, "kg": 3, "ki": 18, "kick": [3, 15], "kiener": 4, "kilomet": 8, "kind": [2, 4, 5, 6, 10, 14, 15, 16, 23], "kj": [8, 14, 18], "kjm": [17, 23], "kkt": 10, "kl": 20, "km": [14, 23], "kmean": 16, "kmeanspoint": 16, "kn_k": 16, "know": [0, 1, 2, 3, 4, 7, 8, 10, 15, 17, 23], "knowledg": [2, 17, 23], "known": [3, 5, 6, 7, 8, 9, 10, 11, 14, 18, 20, 22], "kondev": [2, 23], "kp": 20, "kpca": 13, "kroneck": 16, "kuhn": 10, "kvalsund": [21, 23], "kwown": [2, 23], "l": [2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 14, 15, 18, 20, 23], "l0": 9, "l1": [2, 3, 5, 9, 23], "l1_l2": [3, 5], "l1regl": 7, "l2": [3, 5], "l_": 18, "l_1": 9, "l_2": [9, 15], "l_j": 14, "la": 15, "la_i": 14, "la_k": 14, "lab": [17, 23], "label": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 20, 23], "labelencod": [9, 12], "labels": [8, 10, 11], "labels_shuffl": [2, 3], "laboratori": 19, "lack": [2, 23], "lagari": 4, "lagrang": [10, 13], "lambda": [2, 3, 4, 5, 7, 8, 9, 10, 12, 14, 15, 20, 23], "lambda_": 13, "lambda_0": 13, "lambda_1": [7, 10, 13], "lambda_2": [10, 13], "lambda_i": [10, 13], "lambda_iy_i": 10, "lambda_jy_iy_j": 10, "lambda_k": 10, "lambda_n": [7, 10], "lamda": 3, "land": [2, 10], "landmark": 10, "landscap": 15, "langl": [2, 8, 13, 20, 23], "languag": [2, 3, 6, 10, 17, 18, 22, 23], "lapack": [18, 23], "laplac": 7, "laptop": [0, 17], "larg": [2, 3, 4, 6, 7, 8, 10, 11, 12, 13, 15, 17, 18, 20, 22, 23], "larger": [2, 5, 7, 8, 10, 12, 13, 15, 20, 23], "largest": [6, 10, 13], "lasso": [2, 9, 17, 23], "lasso_sk": 8, "last": [1, 2, 3, 5, 6, 7, 8, 9, 10, 14, 18, 20, 21, 23], "latent": 6, "latent_dim": 6, "latent_point": 6, "latent_space_value_rang": 6, "later": [0, 2, 3, 6, 9, 10, 14, 15, 16, 17, 23], "latest": [0, 6, 17], "latest_checkpoint": 6, "latex": 23, "latter": [2, 5, 8, 9, 10, 13, 15, 18, 20, 23], "lattic": 14, "law": 2, "layer": [2, 6, 15, 23], "lbfg": [9, 11, 12], "lcc": [7, 8], "lda": 13, "ldot": [2, 8, 13, 23], "le": [7, 9, 12, 15, 20], "lead": [1, 2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "leaf": 11, "leaki": 3, "leakyrelu": 6, "lear": 15, "learn": [5, 6, 7, 8, 9, 10, 11, 12, 14, 18, 21, 22], "learnabl": 5, "learner": 12, "learnig": 23, "learning_r": [10, 12], "learning_rate_init": [2, 3, 23], "learning_schedul": 15, "least": [2, 9, 10, 12, 13, 17, 18, 20], "leat": 15, "leav": [2, 3, 5, 7, 8, 11, 13, 23], "lectur": [2, 3, 7, 12, 13, 14, 15, 17, 18, 19, 21, 22], "lecturenot": [2, 17, 22, 23], "left": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "leftarrow": [10, 14], "legend": [0, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 23], "len": [1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 18, 23], "length": [1, 2, 3, 5, 6, 10, 11, 15, 17, 23], "length_of_sequ": 6, "leq": [2, 7, 9, 10, 15, 16, 20, 23], "less": [2, 3, 5, 6, 7, 8, 10, 11, 15, 17, 20, 23], "lessen": 3, "let": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "letter": [1, 2, 18, 20, 23], "level": [2, 3, 7, 8, 11, 17, 18, 19, 21, 23], "li": [10, 13], "liblinear": 12, "librari": [2, 3, 4, 5, 6, 7, 8, 11, 12, 13, 18, 20, 22], "licens": [2, 3, 17, 23], "lie": [2, 8, 13, 20, 23], "life": [2, 3, 10, 14, 23], "lifetim": 15, "like": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 17, 18, 20, 23], "likelihood": [2, 3, 7, 11, 23], "lim_": 20, "limit": [2, 7, 8, 10, 14, 18, 23], "lin_clf": 10, "lin_model": 2, "lin_reg": 11, "linalg": [2, 4, 7, 8, 10, 13, 15, 18, 20, 23], "line": [0, 1, 2, 5, 8, 10, 13, 15, 23], "line1": 10, "line2": 10, "line3": 10, "line_model": 0, "line_predict": 0, "linear": [1, 3, 5, 7, 8, 9, 11, 12, 13, 14, 17, 20], "linear_model": [0, 1, 2, 7, 8, 9, 10, 11, 12, 13, 15, 23], "linear_regress": 8, "linearli": 7, "linearloc": [8, 15], "linearregress": [0, 1, 2, 8, 9, 11, 23], "linearsvc": 10, "liner": [3, 5], "linerar": 12, "linewidth": [2, 4, 6, 8, 10, 11, 12], "link": [0, 2, 6, 11, 14, 17, 19, 21, 23], "linlag": 7, "linpack": [18, 23], "linreg": [2, 23], "linspac": [1, 2, 4, 5, 6, 8, 10, 11, 12, 15, 18, 20, 23], "linu": 6, "linux": [2, 3, 17, 23], "liquid": [2, 23], "list": [0, 2, 3, 4, 5, 6, 11, 17, 23], "listedcolormap": [11, 12], "literatur": [3, 9, 16, 22], "littl": [3, 5, 11, 14], "live": 10, "ll": [2, 20, 23], "lle": 2, "lloyd": [6, 16], "lmb": [2, 4, 7, 8], "lmbd": [2, 3, 5, 23], "lmbd_val": [2, 3, 5, 23], "lmbda": 15, "ln": [3, 15], "load": [2, 3, 6, 8, 9, 11, 12], "load_boston": 2, "load_breast_canc": [3, 9, 11, 12, 13], "load_data": [5, 6], "load_digit": [3, 5], "load_iri": [10, 11], "loc": [2, 5, 8, 9, 10, 11, 12, 23], "local": [0, 2, 3, 5, 9, 14, 15], "locat": [0, 4, 5, 10], "log": [0, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 15, 18, 23], "log10": [2, 7, 8], "log_": [2, 23], "log_clf": 12, "logarithm": [2, 7, 9, 18, 23], "logic": [2, 3, 11, 23], "login": 0, "logist": [2, 3, 4, 10, 11, 12, 13, 14, 15, 17], "logisticregress": [9, 11, 12, 13], "logit": 9, "logreg": [9, 11, 12, 13], "logspac": [2, 3, 5, 7, 8, 23], "long": [2, 3, 5, 6, 14, 15, 23], "longer": [4, 5, 10, 12, 16, 18, 20, 23], "loocv": 8, "look": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 18, 20, 23], "loop": [1, 3, 6, 8, 12, 14, 16, 17, 18, 23], "lose": 3, "loss": [2, 3, 5, 6, 7, 8, 9, 10, 12, 13, 15, 18, 23], "loss_fil": 6, "lossfil": 6, "lost": 6, "lot": [1, 2, 3, 6, 8], "low": [2, 8, 11, 12, 13, 23], "lower": [1, 2, 3, 5, 8, 11, 12, 18], "lowercas": [18, 23], "lowest": [11, 15, 20], "lr": [3, 5, 6, 12], "lstat": 2, "lstm": 6, "lstm_2layer": 6, "lstsq": [2, 23], "lt": 8, "lu": [2, 7, 23], "lubksb": 18, "luckili": 4, "ludcmp": 18, "lux": 18, "lvert": 3, "lw": [2, 23], "m": [0, 2, 3, 4, 5, 7, 8, 10, 11, 12, 13, 14, 15, 18, 20, 21, 22, 23], "m_": [11, 14], "m_1": 16, "m_h": [2, 23], "m_k": 16, "m_l": 14, "m_n": [2, 23], "m_p": [2, 23], "m_t": 15, "ma": 13, "machin": [0, 1, 3, 5, 6, 7, 8, 9, 11, 12, 13, 14, 18, 22], "machinelearn": [1, 2, 8, 17, 19, 21, 22, 23], "mackai": 22, "made": [2, 3, 5, 6, 7, 8, 9, 11, 13, 14, 23], "mae": [2, 23], "magic": 6, "magnitud": [3, 8, 9, 15], "mai": [2, 3, 4, 5, 7, 8, 9, 10, 11, 13, 14, 15, 17, 18, 20, 23], "mail": [19, 21], "main": [2, 3, 5, 6, 7, 8, 9, 11, 18, 22], "mainli": [2, 7, 8, 9, 11, 23], "maintain": 8, "major": [3, 8, 11, 12, 15, 18, 23], "make": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 13, 14, 15, 17, 18, 20, 22], "make_axes_locat": 8, "make_moon": [10, 11, 12], "make_pipelin": [2, 8, 12], "makedir": [2, 8, 9, 11, 23], "makeplot": [2, 23], "malcondit": 18, "malign": [3, 9, 11], "mammographi": 7, "manag": [0, 2, 4, 5, 17, 23], "mandatori": [21, 23], "mani": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 13, 15, 16, 17, 18, 20, 22, 23], "manifold": 13, "manner": 5, "manual": 8, "map": [2, 3, 4, 8, 9, 10, 13, 14, 16, 20, 23], "margin": [2, 7, 10], "marit": [2, 23], "mark": 23, "marker": [2, 9, 18, 23], "markov": [17, 23], "marsaglia": 20, "mass": [2, 3, 7, 15], "massag": [2, 23], "masses2016": [2, 23], "masses2016ol": [2, 23], "masses2016tre": 2, "masseval2016": [2, 23], "master": [19, 21], "mat": [17, 23], "mat1100": [17, 23], "mat1110": [17, 23], "mat1120": [17, 23], "match": [0, 3, 6, 7, 15, 16], "materi": [0, 6, 7, 9, 15, 18, 19, 21], "math": [5, 9, 14, 15, 18, 20, 22, 23], "mathbb": [2, 6, 7, 8, 9, 10, 13, 14, 15, 16, 18, 20, 23], "mathbf": [2, 7, 8, 9, 10, 15, 18, 23], "mathcal": [3, 7, 8, 9, 15], "matheemat": 5, "mathemat": [2, 8, 13, 14, 15, 17, 18, 20, 22, 23], "mathemati": 23, "mathrm": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 23], "matmul": [3, 4, 7], "matnat": 22, "matplotlib": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "matric": [1, 2, 3, 5, 6, 8, 9, 10, 13, 15, 17], "matrix": [2, 4, 5, 6, 8, 9, 10, 12, 15, 20], "matshow": 3, "matter": [4, 5, 15], "max": [2, 3, 4, 5, 6, 11, 12, 14, 15, 21, 23], "max_depth": [2, 11, 12], "max_diff": 4, "max_diff1": 4, "max_diff2": 4, "max_it": [2, 3, 10, 15, 23], "max_iter": 16, "max_leaf_nod": 12, "max_sampl": 12, "maxdegre": [2, 8, 12], "maxdepth": 12, "maxim": [3, 6, 7, 9, 10, 13], "maximum": [2, 4, 5, 7, 9, 10, 11, 12, 15, 16, 23], "maxpolydegre": [7, 8], "maxpooling2d": 5, "mbox": [7, 8], "mcculloch": 14, "md": 13, "mdoel": 6, "mean": [0, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "mean_absolute_error": [2, 23], "mean_divisor": 16, "mean_i": 20, "mean_matrix": 16, "mean_squared_error": [0, 2, 6, 8, 9, 12, 23], "mean_squared_log_error": [2, 23], "mean_vector": 16, "mean_x": 20, "meaning": [2, 6, 9, 23], "meansquarederror": [2, 23], "meant": [5, 9, 12, 15], "measur": [1, 2, 3, 4, 7, 8, 11, 13, 14, 16, 20, 23], "mechan": [2, 6, 20, 23], "median": [2, 23], "medicin": 14, "medium": [6, 10, 15], "medv": 2, "meet": [2, 21], "mehta": [2, 23], "memori": [5, 6, 13, 14, 15, 18], "mention": [2, 14, 15, 20, 23], "mere": [2, 23], "meshgrid": [4, 7, 8, 10, 11, 12, 13], "mess": 0, "messag": [7, 15], "messi": 4, "met": [2, 5, 10], "meteorolog": 11, "meter": 8, "method": [0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 13, 14, 16, 17, 18, 20, 22], "metion": 8, "metric": [0, 2, 3, 5, 8, 9, 11, 12, 16, 23], "metropoli": [17, 23], "mev": [2, 20, 23], "mgd": 15, "mglearn": [17, 23], "mgrid": 15, "mi": 12, "mia": [21, 23], "michigan": 23, "microsoft": 22, "mid": 3, "midel": 6, "midpoint": 11, "might": [0, 2, 3, 4, 6, 8, 11, 15], "mild": 11, "millimet": 8, "million": [2, 23], "mimic": 14, "min": [2, 4, 7, 10, 11], "min_": [2, 4, 7, 16, 23], "min_samples_leaf": 11, "mind": [0, 2, 8, 15, 23], "mindboard": 6, "mine": [17, 23], "mini": [3, 13, 14, 15], "minibatch": [3, 13, 15], "minibathc": 15, "minim": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], "minima": [2, 3, 9, 15, 23], "minimum": [2, 3, 4, 8, 10, 11, 13, 15], "minmaxscal": 2, "minor": 20, "minst": 3, "minu": 9, "mirjalili": 23, "mirror": 11, "misc": 8, "misclassif": [10, 11, 12], "misclassifi": [10, 12], "miser": 2, "mismatch": 3, "miss": [2, 9, 12], "mistak": 6, "mit": 22, "mix": [3, 4, 23], "mixtur": 15, "mk": [11, 18], "mkdir": [2, 8, 9, 11, 23], "ml": [2, 3, 12, 15, 18], "mlab": 20, "mlbook": 23, "mle": [7, 9], "mlp": 3, "mlpclassifi": 3, "mlpregressor": [2, 23], "mm": 18, "mn": [14, 20], "mnist": [3, 13], "mod": 20, "mode": [19, 21, 23], "model": [1, 4, 5, 7, 9, 10, 11, 12, 13, 15, 16, 17, 20, 22], "model_select": [0, 1, 2, 3, 5, 7, 8, 9, 11, 12, 13, 23], "moder": 12, "modern": [2, 8, 9, 17, 23], "modif": [4, 14, 15], "modifi": [2, 3, 5, 7, 9, 10, 12, 14, 15, 23], "modul": [1, 2, 18, 23], "modular": 20, "modulo": 20, "moe": 13, "moment": [7, 8, 15, 20], "mondai": [21, 23], "monitor": 15, "monoton": [7, 14, 20], "mont": [2, 8, 17, 20, 22, 23], "montli": 1, "moor": [7, 8], "more": [1, 2, 3, 4, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20], "moreov": [2, 5], "morten": [21, 23], "mortenhj": 23, "most": [0, 1, 2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20, 23], "mostli": [3, 13], "motion": [2, 15], "motiv": [3, 6], "move": [0, 1, 2, 6, 7, 8, 9, 11, 14, 15, 16, 20], "mpl": [2, 9, 23], "mpl_toolkit": [4, 8, 15], "mplot3d": [4, 8, 15], "mplregressor": 3, "mse": [0, 1, 2, 6, 7, 8, 11, 12, 23], "mse_simpletre": 12, "mselassopredict": 7, "mselassotrain": 7, "mseownridgepredict": 8, "msepredict": 7, "mseridgepredict": [2, 7, 8], "msetrain": 7, "msle": [2, 23], "mt": [9, 14], "mu": [2, 8, 13, 15, 20, 23], "mu0": 20, "mu1": 20, "mu2": 20, "mu_": [8, 20], "mu_i": 8, "mu_n": 13, "mu_x": 20, "much": [0, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 18, 20, 23], "multi": [2, 3, 5, 9, 17, 23], "multiclass": [3, 9], "multidimension": [13, 14, 23], "multilay": 3, "multinomi": 9, "multipl": [0, 4, 6, 7, 8, 9, 14, 15, 20], "multipli": [5, 7, 8, 13, 15, 18, 20], "multiplum": 10, "multivari": [2, 4, 12, 13, 17, 20, 23], "multivariate_norm": [13, 16], "murphi": [13, 22, 23], "must": [0, 3, 4, 7, 8, 10, 12, 14, 15, 16, 20], "mutat": 9, "mutual": [3, 5, 8, 15], "mx_": 20, "my": 23, "myriad": [2, 17, 23], "mz1": 20, "mz2": 20, "m\u00f8svatn": 8, "n": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 18, 20, 23], "n1": 18, "n2": 18, "n_": [3, 4, 5, 10, 14, 20], "n_0": [14, 20], "n_boostrap": [8, 12], "n_bootstrap": 8, "n_categori": [3, 5], "n_cluster": 16, "n_compon": 13, "n_epoch": 15, "n_estim": 12, "n_examples_to_gener": 6, "n_featur": 3, "n_filter": 5, "n_hidden": 4, "n_hidden_neuron": [2, 3, 23], "n_i": 20, "n_input": [2, 3, 5], "n_instanc": 11, "n_job": 12, "n_k": 16, "n_l": [14, 20], "n_layer": 3, "n_m": 11, "n_neuron": 3, "n_neurons_connect": 5, "n_neurons_layer1": 3, "n_neurons_layer2": 3, "n_point": 16, "n_sampl": [8, 10, 11, 12, 16], "n_split": 8, "n_step": 6, "n_t": 4, "n_x": 4, "nabla": [3, 15], "nabla_": [4, 15], "nabla_w": 15, "nag": 15, "naimi": [2, 23], "naiv": 9, "naive_kmean": 16, "name": [0, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 20, 21, 23], "narrow": 15, "nation": [3, 7], "nativ": [17, 23], "natur": [2, 3, 6, 10, 11, 14, 15, 20, 22, 23], "navier": 14, "navig": 0, "nb": 20, "nb_": 18, "nbconvert": 23, "nd": 16, "ndarrai": 8, "ne": [11, 12, 18, 20], "nearest": [3, 5, 8, 13], "nearli": 15, "neat": 23, "neccesari": 8, "necess": 4, "necessari": [2, 3, 5, 6, 10, 16, 23], "necessarili": [2, 6, 13, 20, 23], "necesserali": 7, "neck": 9, "need": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20], "neg": [2, 3, 5, 7, 8, 9, 12, 15, 18, 20, 23], "neg_mean_squared_error": 8, "neglect": 20, "neglig": 20, "neighbor": [5, 8, 13], "neither": [6, 15], "neq": [15, 16, 20], "nervou": 14, "nest": [11, 14], "nesterov": 15, "net": [4, 6, 14], "netlib": [18, 23], "network": [2, 11, 15, 17, 22], "neural": [2, 15, 17, 22], "neural_network": [2, 3, 4, 23], "neuralnetwork": 3, "neuron": [3, 4, 5, 6, 14], "neutral": [2, 23], "neutron": [2, 23], "never": [3, 6, 8, 11, 20], "new": [0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 23], "new_chang": 15, "new_hobbit": 23, "newaxi": [2, 5, 8, 11], "newli": [2, 23], "newton": [3, 9, 10, 15, 20], "next": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 15, 16, 23], "next_guess": 15, "next_input": 6, "ng": 3, "ni": 16, "nice": [2, 3, 7, 13, 23], "niter": 15, "nitric": 2, "nlambda": [2, 7, 8], "nlp": 22, "nm": 20, "nm_n": [2, 23], "nmse": 8, "nn": [4, 7, 8, 14, 18], "nn_model": 3, "nnmin": 4, "node": [3, 5, 11, 12, 14], "nois": [2, 6, 7, 8, 10, 11, 12, 15, 23], "noise_dimens": 6, "noisi": [3, 8], "non": [2, 3, 5, 7, 8, 9, 11, 12, 13, 14, 15, 16, 18, 20, 23], "none": [2, 3, 4, 6, 7, 11, 12, 15, 20, 23], "nonlinear": [5, 8, 10, 11, 13, 14], "nonneg": [8, 11, 15], "nonparametr": 8, "nonsens": 20, "nonsingular": 18, "nonumb": [5, 9, 10, 15, 18], "nor": [3, 6, 15], "norm": [2, 3, 7, 8, 10, 13, 15, 23], "normal": [1, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 20, 23], "normali": [18, 23], "norwai": [8, 23], "notat": [2, 4, 7, 8, 15, 16, 20, 23], "note": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 13, 14, 15, 16, 17, 18, 20, 22, 23], "notebook": [0, 1, 2, 3, 5, 11, 17, 23], "noth": [3, 4, 7, 10, 14, 16, 20], "notic": [6, 7, 14, 15, 18, 20, 23], "notion": 5, "novel": [5, 8, 12], "novemb": [3, 21, 23], "now": [0, 1, 2, 4, 6, 7, 8, 9, 10, 12, 13, 14, 16, 17, 18, 20, 23], "nowadai": [2, 3, 5, 11, 17, 23], "nox": 2, "np": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 23], "npr": 4, "nsampl": 8, "nt": 4, "nu": 20, "nuclear": 7, "nuclei": [2, 20, 23], "nucleon": [2, 23], "nucleu": [2, 23], "num": 6, "num_coordin": 4, "num_hidden_neuron": 4, "num_it": 4, "num_neuron": 4, "num_neurons_hidden": 4, "num_point": 4, "num_tre": 12, "num_valu": 4, "number": [1, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 21, 23], "numberid": 9, "numberparamet": 5, "numer": [2, 7, 8, 11, 12, 13, 14, 15, 17, 18, 22, 23], "numpi": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20], "nunmpi": 7, "nx": 4, "ny": 20, "o": [2, 3, 6, 7, 8, 9, 10, 11, 13, 18, 21, 22, 23], "obei": [8, 13, 15], "object": [0, 2, 3, 6, 10, 12, 18, 23], "obliqu": 7, "observ": [2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20], "obtain": [2, 3, 7, 8, 9, 10, 11, 12, 14, 15, 16, 18, 20, 23], "obviou": [7, 8, 13, 20], "obviouli": 23, "obvious": [2, 6, 7, 8, 18, 23], "occupi": 2, "occur": [2, 8, 10, 11, 18, 20, 23], "octob": [21, 23], "od": 2, "odd": [2, 5, 9, 23], "odenum": 4, "odesi": 4, "oen": 2, "off": [3, 5, 6, 7, 11, 15, 20], "offer": [8, 13, 17, 18, 19, 21, 23], "offic": [21, 23], "offici": [19, 23], "often": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "ofter": [18, 23], "ol": [2, 15, 23], "old": [0, 3, 7, 12, 15], "ols_paramet": 1, "ols_sk": 8, "ols_svd": 8, "olsbeta": [2, 7], "omega": [4, 5, 8], "omega_0": 5, "omit": [2, 7, 23], "onc": [3, 8, 11, 13, 15], "one": [0, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, 20, 21, 23], "onehot": 3, "onehot_vector": 3, "onehotencod": 11, "ones": [1, 2, 4, 7, 8, 10, 11, 12, 13, 15, 18, 23], "ones_lik": 6, "onl": 5, "onli": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "onlin": [0, 13, 19], "onto": [7, 13], "open": [0, 2, 3, 6, 8, 9, 11, 17, 19, 21, 23], "oper": [0, 1, 2, 3, 5, 7, 8, 12, 13, 14, 15, 17, 20, 23], "operation": 20, "oplu": 20, "opmiz": 15, "opportun": 2, "oppos": [8, 15], "opposit": [3, 7, 10], "opt": [3, 7, 23], "optim": [1, 2, 4, 5, 6, 7, 8, 9, 11, 12, 13, 16], "optimis": [3, 5], "option": [0, 2, 3, 5, 7, 8, 10, 13, 18], "optmiz": [3, 10, 15], "oral": 23, "orang": 2, "order": [0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 18, 20, 23], "ordinari": [2, 4, 5, 9, 13, 15, 17], "oreilli": [22, 23], "org": [1, 2, 5, 6, 17, 18, 22, 23], "organ": [8, 9, 12, 18], "orient": [3, 7, 20], "origin": [0, 2, 5, 7, 8, 10, 13, 14, 15, 18, 23], "orthogn": 7, "orthogon": [2, 7, 8, 10, 13, 15, 18, 23], "orthonorm": 7, "os": [21, 23], "oscar": 3, "oscil": [5, 15], "oslo": [2, 17, 19, 21, 23], "osx": [2, 17, 23], "other": [2, 3, 4, 5, 7, 8, 9, 10, 12, 15, 16, 17, 19, 20, 21, 22], "otherwis": [2, 3, 6, 9, 15, 18, 23], "ouput": [7, 9, 14], "our": [0, 1, 3, 4, 5, 8, 9, 10, 11, 12, 14, 16, 17, 18, 20], "ourmodel": 2, "ourselv": [2, 7, 8, 10, 13, 15, 23], "out": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 20, 23], "out_fil": 11, "outcom": [2, 9, 11, 12, 14, 20], "outdoor": 11, "outer": [8, 14, 15], "outfil": 6, "outlier": [2, 10, 23], "outlin": [8, 12, 13], "outlook": 11, "outperform": 12, "output": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 18, 20, 23], "output_bia": 3, "output_bias_gradi": 3, "output_shap": 6, "output_weight": 3, "output_weights_gradi": 3, "outputlayer1": 14, "outputlayer2": 14, "outsid": 6, "over": [0, 1, 2, 3, 5, 6, 7, 8, 11, 12, 14, 15, 18, 23], "over1": 15, "overal": [3, 12], "overcast": 11, "overcom": [14, 15], "overdetermin": [2, 23], "overfit": [2, 3, 5, 8, 11, 12, 15], "overflow": 7, "overhead": 14, "overlap": [5, 9, 10, 11], "overlin": [2, 7, 8, 11, 12, 13, 16, 18, 23], "overst": 2, "overtrain": 6, "overview": 5, "own": [1, 6, 7, 8, 10, 14, 15, 17, 18], "owner": 2, "ownmsepredict": 2, "ownmsetrain": 2, "ownridgebeta": [2, 8], "ownypredictridg": 2, "ownytilderidg": 2, "oxid": 2, "p": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 23], "p0": 4, "p1": 4, "p_": [4, 6, 10, 11], "p_hidden": 4, "p_i": [7, 20], "p_j": 20, "p_n": 20, "p_output": 4, "p_x": 20, "pack": [2, 23], "packag": [0, 2, 3, 5, 6, 7, 10, 13, 15, 17, 20], "packtpub": 23, "packtpublish": 23, "pad": [5, 6], "page": [2, 17, 23], "pai": [0, 2, 3, 11, 15], "pair": [2, 4, 5, 11, 17, 20, 23], "panda": [2, 6, 7, 8, 9, 11, 13, 17], "panel": 23, "paper": 3, "paradigm": [2, 23], "parallel": [12, 15, 17, 18, 23], "param": 4, "paramat": 4, "paramet": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 20], "parameter": [2, 8, 12, 23], "parametr": [2, 8, 23], "paramt": [5, 7], "part": [2, 3, 5, 7, 8, 12, 18, 19, 20, 21, 23], "partial": [1, 2, 3, 7, 8, 9, 10, 12, 13, 14, 15, 20, 23], "particip": [17, 19, 21, 23], "particl": [2, 6, 15, 20, 23], "particular": [1, 2, 3, 4, 5, 7, 8, 11, 12, 13, 14, 15, 20, 22, 23], "particularli": [7, 8, 10, 13, 15, 20], "partit": [3, 6, 11], "partli": [8, 23], "partner": 0, "pass": [4, 5, 14, 16], "past": [12, 20], "patch": [8, 20], "path": [2, 6, 8, 9, 11, 17, 23], "patient": 9, "patter": 6, "pattern": [2, 5, 6, 14, 22, 23], "pauli": [2, 23], "pc": [13, 17], "pca": [2, 9, 17, 23], "pd": [2, 6, 7, 8, 9, 11, 13, 23], "pde": 4, "pdf": [0, 1, 2, 5, 6, 7, 8, 11, 22, 23], "pedagog": [2, 23], "penal": 8, "penalti": [8, 15], "penros": [7, 8], "pentagon": 15, "peopl": [2, 3, 11, 15, 17], "per": [2, 3, 8, 19, 21, 23], "percentag": [2, 12, 13, 21], "perceptron": [2, 3, 9, 23], "peregrin": 23, "perfect": [2, 3, 15, 23], "perfectli": [6, 8], "perform": [1, 2, 4, 5, 6, 7, 8, 10, 12, 13, 14, 15, 16, 17, 18, 20, 23], "performac": 6, "perhap": [2, 7, 15, 23], "perimet": 3, "period": [3, 6, 20], "permiss": 0, "permut": 13, "persist": 15, "person": [1, 7, 8, 9, 19, 21, 23], "perspect": 22, "pertin": [14, 23], "petal": [10, 11], "peter": 22, "phantom": 20, "phase": [8, 14], "phenomena": 20, "phi": 10, "phi_k": 10, "philosophi": 15, "phone": [21, 23], "photo": 6, "phrase": [2, 23], "physic": [2, 3, 6, 9, 14, 15, 20, 21, 22, 23], "pi": [4, 5, 7, 8, 9, 11, 14, 15, 20], "pick": [3, 11, 12, 13, 15, 16], "pickl": 3, "pictur": [2, 23], "pie": [17, 23], "piec": [13, 16], "pillow": [2, 17, 23], "pinv": [7, 8, 15], "pip": [0, 2, 3, 17, 23], "pip3": [2, 3, 23], "pipelin": [2, 8, 10, 12], "pippin": 23, "pit": 6, "pitfal": 8, "pitt": 14, "pixel": [3, 5, 6], "pixel_height": [3, 5], "pixel_width": [3, 5], "place": [0, 2, 6, 8, 10, 15, 18, 23], "plai": [2, 5, 6, 7, 8, 10, 13, 17, 23], "plain": [10, 12, 14, 15, 16], "plan": [8, 11, 21, 22, 23], "plane": [10, 11], "plateau": 7, "platform": [17, 23], "plausibl": 14, "pleas": [15, 21, 23], "plenti": 3, "plethora": [5, 14], "plot": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "plot_confusion_matrix": [9, 12], "plot_count": 8, "plot_cumulative_gain": [9, 12], "plot_data": 3, "plot_dataset": 10, "plot_decision_boundari": [11, 12], "plot_import": 12, "plot_max": 6, "plot_min": 6, "plot_model": 6, "plot_numb": 6, "plot_predict": 10, "plot_regression_predict": 11, "plot_result": 6, "plot_roc": [9, 12], "plot_surfac": [4, 8, 15], "plot_train": 11, "plot_tre": [11, 12], "plt": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "plu": [2, 5, 7, 9, 23], "pm": 10, "pmatrix": 4, "pml": 22, "pn": 5, "png": [2, 6, 8, 9, 11, 23], "point": [2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 21, 23], "point_1": 6, "point_2": 6, "poisson": [17, 20, 23], "poli": [8, 10], "poly100_kernel_svm_clf": 10, "poly3": 2, "poly3_plot": 2, "poly_featur": [0, 10, 11], "poly_features10": 11, "poly_fit": 11, "poly_fit10": 11, "poly_kernel_svm_clf": 10, "poly_model": 0, "poly_predict": 0, "polydegre": [2, 7, 8, 12], "polygon": 15, "polym": 14, "polynomi": [0, 2, 7, 8, 9, 10, 11, 12, 13, 23], "polynomial_featur": [0, 1, 8], "polynomial_svm_clf": 10, "polynomialfeatur": [0, 1, 2, 8, 10, 11], "polytrop": [2, 8, 23], "pool": 5, "pool_siz": 5, "poor": [3, 15], "poorli": 2, "popul": [2, 7, 23], "popular": [2, 3, 5, 8, 9, 10, 11, 13, 14, 17, 18, 20], "popularli": [2, 23], "portabl": 12, "portion": [13, 15], "pose": [2, 6, 7, 8, 13, 20, 23], "posit": [2, 3, 4, 5, 7, 9, 10, 12, 13, 15, 16, 18, 20, 23], "possibl": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 20, 21, 23], "possibli": [8, 10, 15], "posterior": 7, "postpon": [2, 23], "postul": 7, "potenti": [2, 5, 7, 8, 14, 15, 23], "pott": 14, "power": [2, 3, 7, 8, 10, 11, 14, 15, 23], "pp": [7, 8], "practic": [2, 7, 8, 9, 10, 20], "practition": [2, 3, 5, 23], "pre": 23, "preced": [3, 13, 14, 20], "preceed": 6, "preceq": 10, "precis": [2, 4, 7, 13, 15, 18, 20, 23], "pred": 8, "predicit": 2, "predict": [0, 1, 2, 3, 7, 8, 9, 10, 11, 12, 17, 22, 23], "predict_prob": 3, "predict_proba": [9, 12], "predictor": [2, 7, 8, 9, 11, 12, 13, 23], "prefer": [2, 3, 8, 10, 11, 13, 15, 17, 23], "prepar": [2, 8, 18, 23], "preprocess": [0, 1, 2, 6, 8, 9, 10, 11, 12, 13], "prerequisit": 2, "presenc": 15, "present": [2, 7, 8, 9, 11, 14, 15, 18, 20, 23], "preserv": [5, 13, 18], "press": [0, 15, 22], "pretrain": [3, 6], "pretti": [2, 6, 10, 11, 17, 23], "prev_centroid": 16, "prevent": [15, 20], "previou": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 13, 14, 15, 18, 20], "previous": [4, 5, 11, 12, 20], "price": [2, 6, 11, 15], "primal": 10, "primari": [2, 9, 23], "prime": 20, "princip": [2, 7, 9, 17, 23], "principl": [2, 8, 9, 10, 16, 23], "print": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 23], "print_funct": [10, 11], "printout": [2, 23], "prior": [2, 7, 8, 23], "privat": 2, "prob": [3, 20], "probabilist": [2, 22, 23], "probabl": [2, 3, 5, 6, 8, 9, 12, 15, 17, 23], "problem": [2, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 17, 18, 20], "probml": 22, "proce": [2, 7, 8, 9, 10, 11, 12, 13, 15, 18, 23], "procedur": [4, 6, 7, 8, 10, 12, 13, 15], "proceed": 18, "process": [2, 4, 6, 8, 11, 12, 14, 15, 17, 18, 20, 22, 23], "prod": 22, "prod_": [3, 7, 9], "produc": [2, 5, 6, 7, 8, 11, 12, 13, 14, 15, 17, 18, 20, 23], "product": [1, 2, 3, 5, 7, 8, 9, 10, 14, 15, 17, 18, 23], "profess": [2, 23], "program": [0, 2, 3, 6, 7, 8, 10, 14, 16, 17, 18, 19, 20, 21, 23], "programm": 18, "progress": [3, 6, 16], "prohibit": 8, "project": [0, 2, 3, 4, 5, 7, 13, 15, 17, 19], "project_root_dir": [2, 8, 9, 11, 23], "promin": 14, "promis": 10, "promot": [21, 23], "prone": [0, 11], "pronounc": [15, 17, 23], "proof": [2, 13, 14, 15, 23], "propag": [4, 5, 15], "proper": [2, 4, 8, 9], "properli": [3, 8, 10, 12, 15], "properti": [1, 2, 3, 5, 14, 15, 18, 23], "proport": [2, 3, 7, 11, 13, 15, 20, 23], "propos": [3, 6, 8, 12, 23], "propto": [7, 15], "proton": [2, 23], "prove": [5, 15], "provid": [2, 3, 5, 6, 7, 8, 10, 11, 12, 14, 15, 17, 18, 20, 23], "proxi": [3, 15], "prune": 11, "pseudo": [18, 20], "pseudoinv": 7, "pseudoinvers": [7, 8], "pseudorandom": [8, 20], "psychologi": [2, 23], "pt": 15, "public": [0, 2, 17, 23], "pull": 0, "punish": [2, 3, 23], "pure": [5, 11, 20], "purest": 11, "puriti": 11, "purpos": [2, 5, 12, 14, 16, 23], "push": 0, "put": [1, 3], "py": 7, "pycod": 23, "pydata": 17, "pydot": 11, "pyhton2": 23, "pylab": [2, 9, 23], "pypi": 17, "pyplot": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "pythagora": 7, "python": [3, 4, 5, 7, 8, 10, 13, 14, 15, 16, 20], "python2": [2, 23], "python3": [2, 17, 23], "pytorch": [2, 17, 23], "q": [7, 8, 10, 13, 20], "qp": 10, "qquad": [4, 13, 15, 18], "qr": [7, 8, 18], "quad": [3, 15, 18], "quadrat": [2, 10, 11, 15, 23], "qualit": [6, 11, 20], "qualiti": [2, 11, 17, 23], "quantifi": 3, "quantil": 12, "quantit": [2, 8, 11, 23], "quantiti": [1, 2, 4, 7, 8, 9, 11, 12, 13, 14, 16, 18, 20, 23], "quantum": [6, 14, 22, 23], "quartil": 2, "quench": 7, "queri": 11, "question": [2, 7, 8, 11, 13, 14, 15, 21, 23], "qugan": 6, "quick": [6, 20], "quickli": [3, 5, 11, 13, 15], "quit": [0, 3, 7, 8, 11, 12, 14], "quot": 6, "r": [0, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20], "r2": [2, 7, 8, 23], "r2_score": [2, 23], "r2score": [2, 23], "r_1": 11, "r_2": 11, "r_j": 11, "r_m": 11, "rad": 2, "radial": [2, 10, 14], "radioact": 20, "radiu": [2, 3], "rain": 11, "ramp": 3, "ran0": 20, "ran1": 20, "ran2": 20, "ran3": 20, "rand": [0, 2, 6, 7, 8, 11, 12, 15, 18, 23], "randint": [8, 11, 15], "randn": [0, 2, 3, 4, 7, 8, 11, 13, 15, 23], "random": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 15, 16, 17, 18, 23], "random_forest_model": 12, "random_index": 15, "random_indic": [3, 5], "random_st": [2, 9, 10, 11, 12, 13], "randomforestclassifi": 12, "randomli": [3, 8, 11, 15, 16], "rang": [2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 18, 20, 23], "rangl": [2, 8, 13, 20, 23], "rangle_x": 20, "rank": 7, "rankdir": 6, "raphson": [3, 10, 15], "rapidli": 2, "rare": [3, 15, 23], "raschka": 23, "rasckha": 23, "rate": [2, 3, 4, 5, 6, 10, 11, 12, 14, 15], "rather": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "ratio": [6, 9, 11, 12, 13], "rational": [2, 23], "ravel": [7, 8, 9, 10, 11, 12, 13, 15, 18], "raw": 5, "rbf": [10, 13, 14], "rbf_kernel_svm_clf": 10, "rbf_pca": 13, "rc": [2, 20], "rcond": [2, 23], "rcparam": [2, 3, 5, 9, 10, 11, 12, 20, 23], "re": [0, 4, 6, 15], "reach": [3, 6, 7, 8, 11, 12, 14, 15, 16], "read": [1, 2, 4, 5, 6, 7, 8, 9, 10, 13, 14, 18, 20, 22], "read_csv": [2, 8, 9, 11, 23], "read_fwf": [2, 23], "reader": [2, 8, 18, 20, 23], "readi": [2, 3, 7, 8, 10, 12, 13, 14, 18, 23], "readili": 3, "readm": 0, "readthedoc": 17, "real": [1, 2, 3, 6, 9, 12, 13, 14, 18], "real_loss": 6, "real_output": 6, "realist": 10, "realiti": 20, "realiz": [3, 14], "realli": [2, 3, 23], "rearrang": 15, "reason": [2, 3, 5, 6, 12, 15, 22, 23], "reassign": 3, "recal": [7, 8, 11, 12, 13, 14, 18, 20, 23], "recast": 5, "receiv": [3, 5, 12, 14, 20], "recent": [2, 8, 15, 22], "recept": [5, 14], "receptive_field": 5, "recip": [2, 8, 9, 18, 23], "reciproc": 7, "recogn": [2, 6, 7, 12, 23], "recognit": [2, 3, 5, 14, 22, 23], "recommen": 23, "recommend": [0, 2, 4, 5, 6, 7, 8, 10, 15, 17, 18, 22], "reconsid": 11, "reconstruct": 13, "record": [12, 19, 21, 23], "recreat": 0, "rectangl": [11, 15], "rectangular": 7, "rectifi": [3, 5, 14], "recur": [2, 17, 23], "recurr": [2, 3, 17, 23], "recurs": [11, 17, 18, 23], "red": [2, 5, 6, 8, 10, 11], "redefin": [2, 12, 23], "reduc": [3, 5, 7, 8, 11, 12, 13, 15, 23], "reduct": [2, 12, 13, 17, 20, 23], "refer": [2, 3, 4, 5, 7, 8, 13, 14, 15, 16, 18, 22, 23], "referenc": 4, "refin": 14, "refit": 8, "reflect": [2, 3, 6, 7, 20, 23], "refresh": [17, 23], "refreshprogrammingskil": 23, "reg": [12, 13], "regard": [3, 11, 15], "regardless": 14, "region": [5, 6, 8, 11, 14], "regist": [8, 20], "reglasso": 7, "regr_1": [2, 11], "regr_2": [2, 11], "regr_3": [2, 11], "regress": [1, 3, 10, 13, 14, 17, 18], "regressor": [2, 9, 12, 23], "regridg": [2, 7, 8], "regular": [2, 5, 6, 7, 8, 9, 11, 15, 21, 23], "regularli": 0, "reilli": [2, 22, 23], "reinforc": [2, 10, 17, 23], "reiter": 3, "reject": 9, "rel": [2, 6, 8, 9, 11, 14, 15, 20, 23], "relat": [2, 3, 5, 6, 7, 13, 15, 16, 18, 20, 23], "relationship": [2, 6, 11, 23], "relativeerror": [2, 23], "releas": [3, 17, 23], "relev": [2, 3, 7, 9, 13, 17, 20, 23], "reli": [2, 8, 10], "reliabl": [9, 20], "relu": [5, 6, 23], "remain": [3, 4, 6, 8, 14, 18, 20], "remaind": 20, "reman": 4, "remark": 3, "rememb": [2, 10, 15, 18, 23], "remind": [2, 7, 13, 15, 18, 20], "remot": 0, "remov": [2, 6, 7, 8], "renam": 0, "render": [2, 23], "reorder": [7, 9], "reorgan": [2, 23], "repeat": [2, 3, 5, 6, 7, 8, 11, 12, 13, 15, 16, 18, 20, 23], "repeated": 23, "repeatedli": [2, 8, 12, 15], "repet": 5, "repetit": [8, 23], "rephras": 15, "replac": [2, 3, 5, 6, 7, 8, 12, 14, 16, 17, 23], "replica": 8, "repo": 0, "report": 23, "repositori": [2, 6, 23], "repres": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 20, 23], "represent": [2, 3, 5, 8, 20, 23], "representd": 5, "reproduc": [0, 1, 2, 7, 8, 11, 14, 17, 20, 23], "repuls": [2, 23], "request": [2, 15], "requir": [0, 2, 3, 5, 6, 7, 8, 10, 11, 13, 14, 15, 18, 23], "res1": 4, "res2": 4, "res3": 4, "res_analyt": 4, "res_analytical1": 4, "res_analytical2": 4, "res_analytical3": 4, "resaml": 8, "resampl": [2, 9, 12, 17, 23], "rescal": [2, 13, 14], "rescu": 7, "reseach": 8, "research": [2, 6, 15, 17, 22, 23], "resembl": [8, 20], "reserv": [3, 7, 8, 20], "reshap": [2, 3, 4, 5, 6, 8, 10, 11, 12, 18, 23], "residenti": 2, "residu": [2, 7, 15, 23], "resiz": 7, "resourc": 23, "respect": [1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 14, 15, 16, 20, 23], "respond": 14, "respons": [2, 9, 11, 14, 23], "rest": [2, 7], "restat": [2, 14, 23], "restor": 6, "restored_discrimin": 6, "restored_gener": 6, "restrict": [2, 5, 11, 14, 23], "result": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "retail": 2, "retain": [7, 8], "retriev": 23, "return": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 15, 16, 18, 20, 23], "return_data": 16, "return_sequ": 6, "return_x_i": 11, "reus": [3, 5, 8], "reveal": [2, 14, 23], "revers": [3, 18], "review": [17, 18], "revisit": 16, "revolut": 23, "reward": [2, 6, 23], "rewrit": [1, 2, 5, 7, 8, 9, 10, 12, 13, 14, 15, 18, 20], "rewritten": [4, 8, 10, 12, 20], "rewrot": 15, "rf": 12, "rgb": 5, "rgoj5yh7evk": 17, "rh": 8, "rho": [2, 12, 15, 23], "rho_1": 12, "rho_2": 12, "rho_m": 12, "rich": [2, 23], "ride": 11, "rideclass": 11, "ridedata": 11, "ridg": [9, 13, 15, 17, 23], "ridge_sk": 8, "ridgebeta": 7, "right": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 16, 18, 20, 23], "right_sid": 4, "rightarrow": [2, 3, 7, 8, 10, 13, 14, 15, 20, 23], "rigor": [2, 23], "ring": 8, "rise": [2, 23], "risk": [2, 15, 23], "rival": 6, "river": 2, "rlm": 23, "rm": [2, 20], "rmse": 2, "rmsporp": 15, "rmsprop": [3, 5, 6, 15], "rnd_clf": 12, "rng": 20, "rnn": [6, 14], "rnn1": 6, "rnn2": 6, "rnn_2layer": 6, "rnn_input": 6, "rnn_output": 6, "rnn_train": 6, "rntrick1": 20, "rntrick2": 20, "rntrick3": 20, "rntrick4": 20, "ro": [2, 15, 23], "robert": 22, "robust": [2, 23], "robustscal": 2, "roc": [9, 12], "role": [2, 4, 7, 8, 10, 17, 23], "roll": 8, "room": [2, 21, 23], "root": [0, 2, 7, 11, 15, 20], "rot": 23, "rotat": [3, 10, 11, 12], "rotation_matrix": 11, "roughli": [3, 5], "round": [2, 9, 11, 15], "routin": [15, 18, 23], "row": [1, 2, 3, 4, 7, 8, 11, 13, 18, 23], "rr": 7, "rrr": 7, "rug": 15, "rule": [2, 3, 7, 8, 15, 23], "run": [0, 2, 3, 4, 6, 7, 8, 10, 11, 13, 15, 17, 23], "runtim": [0, 3, 8, 16], "rust": [2, 17, 18, 23], "rvert": 3, "rvert_2": 3, "s_": [5, 8], "s_1": 8, "s_i": [8, 9], "s_j": 8, "s_k": 8, "saddl": 15, "sai": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 18, 20, 23], "said": [8, 11, 15], "sake": [2, 7, 9, 13, 23], "sale": [2, 23], "sam": 23, "same": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 14, 16, 18, 20, 23], "samm": 12, "sampl": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 16, 17, 18, 20, 23], "sample_vari": 16, "sampleexptvari": 20, "samwis": 23, "sastri": 13, "satisfactori": [2, 23], "satisfi": [3, 4, 5, 8, 10, 15, 18, 20], "satur": [3, 8], "save": [2, 6, 8, 9, 11, 15, 23], "save_fig": [2, 8, 9, 11, 12, 23], "savefig": [2, 6, 8, 9, 11, 20, 23], "savetxt": 6, "saw": 7, "scalabl": 12, "scalar": [4, 7, 8, 12], "scale": [2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 21, 23], "scale_mean": 6, "scale_std": 6, "scaler": [2, 9, 10, 11, 12, 13], "scan": [7, 9], "scari": 7, "scatter": [0, 2, 3, 8, 9, 10, 11, 16, 23], "scenario": [8, 15], "schedul": 15, "scheme": [3, 15], "schrage": 20, "sch\u00f8yen": 8, "scienc": [2, 3, 12, 14, 15, 17, 19, 20, 21, 22], "scientif": [2, 17, 23], "scientist": [2, 23], "scikit": [0, 1, 5, 7, 8, 10, 11, 12, 15, 17, 18, 22], "scikit_learn": 2, "scikitlearn": 23, "scikitplot": [9, 12], "scipi": [2, 5, 7, 8, 15, 17, 18, 23], "scl": 8, "scm": 0, "score": [1, 2, 3, 5, 8, 9, 11, 12, 13, 21, 23], "scores_kfold": 8, "scratch": [1, 3, 15], "sdg": 15, "seaborn": [2, 3, 5, 8, 9, 23], "seamless": [2, 17, 23], "search": [0, 2, 3, 5, 7, 11, 15, 23], "sebastian": 23, "sebastianraschka": 23, "sec": 8, "second": [0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 16, 17, 18, 20, 21, 23], "secondeigvector": 13, "secondli": 14, "section": [1, 6, 13, 18, 20], "sector": 2, "see": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 17, 18, 20, 23], "seed": [2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 15, 16, 20, 23], "seed_imag": 6, "seek": [3, 4, 10], "seem": [3, 5, 6], "seemingli": [2, 23], "seen": [2, 3, 5, 7, 12, 14, 20, 23], "segment": 15, "seismic": 8, "seldomli": [2, 23], "select": [0, 3, 7, 8, 10, 11, 12, 13, 19, 20, 21, 22, 23], "selevet": 0, "self": [3, 7], "sell": 6, "semest": [9, 19], "semi": [10, 15], "semilogx": 8, "send": [7, 14, 15, 21, 23], "senior": [19, 21], "sens": [2, 6, 8, 10, 23], "sensibl": 5, "sensit": [2, 7, 8, 11, 15, 23], "sent": 4, "sentenc": [6, 14], "separ": [2, 3, 4, 6, 8, 10, 11, 14, 16, 17, 20, 23], "septemb": 23, "sequenc": [5, 6, 9, 11, 12, 14, 15, 17, 18, 20, 23], "sequenti": [3, 5, 6, 12, 14, 20], "seri": [2, 3, 4, 5, 6, 7, 8, 12, 13, 14, 15, 18, 23], "serif": [2, 9, 20, 23], "serv": [2, 3, 4, 5, 7, 9, 15, 22, 23], "session": [0, 3, 19, 21, 23], "set": [1, 3, 6, 7, 8, 9, 10, 12, 13, 15, 16, 17, 18, 20, 21], "set_major_formatt": 8, "set_major_loc": 8, "set_tick": [3, 10], "set_ticklabel": 3, "set_titl": [2, 3, 4, 5, 9, 14, 16, 23], "set_xlabel": [2, 3, 4, 5, 9, 14, 23], "set_xlim": [9, 14], "set_xticklabel": 3, "set_ylabel": [2, 3, 4, 5, 9, 23], "set_ylim": [9, 14], "set_ytick": 9, "set_yticklabel": [3, 8], "set_zlim": 8, "seth": 6, "setminu": 8, "setosa": [10, 11], "setosa_or_versicolor": 10, "setp": 8, "setup": [3, 6, 8, 10, 17, 23], "sever": [1, 2, 5, 7, 8, 9, 10, 11, 13, 14, 15, 17, 18, 20, 23], "sgd": [3, 5], "sgd_clf": 10, "sgdclassifi": 10, "sgdreg": 15, "sgdregressor": 15, "sgn": 7, "shallow": 15, "shape": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 23], "share": [0, 3, 5, 23], "shareabl": 0, "she": 9, "shift": [0, 3, 8, 14, 20], "ship": 5, "shire": 23, "short": [6, 7], "shortcom": 15, "shorten": 6, "shorter": 20, "shorthand": 23, "shortli": [18, 23], "should": [0, 2, 4, 5, 7, 8, 10, 11, 13, 14, 18, 20, 23], "show": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "show_shap": 6, "shown": [2, 6, 7, 10, 14, 15, 18], "shrink": [5, 7, 8, 10, 13], "shrinkag": [7, 8], "shrunk": 13, "shuffl": [2, 3, 6, 8, 15], "side": [2, 4, 7, 10, 14, 15, 18, 23], "sigh": [17, 23], "sigma": [2, 3, 7, 8, 9, 12, 13, 14, 15, 18, 20, 23], "sigma0": 20, "sigma1": 20, "sigma2": 20, "sigma_": [7, 18, 23], "sigma_0": 7, "sigma_1": 7, "sigma_2": 7, "sigma_fn": [9, 14], "sigma_i": [2, 7, 23], "sigma_j": 7, "sigma_m": [8, 20], "sigma_n": [13, 20], "sigma_t": 15, "sigma_x": 20, "sigmoid": [3, 4, 6, 9, 10, 12, 14], "sigmundson": 8, "sign": [3, 4, 9, 10, 12, 20, 21], "signal": [3, 5, 12, 14], "signifi": 6, "signific": 3, "significantli": [3, 15, 20], "sim": [6, 7, 8, 15, 20], "similar": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 16, 17, 18, 23], "similarli": [2, 3, 5, 7, 10, 12, 15, 20, 23], "simpl": [1, 3, 4, 5, 7, 8, 9, 10, 12, 13, 14, 16, 17, 18, 20], "simplepredict": 12, "simpler": [1, 2, 3, 7, 8, 9, 15, 17, 23], "simplernn": 6, "simplest": [2, 3, 5, 6, 11, 12, 14, 16, 23], "simpletre": 12, "simpli": [2, 3, 4, 6, 7, 8, 10, 11, 12, 13, 14, 17, 18, 20, 23], "simplic": [4, 7, 8, 9, 10, 11, 12, 13, 14, 16], "simplicti": 7, "simplifi": [2, 8, 11, 17, 23], "simplist": [5, 8, 20], "simul": 8, "simultan": 8, "sin": [2, 3, 4, 5, 6, 11, 14, 15, 18, 23], "sinc": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 15, 18, 20, 22, 23], "sine": [5, 14], "singl": [2, 3, 4, 5, 7, 8, 9, 10, 11, 14, 15, 18, 20, 23], "singular": [2, 8, 15, 18, 23], "sinusoid": 5, "site": [2, 19], "situat": [2, 6, 7, 9, 15, 20, 23], "six": [5, 20], "size": [2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 15, 18, 20, 23], "skelarn": 0, "sketch": 12, "ski": 11, "skill": 2, "skip": 13, "skl": [2, 8, 23], "sklearn": [0, 2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 15, 16, 23], "skplt": [9, 12], "sl": 8, "slack": 10, "slice": [4, 18, 23], "slide": [1, 2, 5, 20, 23], "slight": [8, 15], "slightli": [3, 4, 5, 7, 8, 9, 12, 20], "slope": [10, 13, 14], "slow": [2, 4, 10, 15], "slower": [7, 18, 23], "slowest": 18, "slowli": 14, "slp": 3, "small": [2, 3, 4, 5, 7, 8, 10, 11, 12, 13, 14, 15, 17, 18, 20, 23], "smaller": [2, 3, 4, 7, 8, 10, 11, 13, 15, 20, 23], "smallest": [2, 6, 16, 23], "smallest_row_index": 16, "smooth": [2, 5, 8, 15, 23], "sn": [2, 3, 5, 8, 9, 23], "sne": 13, "sneak": 23, "so": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23], "soar": 8, "social": 2, "soft": [3, 9, 12, 14], "soften": 10, "softmax": [5, 9], "softwar": [2, 10, 17, 18], "sol": 10, "sole": [2, 8, 23], "solid": [2, 9], "solut": [2, 3, 4, 5, 7, 8, 10, 12, 13, 15, 18, 20, 23], "soluton": 4, "solv": [1, 2, 3, 5, 7, 8, 10, 12, 13, 14, 15, 18, 23], "solve_expdec": 4, "solve_ode_deep_neural_network": 4, "solve_ode_neural_network": 4, "solve_pde_deep_neural_network": 4, "solveod": 4, "solveode_popul": 4, "solver": [4, 9, 10, 11, 12, 18, 23], "some": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 20, 23], "some_model": 8, "somehow": 6, "someon": 1, "someth": [0, 2, 3, 5, 6, 9, 11, 13, 20, 23], "sometim": [2, 3, 13, 14, 15, 16, 23], "soon": [18, 21], "sophist": [2, 23], "sopt": 15, "sort": [7, 8, 11, 13, 20], "sound": [5, 7], "sourc": [2, 3, 5, 8, 17, 18, 20, 23], "space": [2, 3, 6, 7, 10, 11, 13, 14, 15, 16, 20], "span": [2, 5, 7, 11, 13, 18, 23], "spare": 3, "spars": [5, 8, 18, 23], "sparse_mtx": [18, 23], "sparsecategoricalcrossentropi": 5, "sparsiti": 12, "spatial": [3, 4, 5, 14], "speak": 20, "special": [8, 9, 12, 14, 15, 18, 20, 23], "specif": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 17, 18, 20, 22, 23], "specifi": [2, 5, 7, 8, 9, 11, 13, 15, 16, 20, 23], "specifici": [2, 12, 23], "spectacular": 5, "spectral": 3, "speech": [2, 3, 5, 6, 14], "speed": [3, 4, 6, 15], "spend": [1, 20], "sphere": 2, "spin": 8, "spite": 2, "spline": 10, "split": [1, 3, 5, 6, 7, 8, 10, 11, 12, 13, 16, 20], "splite": 2, "splitter": [3, 12], "spontan": 20, "spot": 5, "spread": [2, 13, 20, 23], "springer": [22, 23], "spuriou": 15, "sqrsignal": 5, "sqrt": [2, 5, 6, 7, 8, 10, 12, 13, 15, 20], "squar": [0, 3, 4, 5, 6, 9, 10, 11, 13, 15, 16, 17, 18, 20], "squarederror": 12, "squaredeuclidean": 16, "squash": 14, "srtm": 8, "srtm_data_norway_1": 8, "stabil": 7, "stabl": [1, 2, 6, 7, 8, 11, 17, 23], "stack": [5, 6], "stage": [0, 7, 15], "stai": [2, 4, 6, 7, 13, 23], "stand": [2, 7, 11, 14, 23], "standard": [2, 3, 6, 7, 8, 9, 10, 12, 14, 18, 20, 23], "standardscal": [2, 8, 9, 10, 11, 12, 13], "stanford": 15, "start": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 18, 20, 21, 23], "start_tim": 16, "stat": 8, "state": [3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 17, 20], "statement": [2, 9, 18, 23], "statist": [2, 3, 5, 6, 9, 11, 12, 13, 14, 15, 16, 18, 22], "statu": [0, 2, 9, 23], "stavang": 8, "std": [2, 6, 8, 23], "steep": 15, "step": [0, 2, 3, 4, 6, 8, 9, 11, 12, 13, 14, 15, 16, 18, 23], "step_fn": [9, 14], "step_length": 15, "steps_list": 11, "stereo": 5, "still": [2, 4, 5, 7, 8, 13, 15, 20], "stimuli": 14, "stk": [22, 23], "stk2100": [22, 23], "stk3155": [0, 19, 21], "stk4021": [22, 23], "stk4051": [22, 23], "stk4155": [19, 21], "stk5000": 22, "stochast": [2, 3, 7, 8, 10, 13, 14, 23], "stock": 6, "stoke": 14, "stone": [2, 9], "stop": [3, 6, 11, 15, 16], "storag": 7, "store": [2, 3, 4, 5, 8, 13, 15, 20, 23], "storehaug": [21, 23], "str": [3, 5, 6], "straight": [2, 8, 10, 15, 23], "straightforward": [2, 4, 5, 7, 8, 10, 11, 12, 15, 18, 23], "strategi": [2, 3, 11, 23], "stratifi": 8, "strength": [2, 7, 16], "stretch": 13, "strict": [10, 15], "strictli": [10, 15], "stride": [6, 18], "strike": 8, "string": 3, "stroke": 9, "strong": [5, 8, 11, 12, 14, 18, 20], "strongli": [0, 2, 10, 17, 18], "stronli": 2, "structur": [2, 3, 4, 5, 8, 11, 12, 14, 17, 23], "stuck": [3, 15], "student": [0, 2, 19, 21, 22, 23], "studi": [2, 5, 6, 7, 8, 9, 10, 13, 14, 15, 17, 22, 23], "studier": 22, "style": [2, 9, 11, 18, 23], "st\u00f8land": [21, 23], "sub": [11, 14], "subdivid": [2, 18, 23], "subfield": 2, "subject": [8, 10, 20], "submit": 23, "subplot": [2, 3, 5, 6, 8, 9, 10, 11, 12, 16, 23], "subplots_adjust": [10, 20], "subprogram": [18, 23], "subract": 2, "subroutin": [2, 23], "subscript": 3, "subsequ": [3, 6, 7, 8, 14, 18, 20], "subset": [3, 8, 11, 14, 15, 17, 23], "subspac": [2, 10, 13], "substanti": [11, 12], "substep": 13, "substitut": [1, 5, 8, 14, 18], "subsubset": 11, "subtask": 8, "subtl": 3, "subtract": [2, 6, 7, 8, 13, 15, 18, 20], "subtre": 11, "succeed": [2, 6, 23], "success": [5, 9, 11, 15, 20], "successfulli": [6, 11], "sudo": [2, 17, 23], "suffer": [2, 3, 4, 7, 12, 23], "suffici": [3, 8, 10, 13, 15], "suggest": [3, 15, 22], "suit": [10, 14], "suitabl": [0, 2, 20], "sum": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "sum_": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "sum_i": [2, 4, 7, 8, 10, 15], "sum_j": 8, "sum_ja_": 2, "sum_k": [8, 10, 14, 18], "sum_logist": 15, "sum_m": 5, "sum_n": 5, "sum_nx_": 5, "summar": [7, 8, 11], "summari": [3, 5, 6, 12, 19], "summat": [1, 2, 5], "sunni": 11, "super": 7, "superfici": 5, "superscript": [3, 14], "supervis": [2, 7, 8, 9, 11, 14, 17, 23], "supplement": 9, "support": [2, 3, 11, 12, 13, 15, 17, 23], "suppos": [2, 7, 8, 9, 10, 12, 13, 14, 15, 18, 23], "suppress": [7, 15], "sure": [1, 2, 3, 6, 8], "surf": 8, "surfac": [2, 8, 23], "surpass": 8, "surpris": [2, 23], "surround": [5, 17], "survei": [2, 7, 8, 23], "svc": [10, 11, 12], "svd": [2, 8, 13, 23], "svdinv": 7, "svm": [10, 11, 12, 13], "svm_clf": [10, 12], "swath": 7, "switch": 2, "sy": 15, "symbol": [3, 7, 13, 15, 17, 20, 23], "symmeteri": 3, "symmetr": [2, 7, 10, 13, 14, 15, 18, 23], "symmetri": 8, "sympi": [2, 17, 23], "synonim": 20, "syntax": 15, "system": [0, 2, 3, 5, 6, 8, 9, 11, 12, 14, 15, 17, 18, 23], "systemat": [6, 8], "t": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23], "t0": [5, 8, 15], "t1": [4, 15], "t2": 4, "t3": 4, "t_": 4, "t_0": [4, 11, 15], "t_1": 15, "t_b": 12, "t_i": [3, 4, 7, 14], "t_j": 14, "t_k": 11, "tabl": [11, 20, 21, 23], "tabul": [2, 23], "tabular": 23, "tackl": 6, "tag": [4, 5, 6, 7, 8, 9, 14, 15, 16, 18, 20], "taht": [2, 23], "tail": 20, "tailor": [4, 10, 13, 23], "taiwan": [2, 23], "take": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 20, 23], "taken": [2, 3, 5, 8, 12, 15, 18, 23], "tan": 5, "tangent": [3, 6, 14, 15], "tanh": [3, 6, 9, 10, 14], "target": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 23], "target_nam": 11, "task": [2, 3, 5, 8, 11, 13, 14, 16, 23], "tau": [5, 7, 20], "taught": 23, "tax": 2, "taylor": [4, 15], "taylornr": 15, "tc": 10, "teach": [0, 19, 23], "team": 3, "teaser": 2, "technic": [2, 7, 8, 15, 23], "techniqu": [2, 3, 10, 12, 15, 17, 20, 22, 23], "technologi": [2, 3], "tell": [1, 2, 6, 8, 12, 13, 15, 20], "temp": 3, "temp1": 3, "temp2": 3, "temperatur": [2, 11, 23], "temporarili": 3, "ten": [5, 23], "tend": [5, 7, 8, 10, 11, 12, 14, 15, 16], "tendenc": [2, 23], "tension": 8, "tensor": 5, "tensorflow": [2, 4, 6, 10, 16, 17, 18, 22, 23], "term": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 23], "term1": [7, 8, 13], "term2": [7, 8, 13], "term3": [7, 8, 13], "term4": [7, 8, 13], "termin": [0, 2, 6, 7, 11, 12, 15, 23], "terrain": 8, "terrain1": 8, "test": [1, 5, 6, 7, 8, 9, 10, 11, 12, 15, 18, 20], "test_acc": 5, "test_accuraci": [3, 5], "test_error": 8, "test_imag": [5, 6], "test_ind": 8, "test_input": 6, "test_label": [5, 6], "test_loss": 5, "test_pr": 3, "test_predict": 3, "test_rnn": 6, "test_scor": [9, 12], "test_siz": [0, 2, 3, 5, 7, 8, 12, 23], "test_split": 11, "testerror": [2, 8], "testi": 6, "testpredict": 6, "testx": 6, "text": [2, 3, 4, 6, 7, 10, 11, 13, 15, 18, 20, 22], "textbook": 1, "textual": 11, "textur": 3, "tf": [3, 5, 6, 15, 16], "th": [2, 3, 4, 7, 8, 9, 11, 14, 15, 16, 18, 20, 23], "than": [2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 17, 20, 23], "thank": [6, 8], "theano": [3, 17, 23], "thei": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 18, 20, 23], "them": [2, 3, 5, 6, 8, 10, 11, 12, 13, 14, 15, 18, 23], "theme": [0, 2, 23], "themselv": [2, 20, 23], "thenc": 8, "theorem": [4, 8, 9], "theoret": [2, 6, 12, 23], "theori": [2, 3, 5, 10, 11, 14, 15, 17, 22, 23], "thereaft": [2, 7, 8, 13, 14, 18, 23], "therebi": [2, 7, 9, 13, 23], "therefor": [2, 3, 4, 5, 6, 8, 9, 10, 13, 15, 20, 23], "therein": 13, "thereof": [2, 8, 15, 23], "theta": [3, 6, 15, 20], "theta_": [3, 15], "theta_i": 3, "theta_linreg": 15, "theta_t": 15, "thi": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22], "thing": [0, 1, 2, 3, 4, 6, 7, 9, 11, 20, 23], "think": [2, 3, 5, 6, 8, 11, 14, 15, 16, 20, 23], "third": [2, 5, 8, 15, 21, 23], "thirti": 9, "thorughout": 23, "those": [2, 5, 7, 8, 10, 11, 12, 13, 18, 23], "though": [1, 3, 4, 5, 6, 15, 18, 20], "thought": [8, 16, 20], "thousand": [2, 3], "three": [2, 3, 5, 7, 8, 10, 11, 14, 18, 19, 20, 21, 23], "threshold": [3, 5, 11, 12, 13, 14, 15], "through": [0, 2, 3, 4, 5, 6, 7, 8, 10, 13, 14, 15, 16, 17, 18, 20, 23], "throughout": [0, 2, 6, 7, 16, 17, 18, 20, 23], "throw": [5, 8, 20], "thu": [2, 3, 4, 7, 8, 9, 10, 12, 13, 14, 15, 21, 23], "thumb": [2, 8, 23], "tibshirani": [8, 22, 23], "tick_param": 8, "ticker": [8, 15, 20], "tif": 8, "tight_layout": [3, 9], "tightli": 13, "tild": [2, 7, 8, 9, 13, 20, 23], "till": [2, 6, 9, 10, 11, 12, 14, 18, 23], "time": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "timeit": 6, "timer": 6, "tini": 3, "tip": 5, "titl": [0, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 15, 20, 23], "tmp": 15, "tn": [4, 5, 9], "to_categor": [3, 5, 6], "to_categorical_numpi": 3, "to_numer": [2, 8, 23], "todai": 5, "togeth": [2, 5, 8, 10, 13, 15, 17, 23], "toi": 16, "told": 15, "toler": [4, 16], "tolist": 6, "tomographi": 14, "too": [2, 4, 6, 7, 8, 11, 13, 15, 20, 22, 23], "took": [10, 23], "tool": [0, 2, 3, 5, 8, 15, 17], "toolbox": 10, "top": [2, 5, 7, 8, 11, 12, 17, 23], "topic": [2, 7, 8, 9, 10, 17], "topolog": [5, 14], "topologi": [3, 14], "torkjellsdatt": [21, 23], "toss": [12, 20], "total": [2, 3, 4, 5, 6, 8, 9, 10, 12, 13, 14, 15, 16, 18, 20, 21, 23], "total_loss": 6, "totalclustervari": 16, "totalscatt": 16, "toward": [0, 3, 4, 9, 14, 15], "town": 2, "tp": [6, 9], "tpng": 11, "tpu": [15, 17, 23], "tqdm": 8, "track": [0, 5, 15, 16, 18], "tract": 2, "tractabl": [2, 23], "trade": [7, 11], "tradeoff": [2, 7, 23], "tradit": [2, 3, 6, 8, 23], "train": [1, 4, 5, 7, 8, 10, 11, 12, 13, 14, 15], "train_accuraci": [2, 3, 5, 23], "train_dataset": 6, "train_end": [2, 3], "train_error": 8, "train_imag": [5, 6], "train_ind": 8, "train_label": [5, 6], "train_pr": 3, "train_siz": [2, 3, 5], "train_step": 6, "train_test_split": [0, 1, 2, 3, 5, 7, 8, 9, 11, 12, 13, 23], "train_test_split_numpi": [2, 3], "trainable_vari": 6, "trained_model": 8, "trainerror": 2, "traini": 6, "training_checkpoint": 6, "training_dataset": 6, "training_gradi": 15, "trainingerror": 8, "trainpredict": 6, "trainscor": 6, "trainx": 6, "trait": [2, 23], "trajectori": 6, "transfer": 11, "transform": [2, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 23], "transit": [8, 14], "translat": [3, 6, 8, 12, 23], "transpos": [3, 7, 13, 18], "travers": [2, 7], "treat": [2, 3, 5, 8, 14, 15, 20, 23], "tree": [2, 3, 17, 23], "tree_clf": [11, 12], "tree_clf_": 11, "tree_clf_sr": 11, "tree_reg": 11, "tree_reg1": 11, "tree_reg2": 11, "trend": 20, "treue": 9, "trevor": 22, "tri": [4, 5, 6, 11, 15], "triain": 2, "trial": [2, 4, 6, 8, 15, 20, 23], "triangl": 15, "triangular": 18, "trick": [5, 6, 10, 13, 15, 20], "trickier": 20, "tridiagon": 18, "trillion": 17, "trivial": [2, 3, 7, 13, 20, 23], "troubl": [0, 2, 10, 14], "truck": 5, "true": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 18, 20, 23], "true_beta": 8, "true_fun": 8, "try": [0, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, 20, 23], "tucker": 10, "tuesdai": [21, 23], "tumor": [9, 11], "tumour": 9, "tunabl": 3, "tune": [6, 11, 15, 18, 23], "turn": [2, 3, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "tutori": [3, 6], "tv": 4, "tveito": 4, "tweak": [3, 6, 12, 20], "twice": 15, "twist": 13, "two": [0, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 18, 19, 20, 22, 23], "tx": 15, "tx_1": 15, "txt": [0, 6], "ty": 15, "type": [2, 3, 5, 8, 10, 12, 15, 18, 20], "typic": [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 12, 14, 15, 20, 23], "u": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 22, 23], "u_": 18, "u_i": 14, "u_m": 12, "ua": [2, 23], "ubuntu": [2, 17, 23], "uci": 2, "uio": [0, 21, 22], "un": 16, "unabl": 0, "unari": [18, 23], "unbalanc": [8, 11], "unbias": [2, 7, 8, 23], "uncent": 8, "uncertainti": [2, 7, 23], "uncertitud": 20, "unchang": [3, 5], "uncorrel": [12, 20], "undefin": 7, "under": [2, 3, 7, 8, 12, 15, 17, 23], "underdetermin": [2, 23], "underfit": [3, 8], "underflowproblem": 7, "undergo": 7, "undergradu": [19, 21], "underli": [2, 3, 11, 15, 20, 23], "underset": [6, 16], "understand": [0, 2, 3, 5, 7, 8, 12, 15, 16, 17, 23], "understood": [10, 15], "undesir": 10, "undetermin": [7, 10], "undo": 6, "unexpect": 8, "unexpected": 20, "unfair": 8, "unfortun": [3, 10, 11, 12], "unicode_liter": [10, 11], "uniform": [2, 3, 7, 8, 13, 15, 20, 23], "uniformli": [15, 20], "unifrompdf": 20, "unimport": 15, "union": [7, 8], "uniqu": [2, 4, 8, 15, 16, 18, 23], "unique_cluster_label": 16, "unit": [2, 3, 5, 6, 7, 12, 14, 20, 23], "unitari": [7, 8, 18], "unitarili": [18, 23], "uniti": 20, "univari": 20, "univers": [2, 3, 4, 15, 17, 19, 21, 23], "unix": 3, "unknow": [2, 18, 23], "unknown": [2, 3, 5, 6, 7, 8, 10, 12, 15, 18, 23], "unknowwn": 14, "unlabel": 3, "unless": [2, 5, 8, 13, 15, 23], "unlik": [3, 5, 10, 15], "unnecessarili": 11, "unord": 5, "unravel": 3, "unrol": [5, 13], "unseen": [0, 2, 9, 11], "unstabl": 3, "unsupervis": [2, 3, 6, 14, 17, 23], "unsymmetr": [18, 23], "until": [3, 4, 6, 11, 14, 15, 16], "untouch": 2, "unusu": 14, "up": [1, 3, 5, 6, 7, 8, 10, 12, 13, 15, 16, 17, 18, 20, 21], "updat": [0, 3, 4, 12, 14, 15, 16], "uploa": 23, "upload": [0, 17, 22], "upon": [2, 3, 8, 9, 13, 18], "upper": [1, 2, 10, 11, 18], "uppercas": [18, 23], "upsampl": 6, "upscal": 6, "url": 23, "us": [0, 6, 7, 8, 10, 11, 12, 13, 14, 16, 18, 20, 22], "usa": 23, "usag": [2, 10, 17, 23], "usd": 2, "usd10000": 2, "use_bia": 6, "usecol": [2, 23], "useless": 3, "user": [2, 3, 4, 6, 8, 9, 17, 18, 23], "usernam": 0, "usetex": 20, "usg": 8, "usr": 20, "usual": [2, 5, 6, 9, 14, 15, 16, 23], "ut": 7, "util": [3, 5, 6, 8, 9, 12, 16, 23], "ux": 18, "v": [0, 2, 4, 6, 7, 8, 13, 15, 17], "v0": 20, "v1": 20, "v2": 20, "v_0": 13, "va": 3, "vahid": 23, "val": 15, "val_accuraci": 5, "val_loss": 6, "vale": 4, "valid": [2, 3, 6, 9, 11, 12, 15, 17, 20, 23], "validation_data": 5, "validation_split": 6, "valu": [1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 23], "valuat": 11, "valy": 6, "van": [2, 23], "vandenbergh": [10, 15], "vandermond": [2, 23], "vanilla": [2, 8, 13, 16], "vanish": [3, 6, 15, 20], "var": [7, 8, 12, 13, 20], "var_x": 20, "varabl": 10, "varepsilon": [7, 8], "varepsilon_": [7, 8], "varepsilon_i": [7, 8], "vari": [2, 3, 5, 7, 8, 12, 23], "variabl": [2, 3, 4, 7, 8, 9, 10, 12, 13, 14, 15, 16, 18, 23], "varianc": [2, 3, 7, 9, 11, 12, 13, 15, 16, 17, 18, 20, 23], "variance_i": [7, 13], "variance_x": [7, 13], "variant": [2, 3, 8, 10, 14, 15, 23], "variat": [5, 6, 13, 23], "varieti": [2, 5, 14, 17, 23], "variou": [1, 3, 5, 7, 8, 9, 10, 11, 13, 14, 15, 17, 18, 20], "varydimens": 6, "vastli": 5, "vaue": 3, "vault": 2, "vdot": [4, 15], "vec": 8, "vector": [2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 15, 16, 17], "vector_mean": 16, "ventur": [2, 10, 17, 23], "venv": 0, "verbos": [3, 5, 6], "veri": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 22, 23], "verifi": [5, 13, 18, 23], "versatil": [10, 23], "versicolor": [10, 11], "version": [0, 2, 5, 12, 15, 16, 17, 18, 20, 23], "versu": 3, "vert": [1, 2, 3, 7, 8, 9, 10, 11, 13, 15, 23], "vert_1": [7, 8], "vert_2": [7, 8, 13], "via": [2, 7, 8, 9, 10, 11, 12, 13, 14, 17, 18, 19, 20, 21, 23], "vidal": 13, "video": [2, 3, 14, 17, 19, 21, 23], "view": [3, 5, 7, 8, 14, 15, 20, 22, 23], "violat": 10, "virginica": 11, "viridi": [2, 3, 4, 5, 23], "virtual": 3, "viscos": 15, "viscou": 15, "visibl": 0, "vision": [2, 5], "visual": [2, 5, 13, 14, 17, 23], "visualis": 3, "visualstudio": [0, 1], "viz": [8, 10, 20], "vmap": 15, "vmax": [3, 8], "vmin": [3, 8], "voic": 5, "volum": [2, 5, 23], "vote": 12, "voting_clf": 12, "votingclassifi": 12, "votingsimpl": 12, "vstack": [7, 13, 18, 20, 23], "vt": 7, "w": [2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 18, 20, 23], "w1": 10, "w2": [10, 13], "w3": 10, "w_": [3, 14], "w_1": [10, 18], "w_1x_": 10, "w_1x_1": 10, "w_2": [10, 18], "w_2x_": 10, "w_2x_2": 10, "w_3": 18, "w_4": 18, "w_hidden": 4, "w_i": [3, 4, 12], "w_ix_i": 14, "w_j": 18, "w_m": 18, "w_output": 4, "w_px_": 10, "w_px_p": 10, "wa": [2, 3, 5, 6, 7, 8, 9, 12, 13, 14, 16, 18, 23], "wai": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 18, 20, 23], "walk": 11, "walker": 20, "wang": [2, 23], "want": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 20, 23], "warn": 6, "warrant": 8, "wast": 5, "watch": 17, "wave": 5, "wavelet": 10, "we": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22], "weak": [11, 12, 16], "weather": [3, 14], "web": [17, 19, 21, 23], "webpag": 23, "websit": [8, 18, 19, 23], "wedg": [10, 20], "wednesdai": [21, 23], "wee": 13, "week": [2, 7, 8, 9, 19, 21], "weekli": [0, 1, 17, 19, 21, 22, 23], "weight": [2, 3, 4, 5, 8, 9, 11, 12, 14, 15, 20], "weigth": 4, "welcom": [0, 10, 17], "well": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 17, 18, 20, 22, 23], "went": 10, "were": [2, 3, 5, 6, 7, 8, 9, 10, 12, 13, 14, 16, 20, 23], "wessel": [2, 23], "what": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20], "whatev": 5, "when": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "whenev": [0, 15, 20], "where": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23], "wherea": [8, 20], "wherein": [3, 14], "whether": [2, 5, 7, 9, 11, 20, 23], "which": [0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23], "whichev": [3, 5], "while": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 20, 23], "white": 11, "who": [0, 2], "whole": [3, 5, 6, 7, 11, 13, 15], "whose": [2, 8, 12, 20], "whow": 13, "why": [0, 1, 2, 3, 5, 8, 15], "wide": [2, 3, 5, 8, 9, 14, 17, 18, 23], "widehat": 8, "width": [2, 5, 10, 11, 23], "wieringen": [2, 23], "win": 12, "wind": 11, "wing": [21, 23], "winther": 4, "wiothout": 8, "wiscons": 9, "wisconsin": 12, "wisdom": 8, "wise": [2, 3, 7, 14, 15], "wish": [2, 4, 7, 9, 10, 13, 15, 16, 18, 23], "with_std": 2, "wither": 8, "within": [2, 4, 5, 6, 9, 11, 14, 15, 16, 20, 22, 23], "withinclust": 16, "without": [0, 2, 3, 7, 8, 10, 11, 13, 14, 15, 23], "won": [0, 2, 23], "wonder": 10, "word": [2, 3, 5, 6, 7, 8, 9, 16, 20, 23], "work": [0, 1, 2, 3, 6, 8, 9, 10, 11, 15, 17, 19, 20, 21, 23], "workshop": 23, "world": [1, 2, 10], "worldwid": [2, 23], "worri": 0, "wors": [2, 3, 5, 6, 8, 23], "worth": 11, "would": [1, 2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "wrap": [8, 18, 23], "write": [0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 14, 15, 18, 23], "written": [1, 2, 4, 5, 7, 13, 14, 15, 17, 18, 20, 23], "wrong": [0, 3, 10], "wrongli": 12, "wrote": [7, 13], "wrt": [12, 15], "wth": [12, 15], "www": [17, 18, 22, 23], "wx_1": 10, "x": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "x0": 10, "x1": [6, 10, 11, 12, 15], "x1_exampl": 10, "x1d": 10, "x2": [10, 11, 12, 15], "x2d": [10, 13], "x2d_train": 13, "x2dsl": 13, "x3": 10, "x_": [2, 4, 5, 7, 8, 10, 12, 13, 15, 16, 18, 20, 23], "x_0": [2, 7, 13, 18, 23], "x_1": [2, 4, 7, 8, 9, 10, 11, 12, 13, 15, 18, 20, 23], "x_2": [2, 4, 7, 8, 9, 10, 11, 12, 13, 15, 18, 20, 23], "x_3": [10, 18, 20], "x_4": 18, "x_center": 13, "x_data": 3, "x_data_ful": 3, "x_hidden": 4, "x_i": [2, 3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "x_input": 4, "x_ix_": [2, 23], "x_iy_i": 10, "x_j": [1, 2, 4, 10, 11, 14, 20], "x_jy_j": 10, "x_k": [14, 16, 18, 20], "x_l": 20, "x_m": [8, 14, 18, 20], "x_n": [2, 4, 5, 8, 10, 13, 14, 15, 18, 20, 23], "x_new": [11, 12], "x_offset": 8, "x_output": 4, "x_p": [5, 9, 11], "x_poli": 11, "x_poly10": 11, "x_pred": 6, "x_prev": 4, "x_reduc": 13, "x_scale": 10, "x_small": 15, "x_test": [0, 1, 2, 3, 5, 7, 8, 9, 11, 12, 13, 23], "x_test_own": 8, "x_test_scal": [2, 8, 9, 11, 12, 13], "x_tot": 6, "x_train": [0, 1, 2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 23], "x_train_mean": 8, "x_train_own": 8, "x_train_scal": [2, 8, 9, 11, 12, 13], "x_val": 3, "xarrai": [17, 23], "xavier": 3, "xbkjd4ccgq": 23, "xbnew": 15, "xcode": [2, 17, 23], "xdclassiffierconfus": 12, "xdclassiffierroc": 12, "xg_clf": 12, "xgb": 12, "xgbclassifi": 12, "xgboost": 11, "xgboot": 12, "xgbregressor": 12, "xgparam": 12, "xgtree": 12, "xi": [10, 15], "xi_": 10, "xi_1": 10, "xi_i": 10, "xk": 10, "xla": [15, 17, 23], "xlabel": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 20, 23], "xlim": [8, 12], "xm": 11, "xmesh": 15, "xnew": [2, 15, 23], "xp": 20, "xpanda": 2, "xpd": [7, 13], "xplot": 2, "xscale": 2, "xsr": 11, "xt_x": 15, "xtest": 8, "xtick": [5, 8, 10, 11], "xtrain": 8, "xu": [2, 23], "xx": [2, 18, 23], "xy": [2, 8, 10, 18, 23], "xytext": 10, "xz": [18, 23], "y": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "y1": 6, "y2": 6, "y3": 6, "y_": [2, 3, 7, 8, 12, 13, 18, 23], "y_0": [2, 7, 13, 18, 23], "y_1": [2, 7, 10, 11, 13, 15, 18, 23], "y_1y_1": 10, "y_1y_1k": 10, "y_1y_2": 10, "y_1y_2k": 10, "y_1y_n": 10, "y_1y_nk": 10, "y_2": [2, 7, 10, 11, 13, 18, 23], "y_2y_1": 10, "y_2y_1k": 10, "y_2y_2": 10, "y_2y_2k": 10, "y_3": [2, 11, 18], "y_4": 18, "y_data": [2, 3, 7, 8, 23], "y_data_ful": 3, "y_decis": 10, "y_fit": 2, "y_i": [2, 3, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 23], "y_if_": 12, "y_ix_": [2, 23], "y_ix_i": [9, 10, 15], "y_iy_jk": 10, "y_j": [8, 10, 14], "y_k": 14, "y_m": 18, "y_model": [2, 6, 7, 8, 23], "y_n": [10, 15], "y_ny_1": 10, "y_ny_1k": 10, "y_ny_2": 10, "y_ny_2k": 10, "y_ny_n": 10, "y_ny_nk": 10, "y_offset": 8, "y_plot": 11, "y_pred": [2, 3, 6, 8, 9, 10, 11, 12], "y_pred1": 11, "y_pred2": 11, "y_pred_rf": 12, "y_pred_tre": 12, "y_proba": [9, 12], "y_scaler": 8, "y_test": [0, 1, 2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 23], "y_test_onehot": 3, "y_test_predict": 2, "y_tot": 6, "y_train": [0, 1, 2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 23], "y_train_mean": 8, "y_train_onehot": 3, "y_train_predict": 2, "y_train_scal": 8, "y_val": 3, "ye": [5, 8, 9], "year": [2, 17, 23], "yet": [2, 3, 8, 10, 13, 15, 23], "yi": 15, "yield": [2, 4, 7, 8, 10, 12, 14, 15, 16, 18, 20, 23], "yk": 10, "ylabel": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 20, 23], "ylim": [5, 8], "ym": 11, "ymesh": 15, "yn": 2, "yo": [10, 11, 12], "yoshua": [3, 22], "you": [0, 1, 2, 3, 5, 6, 7, 8, 10, 11, 12, 13, 15, 17, 18, 20, 21, 22, 23], "young": 2, "your": [0, 3, 4, 6, 7, 8, 10, 13, 15, 17, 18], "your_model_object": 1, "yourself": [13, 15, 23], "youtub": 17, "ypred": 8, "ypredict": [2, 15, 23], "ypredict2": 15, "ypredictlasso": 7, "ypredictol": [2, 7], "ypredictown": 8, "ypredictownridg": 8, "ypredictridg": [2, 7, 8], "ypredictskl": 8, "yridg": 23, "ytest": 8, "ytick": [5, 8, 10, 11], "ytild": [2, 8, 23], "ytildelasso": 7, "ytildenp": [2, 23], "ytildeol": [2, 7], "ytildeownridg": 8, "ytilderidg": [7, 8], "ytrain": 8, "yuxi": 23, "yx": [18, 23], "yy": [18, 23], "yz": [18, 23], "z": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 18, 20, 23], "z_": [3, 4, 14, 18, 23], "z_0": [18, 23], "z_1": [18, 23], "z_2": [18, 23], "z_c": 3, "z_h": 3, "z_hidden": 4, "z_i": [3, 14], "z_j": [3, 14], "z_k": 14, "z_m": 3, "z_mod": 11, "z_o": 3, "z_output": 4, "zaman": 20, "zaxi": 8, "zero": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "zeros_lik": 6, "zfill": 6, "zip": [6, 8], "zm_h": [2, 23], "zn": 2, "zone": 2, "zoom": 23, "zx": [18, 23], "zy": [18, 23], "zz": [18, 23], "\u00f8yvind": 8}, "titles": ["Exercises week 34", "Exercises week 35", "<span class=\"section-number\">3. </span>Linear Regression", "<span class=\"section-number\">14. </span>Building a Feed Forward Neural Network", "<span class=\"section-number\">15. </span>Solving Differential Equations  with Deep Learning", "<span class=\"section-number\">16. </span>Convolutional Neural Networks", "<span class=\"section-number\">17. </span>Recurrent neural networks: Overarching view", "<span class=\"section-number\">4. </span>Ridge and Lasso Regression", "<span class=\"section-number\">5. </span>Resampling Methods", "<span class=\"section-number\">6. </span>Logistic Regression", "<span class=\"section-number\">8. </span>Support Vector Machines, overarching aims", "<span class=\"section-number\">9. </span>Decision trees, overarching aims", "<span class=\"section-number\">10. </span>Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods", "<span class=\"section-number\">11. </span>Basic ideas of the Principal Component Analysis (PCA)", "<span class=\"section-number\">13. </span>Neural networks", "<span class=\"section-number\">7. </span>Optimization, the central part of any Machine Learning algortithm", "<span class=\"section-number\">12. </span>Clustering and Unsupervised Learning", "Applied Data Analysis and Machine Learning", "<span class=\"section-number\">2. </span>Linear Algebra, Handling of Arrays and more Python Features", "Course setting", "<span class=\"section-number\">1. </span>Elements of Probability Theory and Statistical Data Analysis", "Teachers and Grading", "Textbooks", "Week 34: Introduction to the course, Logistics and Practicalities"], "titleterms": {"": [10, 12], "1": [0, 1, 2, 23], "2": [0, 1, 2, 23], "2023": 21, "3": [0, 1, 2, 23], "34": [0, 23], "35": 1, "4": [0, 1, 2], "5": [1, 2], "A": [2, 3, 6, 10, 11, 23], "And": 23, "In": 21, "Ising": 8, "The": [0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 13, 14, 17, 23], "To": 23, "With": 6, "about": 23, "activ": [3, 14], "ad": [2, 8, 23], "adaboost": 12, "adagrad": 15, "adam": 15, "adapt": 12, "adjust": 3, "adversari": 6, "again": [5, 11], "ai": 23, "aim": [10, 11, 23], "aka": 23, "algebra": [18, 23], "algorithm": [11, 12, 13, 14], "algortithm": 15, "all": 10, "an": [0, 2, 6, 12, 23], "analys": 7, "analysi": [2, 7, 8, 13, 17, 20, 23], "analyt": [1, 2], "ani": 15, "anoth": 11, "appli": 17, "approach": [2, 10, 16, 23], "approxim": 14, "architectur": 3, "arrai": [18, 23], "assist": 21, "autocorrel": 20, "autograd": [4, 15], "automat": 15, "back": [3, 13, 14], "background": 17, "bag": 12, "base": 15, "basic": [2, 7, 9, 11, 12, 13, 18], "batch": 3, "bay": 7, "befor": 13, "better": 10, "bia": 8, "binari": 3, "bind": 23, "bird": 12, "boost": 12, "bootstrap": [8, 12], "boston": 2, "breast": 3, "brief": 23, "bring": 14, "build": [3, 5, 11], "c": 23, "can": 23, "cancer": [3, 9, 11, 13], "cart": 11, "case": [10, 12, 20], "central": [15, 17, 20], "chain": 14, "chang": 12, "channel": 23, "chi": [2, 23], "choos": 3, "cifar01": 5, "classic": 13, "classif": [3, 11, 12], "classifi": 10, "clip": 3, "cluster": 16, "cnn": 5, "code": [0, 1, 2, 3, 4, 7, 11, 13, 14, 15, 16, 23], "collect": [3, 5], "commun": 23, "compar": [1, 4, 12], "complex": [2, 8], "complic": 8, "compon": 13, "comput": 11, "computerlab": 23, "con": 11, "concept": 20, "conjug": 15, "contn": 23, "convex": [10, 15], "convolut": [5, 14], "correl": 13, "cost": [3, 12], "cours": [17, 19, 22, 23], "covari": [7, 13, 20], "cover": 23, "creat": 1, "cross": 8, "cython": 23, "data": [0, 2, 3, 5, 8, 9, 11, 13, 17, 20, 23], "dataset": [3, 5], "deadlin": 23, "deadllin": 21, "decai": 4, "decis": [11, 12], "decomposit": [7, 13, 18], "deep": [3, 4, 23], "defin": [3, 23], "degre": 2, "deliver": [0, 1], "dens": [2, 23], "deriv": [1, 7, 14], "descent": [4, 12, 15], "detail": [5, 23], "develop": 3, "diagon": 13, "differ": 10, "differenti": [4, 15], "diffus": 4, "dimension": [4, 5, 10], "disadvantag": 11, "discret": 20, "distribut": [7, 20], "do": 3, "domain": 20, "down": 3, "dropout": 3, "element": [2, 20, 23], "elimin": 18, "energi": 23, "ensembl": 12, "entropi": 11, "environ": [0, 2, 23], "equat": [2, 4, 14, 23], "error": [2, 12, 23], "essenti": 23, "etc": 23, "euler": 4, "evalu": 3, "exampl": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 23], "exercis": [0, 1, 2, 8, 23], "expect": 20, "experi": 20, "explor": [2, 23], "exponenti": 4, "express": 1, "extrapol": 6, "extrem": [12, 23], "ey": 12, "fall": 21, "famili": [3, 23], "famou": 18, "featur": [1, 11, 18], "feed": [3, 14], "final": 14, "find": 1, "fine": 3, "first": [6, 14, 23], "fit": [0, 1, 2, 12, 23], "forc": 5, "forest": 12, "format": 23, "forward": [3, 4, 14], "fourier": 5, "frank": 8, "freedom": 2, "frequentist": [2, 23], "from": [7, 12, 14], "full": 4, "function": [2, 3, 8, 9, 10, 12, 13, 14, 15, 20, 23], "further": [5, 7], "gan": 6, "gaussian": 18, "gd": 15, "gener": [6, 11, 23], "geometr": 13, "gini": 11, "github": 0, "goal": [0, 1], "good": [2, 23], "grade": [21, 23], "gradient": [3, 4, 12, 15], "growth": 4, "ha": 17, "handl": [18, 23], "hidden": 4, "hous": 2, "how": 1, "hyperparamet": 3, "hyperplan": 10, "i": [2, 3, 23], "id3": 11, "idea": 13, "ii": 23, "implement": [1, 3], "implic": 7, "import": [7, 18, 23], "improv": 3, "includ": 15, "increment": 13, "index": 11, "inform": 21, "input": 4, "instal": [17, 23], "instructor": 21, "interpret": [7, 13, 23], "introduc": [13, 15], "introduct": [2, 8, 17, 18, 23], "invers": [7, 18], "iter": 12, "jax": 15, "julia": 23, "jungl": 12, "kera": [3, 5], "kernel": [10, 13], "lagrangian": 10, "lasso": [7, 8], "later": 7, "layer": [3, 4, 5, 14], "learn": [0, 1, 2, 3, 4, 13, 15, 16, 17, 23], "least": [1, 7, 8, 23], "lectur": 23, "level": 12, "librari": [17, 23], "likelihood": 9, "limit": [3, 15, 20], "linear": [0, 2, 10, 15, 18, 23], "link": [7, 13, 22], "logist": [9, 23], "lu": 18, "machin": [2, 10, 15, 17, 23], "main": [20, 23], "make": [2, 11, 12, 23], "mani": [12, 14], "mass": 23, "materi": 23, "math": 7, "mathemat": [5, 7, 10], "matric": [7, 18, 23], "matrix": [1, 3, 7, 13, 14, 18, 23], "matter": [2, 23], "mean": 2, "meet": [7, 12, 20, 23], "mercer": 10, "method": [8, 11, 12, 15, 23], "minim": 23, "ml": 23, "mlp": 14, "mnist": [5, 6], "model": [0, 2, 3, 6, 8, 14, 23], "momentum": 15, "moon": [10, 11], "more": [5, 8, 18, 23], "multilay": 14, "multipl": [3, 5], "multipli": 10, "need": 23, "network": [3, 4, 5, 6, 9, 14, 23], "neural": [3, 4, 5, 6, 9, 14, 23], "new": 6, "non": 10, "normal": [2, 3], "notat": 14, "now": [3, 11, 15], "nuclear": [2, 23], "numba": 23, "number": [2, 4, 20], "numer": [4, 20], "numpi": [18, 23], "object": 5, "obtain": 13, "od": 4, "off": 8, "ol": [0, 1, 7, 8], "one": [4, 14], "oper": 18, "optim": [3, 10, 15, 17, 23], "order": 15, "ordinari": [1, 7, 8, 23], "organ": [2, 23], "oslo": 22, "other": [6, 11, 13, 14, 18, 23], "our": [2, 6, 7, 13, 15, 23], "outcom": [17, 23], "output": 4, "overarch": [2, 6, 10, 11, 23], "overview": [12, 23], "own": [2, 12, 13, 23], "packag": [18, 23], "panda": 23, "paramet": 23, "part": [15, 17], "partial": 4, "pass": 3, "pca": 13, "pdf": 20, "perceptron": 14, "perform": [3, 11], "period": 5, "perspect": 3, "point": 6, "poisson": 4, "polynomi": [1, 5], "popul": 4, "popular": 23, "practic": [15, 21, 23], "pre": [3, 5], "predict": 6, "prerequisit": [5, 17, 23], "princip": 13, "principl": 5, "pro": 11, "probabl": [7, 20], "problem": [3, 4, 15, 23], "procedur": [11, 23], "process": [3, 5], "program": [4, 15], "project": [8, 21, 23], "prop": 15, "propag": [3, 14], "properti": [7, 20], "python": [0, 2, 11, 17, 18, 23], "quick": 10, "r": 23, "random": [12, 13, 20], "read": [11, 23], "real": [8, 23], "recommend": 23, "recurr": [6, 14], "reduc": 2, "reduct": 5, "reformul": 4, "regress": [0, 2, 7, 8, 9, 11, 12, 15, 23], "regular": 3, "relev": 22, "relu": 3, "remark": 5, "remind": [8, 10, 23], "replac": 15, "repositori": 0, "requir": [4, 17], "resampl": 8, "rescal": 8, "resourc": 4, "revisit": 15, "rewrit": 23, "ridg": [2, 7, 8], "rm": 15, "rule": 14, "same": 15, "sampl": 13, "schedul": 23, "schemat": 11, "scheme": 4, "scienc": 23, "scikit": [2, 3, 13, 23], "second": 15, "semest": 21, "set": [0, 2, 4, 5, 11, 14, 19, 23], "setup": 0, "sgd": 15, "should": 3, "similar": 15, "simpl": [2, 6, 11, 15, 23], "singl": 12, "singular": [7, 13], "sklearn": 1, "soft": 10, "softmax": 3, "softwar": 23, "solv": 4, "solver": 15, "some": [15, 18], "specifi": 4, "split": [0, 2, 23], "squar": [1, 2, 7, 8, 12, 23], "standard": 15, "state": [2, 23], "statist": [7, 8, 17, 20, 23], "steepest": [12, 15], "stochast": [15, 20], "strongli": 23, "suggest": 23, "summari": [21, 23], "superposit": 5, "supervis": 3, "support": 10, "svd": 7, "systemat": 5, "take": 1, "teach": 21, "teacher": [21, 23], "techniqu": [8, 13], "technologi": 17, "tensorflow": [3, 5], "tent": [21, 23], "test": [0, 2, 3, 23], "text": 23, "textbook": [22, 23], "theorem": [7, 10, 13, 14, 20], "theori": 20, "thi": 23, "tip": 15, "togeth": 14, "tool": 23, "top": 3, "topic": 23, "toward": 13, "trade": 8, "tradeoff": 8, "train": [0, 2, 3, 6, 23], "transform": 5, "tree": [11, 12], "tune": 3, "two": [5, 10, 17], "type": [4, 6, 14, 23], "uio": 23, "univers": [14, 22], "unsupervis": 16, "up": [0, 2, 4, 11, 14, 23], "us": [1, 2, 3, 4, 5, 9, 15, 17, 23], "v": 5, "valid": 8, "valu": [7, 13, 20], "variabl": 20, "varianc": 8, "variou": [2, 23], "vector": [1, 10, 14, 18, 23], "view": [2, 6, 12], "virtual": 0, "visual": [3, 11], "wai": 11, "wave": 4, "we": 23, "week": [0, 1, 23], "what": [2, 23], "which": 3, "why": 23, "wisconsin": 9, "write": [6, 13], "xgboost": 12, "your": [1, 2, 12, 23]}})
\ No newline at end of file
+Search.setIndex({"alltitles": {"A Classification Tree": [[11, "a-classification-tree"]], "A Frequentist approach to data analysis": [[2, "a-frequentist-approach-to-data-analysis"], [23, "a-frequentist-approach-to-data-analysis"]], "A better approach": [[10, "a-better-approach"]], "A first summary": [[23, "a-first-summary"]], "A quick Reminder on Lagrangian Multipliers": [[10, "a-quick-reminder-on-lagrangian-multipliers"]], "A simple example": [[6, "a-simple-example"]], "A soft classifier": [[10, "a-soft-classifier"]], "A top-down perspective on Neural networks": [[3, "a-top-down-perspective-on-neural-networks"]], "ADAM optimizer": [[15, "adam-optimizer"]], "Activation functions": [[14, "activation-functions"]], "Adaptive boosting: AdaBoost, Basic Algorithm": [[12, "adaptive-boosting-adaboost-basic-algorithm"]], "Adding error analysis and training set up": [[23, "adding-error-analysis-and-training-set-up"]], "Adjust hyperparameters": [[3, "adjust-hyperparameters"]], "Algorithms for Setting up Decision Trees": [[11, "algorithms-for-setting-up-decision-trees"]], "An Overview of Ensemble Methods": [[12, "an-overview-of-ensemble-methods"]], "An extrapolation example": [[6, "an-extrapolation-example"]], "An optimization/minimization problem": [[23, "an-optimization-minimization-problem"]], "And what about using neural networks?": [[23, "and-what-about-using-neural-networks"]], "Another example, the moons again": [[11, "another-example-the-moons-again"]], "Applied Data Analysis and Machine Learning": [[17, null]], "Autocorrelation function": [[20, "autocorrelation-function"]], "Automatic differentiation": [[15, "automatic-differentiation"]], "Back to the Cancer Data": [[13, "back-to-the-cancer-data"]], "Bagging": [[12, "bagging"]], "Bagging Examples": [[12, "bagging-examples"]], "Basic Matrix Features": [[18, "basic-matrix-features"]], "Basic ideas of the Principal Component Analysis (PCA)": [[13, null]], "Basic math of the SVD": [[7, "basic-math-of-the-svd"]], "Basics": [[9, "basics"]], "Basics of a tree": [[11, "basics-of-a-tree"]], "Batch Normalization": [[3, "batch-normalization"]], "Bayes\u2019 Theorem and Ridge and Lasso Regression": [[7, "bayes-theorem-and-ridge-and-lasso-regression"]], "Boosting, a Bird\u2019s Eye View": [[12, "boosting-a-bird-s-eye-view"]], "Bootstrap": [[8, "bootstrap"]], "Bringing it together, first back propagation equation": [[14, "bringing-it-together-first-back-propagation-equation"]], "Building a Feed Forward Neural Network": [[3, null]], "Building a tree, regression": [[11, "building-a-tree-regression"]], "Building neural networks in Tensorflow and Keras": [[3, "building-neural-networks-in-tensorflow-and-keras"]], "CNNs in more detail, building convolutional neural networks in Tensorflow and Keras": [[5, "cnns-in-more-detail-building-convolutional-neural-networks-in-tensorflow-and-keras"]], "Cancer Data again now with Decision Trees and other Methods": [[11, "cancer-data-again-now-with-decision-trees-and-other-methods"]], "Choose cost function and optimizer": [[3, "choose-cost-function-and-optimizer"]], "Classical PCA Theorem": [[13, "classical-pca-theorem"]], "Clustering and Unsupervised Learning": [[16, null]], "Code for SVD and Inversion of Matrices": [[7, "code-for-svd-and-inversion-of-matrices"]], "Codes and Approaches": [[16, "codes-and-approaches"]], "Codes for the SVD": [[7, "codes-for-the-svd"]], "Coding Setup and Linear Regression": [[0, "coding-setup-and-linear-regression"]], "Collect and pre-process data": [[3, "collect-and-pre-process-data"]], "Communication channels": [[23, "communication-channels"]], "Compare  Bagging on Trees with Random Forests": [[12, "compare-bagging-on-trees-with-random-forests"]], "Comparing with a numerical scheme": [[4, "comparing-with-a-numerical-scheme"]], "Computing the Gini index": [[11, "computing-the-gini-index"]], "Conjugate gradient method": [[15, "conjugate-gradient-method"]], "Convex functions": [[15, "convex-functions"]], "Convolution Examples: Polynomial multiplication": [[5, "convolution-examples-polynomial-multiplication"]], "Convolution Examples: Principle of Superposition and Periodic Forces (Fourier Transforms)": [[5, "convolution-examples-principle-of-superposition-and-periodic-forces-fourier-transforms"]], "Convolutional Neural Network": [[14, "convolutional-neural-network"]], "Convolutional Neural Networks": [[5, null]], "Correlation Matrix": [[13, "correlation-matrix"]], "Course Format": [[23, "course-format"]], "Course setting": [[19, null]], "Cross-validation": [[8, "cross-validation"]], "Deadlines for projects (tentative)": [[23, "deadlines-for-projects-tentative"]], "Decision trees, overarching aims": [[11, null]], "Deep learning methods": [[23, "deep-learning-methods"]], "Define model and architecture": [[3, "define-model-and-architecture"]], "Defining the cost function": [[3, "defining-the-cost-function"]], "Deliverables": [[0, "deliverables"], [1, "deliverables"]], "Derivatives and the chain rule": [[14, "derivatives-and-the-chain-rule"]], "Deriving OLS from a probability distribution": [[7, "deriving-ols-from-a-probability-distribution"]], "Deriving and Implementing Ordinary Least Squares": [[1, "deriving-and-implementing-ordinary-least-squares"]], "Deriving the back propagation code for a multilayer perceptron model": [[14, "deriving-the-back-propagation-code-for-a-multilayer-perceptron-model"]], "Developing a code for doing neural networks with back propagation": [[3, "developing-a-code-for-doing-neural-networks-with-back-propagation"]], "Diagonalize the sample covariance matrix to obtain the principal components": [[13, "diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components"]], "Different kernels and Mercer\u2019s theorem": [[10, "different-kernels-and-mercer-s-theorem"]], "Disadvantages": [[11, "disadvantages"]], "Domains and probabilities": [[20, "domains-and-probabilities"]], "Dropout": [[3, "dropout"]], "Elements of Probability Theory and Statistical Data Analysis": [[20, null]], "Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods": [[12, null]], "Entropy and the ID3 algorithm": [[11, "entropy-and-the-id3-algorithm"]], "Essential elements of ML": [[23, "essential-elements-of-ml"]], "Evaluate model performance on test data": [[3, "evaluate-model-performance-on-test-data"]], "Example: Exponential decay": [[4, "example-exponential-decay"]], "Example: Population growth": [[4, "example-population-growth"]], "Example: The diffusion equation": [[4, "example-the-diffusion-equation"]], "Example: binary classification problem": [[3, "example-binary-classification-problem"]], "Examples": [[23, "examples"]], "Examples of likelihood functions used in logistic regression and neural networks": [[9, "examples-of-likelihood-functions-used-in-logistic-regression-and-neural-networks"]], "Exercise 1 - Finding the derivative of Matrix-Vector expressions": [[1, "exercise-1-finding-the-derivative-of-matrix-vector-expressions"]], "Exercise 1 - Github Setup": [[0, "exercise-1-github-setup"]], "Exercise 1: Setting up various Python environments": [[2, "exercise-1-setting-up-various-python-environments"], [23, "exercise-1-setting-up-various-python-environments"]], "Exercise 2 - Deriving the expression for OLS": [[1, "exercise-2-deriving-the-expression-for-ols"]], "Exercise 2 - Setting up a Github repository": [[0, "exercise-2-setting-up-a-github-repository"]], "Exercise 2: making your own data and exploring scikit-learn": [[2, "exercise-2-making-your-own-data-and-exploring-scikit-learn"], [23, "exercise-2-making-your-own-data-and-exploring-scikit-learn"]], "Exercise 3 - Creating feature matrix and implementing OLS using the analytical expression": [[1, "exercise-3-creating-feature-matrix-and-implementing-ols-using-the-analytical-expression"]], "Exercise 3 - Fitting an OLS model to data": [[0, "exercise-3-fitting-an-ols-model-to-data"]], "Exercise 3 - Setting up a Python virtual environment": [[0, "exercise-3-setting-up-a-python-virtual-environment"]], "Exercise 3: Normalizing our data": [[2, "exercise-3-normalizing-our-data"]], "Exercise 3: Split data in test and training data": [[23, "exercise-3-split-data-in-test-and-training-data"]], "Exercise 4 - Fitting a polynomial": [[1, "exercise-4-fitting-a-polynomial"]], "Exercise 4 - The train-test split": [[0, "exercise-4-the-train-test-split"]], "Exercise 4: Adding Ridge Regression": [[2, "exercise-4-adding-ridge-regression"]], "Exercise 5 - Comparing your code with sklearn": [[1, "exercise-5-comparing-your-code-with-sklearn"]], "Exercise 5: Analytical exercises": [[2, "exercise-5-analytical-exercises"]], "Exercise:  Cross-validation as resampling techniques, adding more complexity": [[8, "exercise-cross-validation-as-resampling-techniques-adding-more-complexity"]], "Exercise: Analysis of real data": [[8, "exercise-analysis-of-real-data"]], "Exercise: Bias-variance trade-off and resampling techniques": [[8, "exercise-bias-variance-trade-off-and-resampling-techniques"]], "Exercise: Lasso Regression on the Franke function  with resampling": [[8, "exercise-lasso-regression-on-the-franke-function-with-resampling"]], "Exercise: Ordinary Least Square (OLS) on the Franke function": [[8, "exercise-ordinary-least-square-ols-on-the-franke-function"]], "Exercise: Ridge Regression on the Franke function  with resampling": [[8, "exercise-ridge-regression-on-the-franke-function-with-resampling"]], "Exercises": [[2, "exercises"], [23, "exercises"]], "Exercises and Projects": [[8, "exercises-and-projects"]], "Exercises week 34": [[0, null]], "Exercises week 35": [[1, null]], "Expectation values": [[20, "expectation-values"]], "Extremely useful tools, strongly recommended": [[23, "extremely-useful-tools-strongly-recommended"]], "Feed-forward neural networks": [[14, "feed-forward-neural-networks"]], "Feed-forward pass": [[3, "feed-forward-pass"]], "Final back propagating equation": [[14, "final-back-propagating-equation"]], "Fine-tuning neural network hyperparameters": [[3, "fine-tuning-neural-network-hyperparameters"]], "Fitting an Equation of State for Dense Nuclear Matter": [[2, "fitting-an-equation-of-state-for-dense-nuclear-matter"], [23, "fitting-an-equation-of-state-for-dense-nuclear-matter"]], "From one to many layers, the universal approximation theorem": [[14, "from-one-to-many-layers-the-universal-approximation-theorem"]], "Further Dimensionality Remarks": [[5, "further-dimensionality-remarks"]], "Further properties (important for our analyses later)": [[7, "further-properties-important-for-our-analyses-later"]], "Gaussian Elimination": [[18, "gaussian-elimination"]], "General Features": [[11, "general-features"]], "General linear models and linear algebra": [[23, "general-linear-models-and-linear-algebra"]], "Generalizing the fitting procedure as a linear algebra problem": [[23, "generalizing-the-fitting-procedure-as-a-linear-algebra-problem"], [23, "id1"]], "Generative Adversarial Networks": [[6, "generative-adversarial-networks"]], "Generative Models": [[6, "generative-models"]], "Geometric Interpretation and link with Singular Value Decomposition": [[13, "geometric-interpretation-and-link-with-singular-value-decomposition"]], "Gradient Boosting, Classification Example": [[12, "gradient-boosting-classification-example"]], "Gradient Boosting, Examples of Regression": [[12, "gradient-boosting-examples-of-regression"]], "Gradient Clipping": [[3, "gradient-clipping"]], "Gradient boosting: Basics with Steepest Descent/Functional Gradient Descent": [[12, "gradient-boosting-basics-with-steepest-descent-functional-gradient-descent"]], "Gradient descent": [[4, "gradient-descent"]], "Grading": [[21, "grading"], [21, "id2"], [23, "grading"]], "Housing data, the code": [[2, "housing-data-the-code"]], "How to take derivatives of Matrix-Vector expressions": [[1, "how-to-take-derivatives-of-matrix-vector-expressions"]], "Hyperplanes and all that": [[10, "hyperplanes-and-all-that"]], "Important Matrix and vector handling packages": [[18, "important-matrix-and-vector-handling-packages"]], "Improving performance": [[3, "improving-performance"]], "In summary": [[21, "in-summary"]], "Including Stochastic Gradient Descent with Autograd": [[15, "including-stochastic-gradient-descent-with-autograd"]], "Incremental PCA": [[13, "incremental-pca"]], "Installing R, C++, cython or Julia": [[23, "installing-r-c-cython-or-julia"]], "Installing R, C++, cython, Numba etc": [[23, "installing-r-c-cython-numba-etc"]], "Instructor information": [[21, "instructor-information"]], "Interpretations and optimizing our parameters": [[23, "interpretations-and-optimizing-our-parameters"], [23, "id2"], [23, "id3"]], "Introducing JAX": [[15, "introducing-jax"]], "Introducing the Covariance and Correlation functions": [[13, "introducing-the-covariance-and-correlation-functions"]], "Introduction": [[2, "introduction"], [8, "introduction"], [17, "introduction"], [18, "introduction"]], "Iterative Fitting, Classification and AdaBoost": [[12, "iterative-fitting-classification-and-adaboost"]], "Iterative Fitting, Regression and Squared-error Cost Function": [[12, "iterative-fitting-regression-and-squared-error-cost-function"]], "Kernel PCA": [[13, "kernel-pca"]], "Kernels and non-linearity": [[10, "kernels-and-non-linearity"]], "LU Decomposition, the inverse of a matrix": [[18, "lu-decomposition-the-inverse-of-a-matrix"]], "Layers": [[3, "layers"]], "Layers used to build CNNs": [[5, "layers-used-to-build-cnns"]], "Learning goals": [[0, "learning-goals"], [1, "learning-goals"]], "Learning outcomes": [[17, "learning-outcomes"], [23, "learning-outcomes"]], "Lectures and ComputerLab": [[23, "lectures-and-computerlab"]], "Limitations of supervised learning with deep networks": [[3, "limitations-of-supervised-learning-with-deep-networks"]], "Linear Algebra, Handling of Arrays and more Python Features": [[18, null]], "Linear Regression": [[2, null]], "Linear Regression, basic elements": [[2, "linear-regression-basic-elements"]], "Linking Bayes\u2019 Theorem with Ridge and Lasso Regression": [[7, "linking-bayes-theorem-with-ridge-and-lasso-regression"]], "Linking the regression analysis with a statistical interpretation": [[7, "linking-the-regression-analysis-with-a-statistical-interpretation"]], "Linking with the SVD": [[7, "linking-with-the-svd"]], "Links to relevant courses at the University of Oslo": [[22, "links-to-relevant-courses-at-the-university-of-oslo"]], "Logistic Regression": [[9, null], [9, "id1"]], "MNIST and GANs": [[6, "mnist-and-gans"]], "Machine Learning": [[23, "machine-learning"]], "Machine learning": [[17, "machine-learning"]], "Main textbooks": [[23, "main-textbooks"]], "Making a tree": [[11, "making-a-tree"]], "Making your own Bootstrap: Changing the Level of the Decision Tree": [[12, "making-your-own-bootstrap-changing-the-level-of-the-decision-tree"]], "Mathematical Interpretation of Ordinary Least Squares": [[7, "mathematical-interpretation-of-ordinary-least-squares"]], "Mathematical optimization of convex functions": [[10, "mathematical-optimization-of-convex-functions"]], "Mathematics of CNNs": [[5, "mathematics-of-cnns"]], "Mathematics of the SVD and implications": [[7, "mathematics-of-the-svd-and-implications"]], "Matrices in Python": [[23, "matrices-in-python"]], "Matrix  multiplication": [[3, "matrix-multiplication"]], "Matrix-vector notation  and activation": [[14, "matrix-vector-notation-and-activation"]], "Meet the  covariance!": [[20, "meet-the-covariance"]], "Meet the Covariance Matrix": [[7, "meet-the-covariance-matrix"]], "Meet the Pandas": [[23, "meet-the-pandas"]], "Momentum based GD": [[15, "momentum-based-gd"]], "More complicated Example: The Ising model": [[8, "more-complicated-example-the-ising-model"]], "More on Dimensionalities": [[5, "more-on-dimensionalities"]], "More on Rescaling data": [[8, "more-on-rescaling-data"]], "Multilayer perceptrons": [[14, "multilayer-perceptrons"]], "Network requirements": [[4, "network-requirements"]], "Neural Networks vs CNNs": [[5, "neural-networks-vs-cnns"]], "Neural networks": [[14, null]], "Numerical experiments and the covariance, central limit theorem": [[20, "numerical-experiments-and-the-covariance-central-limit-theorem"]], "Numpy and arrays": [[18, "numpy-and-arrays"], [23, "numpy-and-arrays"]], "Numpy examples and Important Matrix and vector handling packages": [[23, "numpy-examples-and-important-matrix-and-vector-handling-packages"]], "Optimization, the central part of any Machine Learning algortithm": [[15, null]], "Optimizing our parameters": [[23, "optimizing-our-parameters"]], "Optimizing our parameters, more details": [[23, "optimizing-our-parameters-more-details"]], "Optimizing the cost function": [[3, "optimizing-the-cost-function"]], "Organizing our data": [[2, "organizing-our-data"], [23, "organizing-our-data"]], "Other Matrix and Vector Operations": [[18, "other-matrix-and-vector-operations"]], "Other Types of Recurrent Neural Networks": [[6, "other-types-of-recurrent-neural-networks"]], "Other courses on Data science and Machine Learning  at UiO": [[23, "other-courses-on-data-science-and-machine-learning-at-uio"]], "Other courses on Data science and Machine Learning  at UiO, contn": [[23, "other-courses-on-data-science-and-machine-learning-at-uio-contn"]], "Other popular texts": [[23, "other-popular-texts"]], "Other techniques": [[13, "other-techniques"]], "Other types of networks": [[14, "other-types-of-networks"]], "Other ways of visualizing the trees": [[11, "other-ways-of-visualizing-the-trees"]], "Our model for the nuclear binding energies": [[23, "our-model-for-the-nuclear-binding-energies"]], "Overview of first week": [[23, "overview-of-first-week"]], "Own code for Ordinary Least Squares": [[23, "own-code-for-ordinary-least-squares"]], "PCA and scikit-learn": [[13, "pca-and-scikit-learn"]], "Pandas AI": [[23, "pandas-ai"]], "Partial Differential Equations": [[4, "partial-differential-equations"]], "Practical tips": [[15, "practical-tips"]], "Practicalities": [[21, "practicalities"], [21, "id1"]], "Predicting New Points With A Trained Recurrent Neural Network": [[6, "predicting-new-points-with-a-trained-recurrent-neural-network"]], "Prerequisites": [[23, "prerequisites"]], "Prerequisites and background": [[17, "prerequisites-and-background"]], "Prerequisites: Collect and pre-process data": [[5, "prerequisites-collect-and-pre-process-data"]], "Probability Distribution Functions": [[20, "probability-distribution-functions"]], "Program for stochastic gradient": [[15, "program-for-stochastic-gradient"]], "Properties of PDFs": [[20, "properties-of-pdfs"]], "Pros and cons of trees, pros": [[11, "pros-and-cons-of-trees-pros"]], "Python installers": [[17, "python-installers"], [23, "python-installers"]], "RMS prop": [[15, "rms-prop"]], "Random Numbers": [[20, "random-numbers"]], "Random forests": [[12, "random-forests"]], "Randomized PCA": [[13, "randomized-pca"]], "Reading material": [[23, "reading-material"]], "Reading suggestions week 34": [[23, "reading-suggestions-week-34"]], "Recurrent neural networks": [[14, "recurrent-neural-networks"]], "Recurrent neural networks: Overarching view": [[6, null]], "Reducing the number of degrees of freedom, overarching view": [[2, "reducing-the-number-of-degrees-of-freedom-overarching-view"]], "Reformulating the problem": [[4, "reformulating-the-problem"]], "Regression Case": [[12, "regression-case"]], "Regression analysis, overarching aims": [[23, "regression-analysis-overarching-aims"]], "Regression analysis, overarching aims II": [[23, "regression-analysis-overarching-aims-ii"]], "Regularization": [[3, "regularization"]], "Reminder on Statistics": [[8, "reminder-on-statistics"]], "Replace or not": [[15, "replace-or-not"]], "Required Technologies": [[17, "required-technologies"]], "Resampling Methods": [[8, null]], "Resampling methods": [[8, "id1"]], "Resources on differential equations and deep learning": [[4, "resources-on-differential-equations-and-deep-learning"]], "Revisiting our Linear Regression Solvers": [[15, "revisiting-our-linear-regression-solvers"]], "Rewriting the fitting procedure as a linear algebra problem": [[23, "rewriting-the-fitting-procedure-as-a-linear-algebra-problem"]], "Rewriting the fitting procedure as a linear algebra problem, more details": [[23, "rewriting-the-fitting-procedure-as-a-linear-algebra-problem-more-details"]], "Ridge and Lasso Regression": [[7, null], [7, "id1"]], "Same code but now with momentum gradient descent": [[15, "same-code-but-now-with-momentum-gradient-descent"]], "Schedule first week": [[23, "schedule-first-week"]], "Schematic Regression Procedure": [[11, "schematic-regression-procedure"]], "Setting up the Back propagation algorithm": [[14, "setting-up-the-back-propagation-algorithm"]], "Setting up the network using Autograd; The full program": [[4, "setting-up-the-network-using-autograd-the-full-program"]], "Similar (second order function now) problem but now with AdaGrad": [[15, "similar-second-order-function-now-problem-but-now-with-adagrad"]], "Simple Python Code to read in Data and perform Classification": [[11, "simple-python-code-to-read-in-data-and-perform-classification"]], "Simple linear regression model using scikit-learn": [[2, "simple-linear-regression-model-using-scikit-learn"], [23, "simple-linear-regression-model-using-scikit-learn"]], "Software and needed installations": [[23, "software-and-needed-installations"]], "Solving Differential Equations  with Deep Learning": [[4, null]], "Solving the one dimensional Poisson equation": [[4, "solving-the-one-dimensional-poisson-equation"]], "Solving the wave equation with Neural Networks": [[4, "solving-the-wave-equation-with-neural-networks"]], "Some famous Matrices": [[18, "some-famous-matrices"]], "Some simple problems": [[15, "some-simple-problems"]], "Splitting our Data in Training and Test data": [[2, "splitting-our-data-in-training-and-test-data"], [23, "splitting-our-data-in-training-and-test-data"]], "Standard steepest descent": [[15, "standard-steepest-descent"]], "Statistical analysis and optimization of data": [[17, "statistical-analysis-and-optimization-of-data"], [23, "statistical-analysis-and-optimization-of-data"]], "Steepest descent": [[15, "steepest-descent"]], "Stochastic Gradient Descent (SGD)": [[15, "stochastic-gradient-descent-sgd"]], "Stochastic variables and the main concepts, the discrete case": [[20, "stochastic-variables-and-the-main-concepts-the-discrete-case"]], "Support Vector Machines, overarching aims": [[10, null]], "Systematic reduction": [[5, "systematic-reduction"]], "Teachers": [[23, "teachers"]], "Teachers and Grading": [[21, null]], "Teaching Assistants Fall semester 2023": [[21, "teaching-assistants-fall-semester-2023"]], "Tentative deadllines for projects": [[21, "tentative-deadllines-for-projects"]], "Testing the Means Squared Error as function of Complexity": [[2, "testing-the-means-squared-error-as-function-of-complexity"]], "Textbooks": [[22, null]], "The Algorithm before theorem": [[13, "the-algorithm-before-theorem"]], "The Boston housing data example": [[2, "the-boston-housing-data-example"]], "The Breast Cancer Data, now with Keras": [[3, "the-breast-cancer-data-now-with-keras"]], "The CART algorithm for Classification": [[11, "the-cart-algorithm-for-classification"]], "The CART algorithm for Regression": [[11, "the-cart-algorithm-for-regression"]], "The CIFAR01 data set": [[5, "the-cifar01-data-set"]], "The MNIST dataset again": [[5, "the-mnist-dataset-again"]], "The RELU function family": [[3, "the-relu-function-family"]], "The Softmax function": [[3, "the-softmax-function"]], "The \\chi^2 function": [[2, "the-chi-2-function"], [23, "the-chi-2-function"], [23, "id4"], [23, "id5"], [23, "id6"], [23, "id7"], [23, "id8"]], "The bias-variance tradeoff": [[8, "the-bias-variance-tradeoff"]], "The code": [[23, "the-code"]], "The code for solving the ODE": [[4, "the-code-for-solving-the-ode"]], "The course has two central parts": [[17, "the-course-has-two-central-parts"]], "The logistic function": [[9, "the-logistic-function"]], "The moons example": [[10, "the-moons-example"]], "The multilayer  perceptron (MLP)": [[14, "the-multilayer-perceptron-mlp"]], "The network with one input layer, specified number of hidden layers, and one output layer": [[4, "the-network-with-one-input-layer-specified-number-of-hidden-layers-and-one-output-layer"]], "The singular value decomposition": [[7, "the-singular-value-decomposition"]], "The two-dimensional case": [[10, "the-two-dimensional-case"]], "To our real data: nuclear binding energies. Brief reminder on masses and binding energies": [[23, "to-our-real-data-nuclear-binding-energies-brief-reminder-on-masses-and-binding-energies"]], "Topics covered in this course: Statistical analysis and optimization of data": [[23, "topics-covered-in-this-course-statistical-analysis-and-optimization-of-data"]], "Towards the PCA theorem": [[13, "towards-the-pca-theorem"]], "Train and test datasets": [[3, "train-and-test-datasets"]], "Two-dimensional Objects": [[5, "two-dimensional-objects"]], "Type of problem": [[4, "type-of-problem"]], "Types of Machine Learning": [[23, "types-of-machine-learning"]], "Useful Python libraries": [[17, "useful-python-libraries"], [23, "useful-python-libraries"]], "Using Autograd": [[15, "using-autograd"]], "Using forward Euler to solve the ODE": [[4, "using-forward-euler-to-solve-the-ode"]], "Using gradient descent methods, limitations": [[15, "using-gradient-descent-methods-limitations"]], "Visualization": [[3, "visualization"], [3, "id1"]], "Visualizing the Tree, Classification": [[11, "visualizing-the-tree-classification"]], "Week 34: Introduction to the course, Logistics and Practicalities": [[23, null]], "What is Machine Learning?": [[2, "what-is-machine-learning"]], "What is a good model?": [[2, "what-is-a-good-model"], [23, "what-is-a-good-model"]], "What is a good model? Can we define it?": [[23, "what-is-a-good-model-can-we-define-it"]], "Which activation function should I use?": [[3, "which-activation-function-should-i-use"]], "Why Linear Regression (aka Ordinary Least Squares and family)": [[23, "why-linear-regression-aka-ordinary-least-squares-and-family"]], "Wisconsin Cancer Data": [[9, "wisconsin-cancer-data"]], "Writing Our First Generative Adversarial Network": [[6, "writing-our-first-generative-adversarial-network"]], "Writing our own PCA code": [[13, "writing-our-own-pca-code"]], "XGBoost: Extreme Gradient Boosting": [[12, "xgboost-extreme-gradient-boosting"]], "scikit-learn implementation": [[3, "scikit-learn-implementation"]]}, "docnames": ["E1", "E2", "chapter1", "chapter10", "chapter11", "chapter12", "chapter13", "chapter2", "chapter3", "chapter4", "chapter5", "chapter6", "chapter7", "chapter8", "chapter9", "chapteroptimization", "clustering", "intro", "linalg", "schedule", "statistics", "teachers", "textbooks", "week34"], "envversion": {"sphinx": 62, "sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.intersphinx": 1}, "filenames": ["E1.ipynb", "E2.ipynb", "chapter1.ipynb", "chapter10.ipynb", "chapter11.ipynb", "chapter12.ipynb", "chapter13.ipynb", "chapter2.ipynb", "chapter3.ipynb", "chapter4.ipynb", "chapter5.ipynb", "chapter6.ipynb", "chapter7.ipynb", "chapter8.ipynb", "chapter9.ipynb", "chapteroptimization.ipynb", "clustering.ipynb", "intro.md", "linalg.ipynb", "schedule.md", "statistics.ipynb", "teachers.md", "textbooks.md", "week34.ipynb"], "indexentries": {}, "objects": {}, "objnames": {}, "objtypes": {}, "terms": {"": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 13, 14, 15, 17, 18, 20, 21, 23], "0": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 21, 23], "00": [2, 3, 7, 13, 23], "000": [3, 5], "001": [4, 10, 15], "004": 7, "00727646693": [2, 23], "0086649156": [2, 23], "01": [2, 3, 4, 7, 11, 13, 15, 22, 23], "0110": 20, "02": [2, 6, 9, 14, 23], "02857": 6, "02f": 8, "03077640549": 6, "031": 7, "04": 13, "0458": 11, "05": [6, 8], "062292565": 6, "0713": [2, 23], "07285": 5, "08": 20, "08336233266": 6, "0917": 11, "0n": [2, 23], "1": [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 20, 21, 22], "10": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 19, 20, 21, 23], "100": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 15, 16, 18, 20, 21, 23], "1000": [2, 3, 4, 6, 7, 10, 13, 15, 16, 17, 20, 23], "10000": [4, 7, 8, 12, 13, 15, 20], "100000": 10, "10001": 12, "1001": 20, "1002": 20, "1003": 20, "1005": 20, "1009": 20, "101": 1, "1011": 20, "1013": 20, "1013904243": 20, "1015": 20, "102": 1, "1023": 20, "1024": 5, "1026": 20, "1027": 20, "103": 3, "1030": 20, "1037": 20, "1038": 20, "1040": 20, "1047": 20, "107": 1, "10th": 11, "10x": [2, 23], "11": [1, 2, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 22, 23], "1100": 20, "1101": 20, "111": [3, 9, 14], "112": 1, "116": 1, "117": 1, "118": 1, "12": [2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 14, 18, 20, 22, 23], "120": 5, "121": [1, 10, 11, 12], "1215pm": [21, 23], "122": [10, 11, 12], "124": [2, 23], "125": 1, "127": [1, 6], "128": [5, 6, 15], "129": 1, "1298": 11, "12pm": [21, 23], "13": [2, 4, 11, 14, 18, 20, 23], "131": 1, "133": 9, "135": 1, "136": 1, "14": [2, 4, 6, 8, 10, 11, 12, 14, 18, 20, 22], "141": 1, "143": 1, "1446729567": 6, "149": 1, "14g": 8, "15": [2, 4, 6, 8, 9, 10, 11, 14, 15, 20, 23], "150": [6, 10], "152": 1, "156": 1, "159": 1, "15g": 8, "15pm": 23, "16": [3, 4, 5, 6, 7, 10, 11, 12, 20, 23], "160": 1, "1603": 5, "161": 1, "162": 1, "16231451": 6, "163": 1, "16384": 5, "164": 1, "167": 1, "17": [3, 4, 10, 20], "172": 1, "173": 1, "176": 1, "178": 1, "179": 1, "1797": 3, "18": [4, 8, 9, 10, 11, 12, 20, 23], "1807": 6, "19": [4, 20, 23], "1940": 2, "1943": 14, "1970": [18, 23], "1973": 11, "1979": 8, "1_1": 14, "1_2": 14, "1_3": 14, "1cm": [2, 10, 12, 20, 23], "1d": [3, 4, 5], "1e": [4, 6, 15, 16], "1e10": 16, "1e4": 8, "1f": 3, "1k": 18, "1n": [2, 23], "1x": [2, 23], "2": [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22], "20": [1, 2, 3, 4, 8, 9, 10, 20, 21, 23], "200": [2, 4, 5, 6, 10, 11, 12], "2000": 2, "2004": 15, "2006": 22, "2008": 23, "2010": 3, "2011": 3, "2014": 6, "2015": 3, "2016": [2, 23], "2018": [2, 8], "2021": [8, 16], "2022": 23, "2025": 23, "21": [2, 3, 7, 9, 11, 14, 18, 23], "2116753732": 6, "215pm": [21, 23], "22": [2, 3, 7, 14, 15, 18, 23], "221": 10, "225": 6, "23": [3, 14, 18], "24": [2, 3, 18, 23], "25": [4, 5, 6, 7, 8, 10, 11, 13], "250": [4, 6, 9, 11], "25000": 2, "255": 5, "256": 6, "265109911": 6, "27": [2, 3], "27n_": 20, "28": [3, 5, 6], "2830637392": 6, "2861": 20, "2873": 11, "2882": 20, "2886": 20, "2890": [2, 23], "2892": 20, "2915": 20, "2931": 23, "2968": 23, "2980": 23, "2990": 23, "2_": 14, "2_1": 14, "2_2": 14, "2_3": 14, "2_i": 14, "2_m": [8, 20], "2_t": 15, "2_x": 20, "2b": 20, "2cm": 10, "2d": [3, 5, 13, 14, 17, 23], "2e": 8, "2f": [2, 9, 11, 12, 13, 14, 23], "2g": 4, "2g_i": 4, "2k": 5, "2m": 8, "2n": [2, 4, 5, 23], "2nd": 11, "2p": 20, "2pt": 6, "2x": [2, 5, 10, 15, 23], "2x_ix_jy_iy_j": 10, "2x_j": 10, "2y_i": 12, "2y_j": 10, "3": [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21], "30": [2, 3, 6, 8, 9, 12, 15, 21], "30000": [2, 23], "3072": 5, "31": [14, 18, 20], "315": 8, "3155": [2, 7, 8], "32": [5, 6, 8, 14, 15, 18, 20], "3200": 3, "3250": 3, "33": [14, 18, 21], "333": 9, "34": 18, "3436": [2, 23], "3437": [2, 23], "35": [2, 8, 23], "3581341341": 6, "359": 7, "36": [2, 7, 8, 20], "370782966": 6, "38": 20, "39": [2, 21, 23], "3d": [1, 4, 5, 6, 8, 15], "3f": [3, 5, 11], "3n": 18, "3x": [4, 10], "3x_i": 4, "3y": 10, "4": [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "40": [3, 8, 21, 23], "400": 6, "4000": 23, "4050": [22, 23], "41": 18, "4155": [0, 4], "42": [3, 6, 10, 11, 12, 18], "43": [2, 9, 18], "4310": 23, "436462435": 6, "44": [2, 18], "45": [21, 23], "46": [21, 23], "462": 9, "47": [21, 23], "479465113": 6, "48257387": [21, 23], "49": [7, 8, 13], "49152": 5, "4940954": [2, 23], "4990": 20, "4992": 20, "4997": 20, "4c4c7f": [11, 12], "4d": 5, "4f": 8, "4pm": [21, 23], "4y": 10, "4y_i": 12, "5": [0, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "50": [3, 4, 5, 6, 8, 9, 10, 12, 15, 23], "500": [3, 5, 6, 8, 11, 12, 15], "5018": 20, "506": 2, "507d50": [11, 12], "50j": 15, "50x10": 3, "51": 12, "510": 3, "5177783846": 6, "53": 11, "54": [8, 20], "55": 3, "56": 3, "56536": [2, 23], "569": 3, "57": [2, 10, 21, 23], "571": 7, "58": [12, 21, 23], "591317992": 6, "5cm": 20, "5f": 10, "5x": 10, "5y": 10, "6": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 18, 20, 21, 23], "60": [3, 5], "60000": 6, "6019067271": 6, "625": 9, "63": [2, 3], "64": [3, 5, 6, 15, 18, 23], "64x50": 3, "65": [3, 10, 11], "6887363571": 6, "69": [1, 20], "69069n_": 20, "6n_": 20, "6pm": 23, "7": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 18, 20, 22, 23], "70": [3, 9], "70653767": 6, "71": 3, "724": 5, "75": [7, 8, 10, 13], "76": [21, 23], "765": 9, "77": [21, 23], "7718": 11, "7782028952": 6, "7d7d58": [11, 12], "8": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 18, 20, 21, 23], "80": [2, 3, 7, 10], "800": [6, 9], "81": 3, "815am": [21, 23], "85": 3, "8702784034": 6, "88": 23, "8f": 8, "8g": 8, "8n": 18, "8x8": 3, "9": [2, 3, 4, 6, 7, 8, 9, 10, 11, 13, 14, 15, 18, 20, 23], "90": 3, "9040": 11, "91": [21, 23], "92": [21, 23], "93": 1, "931": [2, 23], "933": 7, "937": 20, "938": 20, "939": [2, 20, 23], "94": 20, "95": [3, 13], "954": 20, "955820c21e8b": 6, "96": 8, "960": 20, "961": 20, "962": 20, "9649652536": 6, "9780387310732": 22, "9780387848570": 22, "9781098134174": 23, "9781492032632": 22, "9781801819312": 23, "98": [1, 2, 3], "985": 20, "986": 20, "989": 20, "9898ff": [11, 12], "99": [1, 15], "991": 20, "992": 20, "993": 20, "996": 7, "999": [11, 20], "9x": 8, "9y": 8, "A": [0, 1, 4, 5, 7, 8, 9, 12, 13, 14, 15, 17, 18, 19, 20, 21, 22], "AND": 4, "And": [2, 5, 6, 7, 8, 11, 15, 17, 20], "As": [1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 14, 15, 18, 20, 23], "At": [2, 6, 8, 15, 23], "BE": [2, 23], "Be": [4, 17, 23], "Being": 15, "But": [1, 2, 3, 4, 5, 7, 8, 11, 12, 20], "By": [2, 5, 7, 8, 14, 15, 18, 23], "For": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22, 23], "IF": 8, "IN": 22, "If": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 20, 23], "In": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22, 23], "Ising": [7, 14], "It": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "Its": [3, 4, 6, 13], "No": [8, 11, 23], "Not": [2, 3, 7, 8], "OR": 20, "Of": 20, "On": [2, 5, 20, 21, 22, 23], "One": [2, 3, 5, 6, 7, 8, 9, 10, 13, 14, 15, 20], "Or": [2, 3, 8, 23], "Such": [1, 2, 8, 14, 20], "That": [2, 7, 9, 12, 13, 14, 16, 20, 23], "The": [1, 6, 12, 15, 16, 18, 19, 20, 21, 22], "Then": [0, 1, 2, 3, 8, 10, 11, 12, 13, 14, 15, 16, 18, 23], "There": [0, 2, 5, 6, 7, 8, 10, 11, 13, 14, 16, 18, 20, 21, 23], "These": [2, 5, 6, 7, 10, 11, 12, 13, 14, 15, 16, 18, 20, 21, 23], "To": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20], "With": [1, 2, 7, 8, 10, 11, 12, 13, 14, 16, 18, 20, 23], "_": [1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 23], "_0": [7, 10, 12, 13, 15], "_1": [4, 7, 8, 10, 12, 13, 14, 15, 16, 18], "_2": [4, 7, 10, 13, 14, 15, 18], "_3": 18, "_4": 18, "_9": 15, "__class__": 12, "__doc__": 8, "__future__": [10, 11], "__init__": 3, "__main__": 4, "__name__": [4, 12], "_auto1": [4, 5, 6, 7, 8, 9, 14, 15, 18, 20], "_auto10": [8, 14], "_auto11": 8, "_auto12": 8, "_auto2": [4, 5, 6, 7, 8, 14, 15, 18, 20], "_auto3": [5, 6, 7, 8, 14, 15, 18], "_auto4": [6, 8, 14, 15, 18], "_auto5": [6, 8, 14, 15, 18], "_auto6": [6, 8, 14, 18], "_auto7": [6, 8, 14, 18], "_auto8": [8, 14], "_auto9": [8, 14], "_build": [2, 17, 22, 23], "_c": 3, "_compon": 13, "_depth": 11, "_export": [0, 1], "_fraction": 11, "_i": [2, 3, 4, 7, 8, 9, 10, 13, 14, 15, 23], "_j": [2, 3, 4, 5, 7, 8, 10, 15], "_k": 15, "_l": 14, "_lambda": [8, 23], "_leaf": 11, "_m": 12, "_n": [4, 7, 10, 13, 15], "_node": 11, "_p": [7, 10], "_ratio": 13, "_sampl": 11, "_split": [8, 11], "_t": 15, "_test": 8, "_varianc": 13, "_weight": 11, "a0": 5, "a0faa0": [11, 12], "a1": [2, 23], "a2": [2, 23], "a3": [2, 23], "a4": [2, 23], "a_": [1, 2, 3, 18, 23], "a_0": [2, 23], "a_1a": [2, 23], "a_2a": [2, 23], "a_3": [2, 23], "a_3a": [2, 23], "a_4": [2, 23], "a_4a": [2, 23], "a_h": 3, "a_i": [2, 3, 4, 14, 23], "a_j": [3, 14], "a_k": [2, 3, 14], "aaron": 22, "ab": [2, 4, 7, 15, 16, 23], "ab_channel": 17, "abandon": 3, "abid": 20, "abil": [2, 12], "abl": [1, 2, 3, 6, 7, 8, 9, 12, 14, 15], "about": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 21], "abov": [1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 22, 23], "abovement": [8, 23], "abscissa": 15, "absolut": [2, 4, 7, 8, 15, 23], "abstract": 3, "acceler": 15, "accept": [2, 5, 8, 11, 23], "access": [2, 5, 13, 20, 23], "accid": [6, 8], "accompani": [2, 23], "accomplish": [10, 11, 15], "accord": [2, 3, 4, 7, 8, 11, 14, 15, 16, 20, 23], "accordingli": 13, "account": [0, 1, 2, 5, 7, 15, 20, 23], "accumul": [14, 15, 20], "accur": [2, 5, 6, 8, 12, 15], "accuraci": [2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 14, 23], "accuracy_scor": [2, 3, 12, 23], "accuracy_score_numpi": 3, "achiev": [2, 3, 7, 8, 10, 14, 18, 23], "aco": 20, "acquaint": 17, "acquir": [3, 17, 23], "acr": 2, "across": [3, 5, 8, 11, 17, 23], "act": [3, 5, 18], "action": 20, "activ": [0, 2, 4, 5, 6, 11, 19, 21, 23], "actual": [0, 1, 2, 3, 6, 7, 8, 10, 13, 18, 20, 23], "ad": [0, 1, 3, 5, 6, 7, 10, 15, 18], "ada_clf": 12, "adaboostclassifi": 12, "adadelta": 15, "adam": [3, 5, 6, 23], "adapt": [6, 8, 15, 22], "add": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 13, 14, 20, 21, 23], "add_subplot": [3, 9, 14, 16], "addendum": 7, "addit": [2, 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 17, 18, 20, 21, 22, 23], "addition": [14, 15], "address": [3, 11, 13, 15, 23], "adjac": [5, 14], "adjoint": 7, "adjust": [2, 7, 14, 15], "admir": [2, 23], "advanc": [6, 8, 14, 22, 23], "advantag": [3, 5, 7, 8, 12, 15, 18], "adversari": 23, "afecionado": 23, "affect": [0, 5], "affin": [2, 5, 10, 13], "afford": 5, "aficionado": 23, "aforement": 16, "african": 2, "after": [0, 1, 2, 3, 4, 6, 7, 8, 11, 13, 14, 15, 17, 18, 20, 23], "afterward": [2, 23], "ag": [2, 9, 23], "ag_0": 4, "again": [2, 3, 6, 7, 8, 9, 10, 12, 13, 14, 15, 20, 23], "against": [3, 6, 9, 12], "agegroup": 9, "agegroupmean": 9, "aggreg": [11, 12], "agorithm": 12, "agre": [7, 8, 20], "agreement": 15, "ahead": 11, "ai": [2, 22], "aid": 13, "aim": [1, 2, 3, 6, 8, 9, 13, 16, 17, 18], "ainv": 7, "airplan": 5, "aka": 7, "al": [1, 2, 4, 6, 22, 23], "alarm": [7, 9], "algebra": [2, 5, 7, 15, 17], "algorithm": [1, 2, 3, 4, 6, 7, 8, 9, 10, 15, 16, 17, 18, 20, 22, 23], "align": [2, 4, 7, 8, 9, 10, 15, 20, 23], "all": [0, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], "allevi": [3, 15], "alloc": [5, 18], "allow": [2, 3, 4, 5, 7, 8, 10, 12, 15, 17, 18, 23], "almost": [2, 3, 8, 10, 13, 15, 20], "alon": [4, 11], "along": [0, 4, 5, 6, 7, 8, 11, 12, 13, 17, 18, 23], "alpha": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 15, 16, 20, 23], "alpha_": 12, "alpha_0": 5, "alpha_1": 5, "alpha_2": 5, "alpha_i": [5, 15], "alpha_k": 15, "alpha_m": 12, "alpha_n": 5, "alpha_opt": 15, "alreadi": [0, 4, 5, 6, 7, 8, 12, 14, 17, 18, 20, 23], "also": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "alter": 3, "altern": [2, 3, 6, 7, 8, 10, 11, 13, 15, 18, 23], "although": [1, 2, 3, 7, 8, 10, 12, 15, 23], "alwai": [1, 2, 5, 7, 8, 14, 15, 20, 23], "am": 6, "ame2016": [2, 23], "american": 2, "among": [2, 5, 7, 11, 12, 14, 18, 23], "amongst": 7, "amount": [2, 3, 5, 6, 8, 10, 12, 16, 17], "an": [1, 3, 4, 5, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 20, 21, 22], "an_": 20, "anaconda": [2, 3, 17, 23], "analogi": 15, "analys": 8, "analysi": [3, 5, 6, 9, 16, 18, 22], "analyt": [4, 5, 7, 8, 9, 14, 15, 17, 23], "analyz": [1, 2, 3, 5, 6, 7, 8, 20], "andrew": 3, "angl": [2, 5, 11], "anharmon": 5, "ani": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 16, 20, 23], "anim": [6, 14], "ann": 14, "annot": [2, 3, 5, 9, 10, 23], "announc": 23, "anoth": [0, 2, 3, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 18, 20, 23], "ansatz": [2, 23], "answer": [2, 3, 5, 7, 8, 18, 21, 23], "antialias": [4, 8], "anticip": 6, "anymor": [3, 10], "anyon": [0, 6, 10], "anyth": [0, 1, 3, 20], "anytim": [21, 23], "apach": 3, "apart": [13, 15], "api": [3, 17, 23], "appar": 4, "appear": [2, 3, 5, 15, 18, 20], "append": [3, 5, 6, 10, 11, 15, 23], "appli": [2, 3, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 20, 22, 23], "applic": [1, 2, 3, 5, 6, 7, 8, 9, 11, 14, 15, 18, 20, 22, 23], "apply_gradi": 6, "approach": [0, 1, 3, 4, 6, 7, 8, 11, 12, 13, 14, 15, 17, 20, 22], "appropri": [4, 8, 11, 14, 15, 17, 20], "approv": 23, "approx": [2, 4, 5, 8, 12, 13, 15, 20, 23], "approxim": [2, 3, 4, 5, 6, 7, 8, 9, 12, 13, 15, 20, 23], "apt": [2, 17, 23], "aq": 20, "ar": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], "aragorn": 23, "arang": [3, 5, 6, 8, 9, 11, 12, 14, 15, 23], "arbitrari": [3, 6, 8, 10, 14, 15, 20], "arbitrarili": [2, 3, 13, 23], "arc": 8, "architectur": [5, 6, 14], "area": [2, 5, 8, 22, 23], "argmax": [3, 13], "argmin": [6, 12, 16], "argsort": 13, "argu": [3, 15], "argument": [2, 4, 5, 7, 13, 14, 15, 23], "aris": [2, 8, 14, 15, 20, 23], "arithmet": [2, 15, 18, 23], "arm": 8, "armadillo": 18, "around": [2, 3, 6, 7, 8, 13, 20, 23], "arrai": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 20], "arrang": [5, 23], "arraybox": 15, "arriv": [2, 8, 11, 13, 18, 20, 23], "arrow": 14, "arrowprop": 10, "art": [2, 3, 17, 23], "articl": [2, 5, 6, 8, 12, 23], "artifici": [2, 4, 9, 14, 22, 23], "artificialneuron": 14, "arug": 15, "arxiv": [5, 6], "asarrai": [2, 8, 11], "ask": [0, 7, 8, 13, 14], "aspect": [2, 8, 17, 23], "assembl": 5, "assembli": [2, 23], "assert": 6, "assess": [2, 8, 23], "assici": 6, "assign": [0, 2, 9, 10, 11, 14, 15, 16, 19, 21, 22, 23], "associ": [2, 8, 11, 14, 16, 20, 23], "assum": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "assumpt": [2, 5, 7, 8, 11, 13, 20, 23], "ast": [2, 7, 8, 23], "astronomi": 23, "astyp": [6, 11, 12], "asymmetri": [2, 23], "asymptot": [6, 8], "atom": [2, 23], "attempt": [2, 6, 8, 9, 10, 12, 23], "attend": 23, "attent": [2, 18, 23], "attract": [2, 12, 23], "attribut": [2, 11, 23], "audi": [2, 23], "audio": [5, 6], "august": 23, "aurelien": [2, 22, 23], "austfjel": 8, "auth": 0, "authent": 0, "author": [2, 3, 12, 20], "authour": 23, "auto": [11, 12, 20], "autocor": 20, "autocorrelation_tim": 20, "autocorrelform": 20, "autocovari": 20, "autoencod": [6, 17, 23], "autoencond": 17, "autograd": [17, 23], "autom": [2, 17, 22, 23], "automac": 18, "automag": 23, "automat": [1, 2, 3, 4, 5, 6, 13, 17, 18, 23], "automobil": 5, "autonom": 6, "avail": [2, 3, 6, 8, 12, 13, 17, 18, 19, 21, 22, 23], "averag": [2, 3, 5, 8, 11, 12, 15, 16, 20, 21, 23], "avoid": [2, 6, 7, 8, 11, 13, 15, 18, 23], "awai": [4, 5, 8], "awar": [4, 12], "award": [21, 23], "ax": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 23], "axes3d": [4, 8, 15], "axes_grid1": 8, "axhlin": 10, "axi": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "axiom": 7, "axlabel": [2, 23], "axvlin": [6, 10], "axvspan": 6, "b": [0, 1, 2, 3, 5, 6, 7, 8, 10, 11, 12, 14, 15, 16, 20, 21, 23], "b1": 10, "b2": 10, "b3": 10, "b_": [2, 3, 18], "b_0": 2, "b_1": [2, 4, 14, 15], "b_2": [2, 15], "b_5": 15, "b_group": 11, "b_i": [2, 3, 4, 14, 23], "b_ia_": [2, 23], "b_ia_i": 2, "b_index": 11, "b_j": [3, 14], "b_k": [2, 3, 14, 15], "b_m": 14, "b_score": 11, "b_valu": 11, "babcock": 23, "bachelor": [19, 21], "back": [0, 1, 2, 5, 6, 7, 8, 10, 11, 12, 18, 20, 23], "backbon": 18, "backend": [3, 6], "background": [22, 23], "backpropag": 3, "backtrack": 11, "backup": 18, "backward": [3, 4, 6, 14, 18], "bad": 8, "badli": 20, "bag": [11, 17, 23], "bag_clf": 12, "baggin": 23, "baggingboot": 12, "baggingclassifi": 12, "baggingtre": 12, "balanc": 8, "band": 18, "bandwidth": 18, "bar": [2, 8, 13, 23], "barber": 22, "bare": [6, 12], "base": [1, 2, 3, 5, 6, 7, 9, 10, 11, 12, 16, 17, 20, 21, 22, 23], "basi": [7, 9, 10, 12, 13, 14, 15, 18], "basic": [0, 8, 10, 14, 15, 16, 17, 20, 23], "batch": [5, 6, 13, 14, 15], "batch_shap": 6, "batch_siz": [3, 5, 6], "batchnorm": 6, "bay": 9, "bayesian": [7, 17, 22, 23], "beam": 23, "becaus": [2, 3, 4, 5, 6, 7, 8, 10, 11, 14, 15, 16, 23], "becom": [2, 3, 4, 7, 8, 9, 11, 14, 15, 20, 23], "been": [2, 3, 4, 5, 6, 7, 8, 13, 14, 15, 17, 18, 23], "befor": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 14, 15, 16, 18, 20, 23], "beforehand": [2, 20, 23], "begin": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 18, 20, 21, 23], "behav": [3, 8, 15], "behavior": [2, 3, 15, 23], "behaviour": 14, "behind": [2, 3, 8, 10, 15, 23], "being": [2, 3, 4, 5, 6, 7, 9, 10, 12, 13, 14, 15, 20, 23], "believ": [11, 18], "belong": [9, 10, 11, 15, 16], "below": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "benchmark": 12, "benefici": [3, 15], "benefit": [2, 3, 6, 13, 15, 17, 23], "bengio": [3, 22, 23], "benign": [3, 9], "besid": [6, 7], "bessel": 7, "best": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 21, 23], "beta": [1, 2, 3, 5, 7, 8, 9, 12, 13, 15, 23], "beta_": [2, 5, 8, 9, 15, 23], "beta_0": [1, 2, 3, 5, 7, 8, 9, 15, 23], "beta_0x_": [2, 23], "beta_1": [2, 3, 5, 7, 8, 9, 12, 15, 23], "beta_1x_": [2, 23], "beta_1x_0": [2, 23], "beta_1x_1": [2, 9, 23], "beta_1x_2": [2, 23], "beta_1x_i": [9, 15], "beta_2": [2, 5, 15, 23], "beta_2x_": [2, 23], "beta_2x_0": [2, 23], "beta_2x_1": [2, 23], "beta_2x_2": [2, 9, 23], "beta_3": 5, "beta_i": [2, 5, 7, 23], "beta_j": [2, 7, 8, 15, 23], "beta_k": 15, "beta_linreg": 15, "beta_m": 12, "beta_mg_m": 12, "beta_n": 5, "beta_p": 9, "beta_px_p": 9, "betavalu": 7, "better": [2, 3, 4, 5, 6, 8, 11, 12, 13, 14, 15, 23], "between": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 20, 23], "beyond": [2, 3, 7, 8, 10, 15, 23], "bf": [15, 16, 18, 20], "bg": 23, "bgd": 15, "bia": [2, 3, 4, 5, 7, 10, 11, 12, 14, 15, 23], "bias": [3, 4, 5, 7, 8, 11, 14], "big": [2, 3, 4, 7, 8, 16], "bigger": [3, 8], "bigr": 14, "bike": 11, "bilbo": 23, "billion": [5, 14, 17], "bin": [2, 9, 20], "binari": [2, 5, 7, 9, 11, 12, 14, 23], "binarycrossentropi": 6, "bind": 2, "binomi": [17, 20, 23], "binsboot": 8, "bioinformat": 2, "biolog": [3, 14], "bios1100": [17, 23], "bird": [2, 5], "birth": 23, "bishop": [22, 23], "bit": [3, 6, 18, 20, 23], "bitwis": 20, "bivari": 4, "bk": [2, 15], "bla": [18, 23], "black": [10, 11, 16], "block": [8, 12, 17, 18, 20, 23], "blog": 23, "blogpost": 6, "blue": [2, 5], "bmatrix": [2, 3, 5, 7, 9, 10, 13, 15, 18, 23], "bmi": 3, "bodi": [2, 3, 6, 14, 23], "bold": 3, "boldfac": [1, 2, 7], "boldsymbol": [1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 15, 16, 23], "boltzmann": [14, 17, 23], "book": [22, 23], "book1": 22, "boolean": 6, "boost": [3, 11, 17, 23], "boostrap": 12, "bootstrap": [3, 15, 17, 23], "borrow": 23, "boston_dataset": 2, "bot": 10, "both": [0, 1, 2, 3, 6, 7, 8, 10, 11, 12, 15, 16, 17, 18, 20, 21, 23], "bottl": 9, "bound": [2, 10, 14], "boundari": [4, 6, 10, 13, 14], "box": [6, 11], "boyd": [10, 15], "bracket": [6, 20], "brain": [3, 9, 14], "branch": 11, "break": [2, 6, 8, 13, 16, 23], "breast": [7, 9, 13], "breviti": 15, "brew": [2, 17, 23], "brg": 10, "briefli": [1, 2, 23], "bring": [2, 7, 8, 12], "britt": [21, 23], "broad": 2, "brought": [15, 17, 23], "brownle": 6, "browser": [0, 23], "brute": [5, 7, 13], "buffer_s": 6, "bui": 6, "build": [1, 2, 6, 7, 8, 12, 18, 20, 23], "built": [2, 3, 5, 6, 8], "bunch": 13, "busi": 2, "byte": [18, 23], "c": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22], "c1": [10, 13], "c2": [10, 13], "c_": [2, 10, 11, 12, 15, 20], "c_0": 20, "c_1": 14, "c_2": 14, "c_3": 14, "c_4": 14, "c_i": [14, 15], "c_k": 20, "ca": [3, 23], "cach": 12, "cal": [2, 10, 12, 14, 15, 23], "calcul": [1, 2, 3, 4, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "call": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23], "calor": 2, "cambridg": [15, 22], "can": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22], "cancel": [2, 15, 23], "cancer": [7, 12], "cancerpd": 9, "candid": [10, 11, 12], "cannot": [2, 3, 6, 7, 8, 9, 10, 11, 20], "canopi": [2, 17, 23], "canva": [0, 1, 23], "cap": 7, "capabl": [2, 3, 10, 15, 17, 23], "capac": [4, 21], "capita": 2, "captur": [6, 13, 14], "car": [5, 6], "card": [2, 9, 23], "cardin": 3, "care": [0, 13], "carefulli": 15, "carlo": [2, 8, 17, 20, 22, 23], "carri": [4, 8, 9], "cart": 12, "case": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 13, 14, 15, 16, 17, 18, 23], "casella": 22, "cast": 3, "cat": [5, 6], "catch": 2, "categor": [2, 3, 5, 11, 13, 23], "categori": [2, 3, 5, 9, 12, 14, 16, 23], "categorical_crossentropi": [3, 5], "caus": [2, 7, 8, 20, 23], "causal": 2, "causat": [2, 23], "cax": 3, "cb": [8, 23], "cbar": 3, "cc": [2, 3, 7, 15, 23], "ccc": [7, 14], "cdf": 20, "cdot": [2, 4, 8, 14, 15, 16, 18, 20, 23], "celebr": 15, "cell": 6, "center": [2, 3, 8, 9, 10, 11, 13, 16, 20, 23], "central": [1, 2, 5, 7, 8, 10, 18, 23], "centroid": [16, 20], "centroid_differ": 16, "centuri": 5, "certain": [2, 5, 8, 9, 11, 20, 23], "cg": 15, "cha": 2, "chain": [2, 3, 15, 17, 20, 23], "challeng": 0, "chanc": [3, 7, 15, 20], "chang": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 14, 15, 16, 18, 20, 23], "channel": 5, "chapter": [1, 2, 8, 12, 13, 18, 22, 23], "chapter3": 2, "charact": [2, 5, 7, 23], "character": [10, 11, 12, 14, 20], "characterist": [2, 3, 5, 12, 15, 23], "charg": [2, 23], "charl": 2, "chase": 6, "chatgpt": 0, "chd": 9, "chddata": 9, "cheap": 7, "cheaper": [3, 15], "check": [0, 1, 2, 3, 5, 6, 7, 13, 15, 18, 23], "checkmark": 5, "checkpoint": 6, "checkpoint_dir": 6, "checkpoint_prefix": 6, "chen": 12, "chiaramont": 4, "childcar": 1, "children": 1, "choic": [2, 3, 4, 5, 6, 8, 11, 14, 15, 16, 18, 23], "choleski": [7, 18], "choos": [0, 4, 5, 8, 11, 12, 13, 15, 16], "chosen": [1, 2, 3, 4, 8, 10, 11, 12, 15, 20, 23], "chosen_datapoint": 3, "christian": 22, "christoph": [22, 23], "cifar": 5, "cifar10": 5, "circ": [3, 14], "circl": [2, 10, 14], "circuit": 5, "circumfer": 11, "circumv": [3, 7, 15], "ckpt": 6, "clariti": 20, "class": [2, 3, 5, 6, 8, 9, 10, 11, 13, 14, 15, 20, 23], "class_nam": [5, 11], "class_val": 11, "class_valu": 11, "classic": [9, 11, 15], "classif": [2, 5, 7, 8, 9, 10, 13, 14, 17, 22, 23], "classifi": [2, 3, 6, 9, 11, 12, 13, 23], "classificaton": 3, "classifii": 12, "clean": 3, "clear": [3, 7, 12, 14, 15], "clearli": [2, 5, 7, 8, 9, 10, 20], "clever": [3, 12], "clf": [2, 8, 10, 11, 12, 23], "clf3": 2, "clf_lasso": 8, "clf_ridg": [8, 23], "cli": 0, "clip": [5, 20], "clone": [0, 21], "close": [2, 3, 4, 6, 8, 10, 11, 13, 14, 15, 16, 20, 22, 23], "closer": [5, 7, 15], "closest": [10, 13, 15, 16], "closur": [17, 23], "cloud": [17, 23], "cluster": [2, 3, 6, 8, 13, 17, 23], "cluster_label": 16, "cm": [3, 4, 5, 8, 10, 15], "cmap": [2, 3, 4, 5, 6, 8, 10, 11, 12, 23], "cmap_arg": 8, "cmd": [0, 11], "cn_": 20, "cnn": 14, "cnn_kera": 5, "cntk": [17, 23], "co": [2, 4, 5, 8, 11, 15, 23], "code": [5, 6, 8, 9, 10, 17, 18, 20, 22], "coef": [2, 23], "coef0": 10, "coef_": [1, 2, 7, 8, 10, 11, 15, 23], "coeff": 7, "coeffici": [2, 5, 7, 8, 9, 10, 11, 15, 18, 23], "coerc": [2, 8, 23], "coin": [12, 20], "coin_toss": 12, "col": [2, 13, 23], "colab": [17, 23], "cold": 11, "colinear": 2, "collaps": 10, "collect": [2, 4, 8, 12, 13, 17, 20, 22, 23], "collinear": 7, "color": [2, 5, 6, 8, 10, 11, 12, 20], "color_channel": 5, "color_cod": 8, "colorbar": [3, 8], "colsample_bytre": 12, "colsaobject": 12, "column": [1, 2, 3, 4, 7, 8, 9, 10, 11, 13, 14, 18, 23], "columntransform": 11, "com": [0, 1, 6, 8, 17, 22, 23], "combin": [3, 4, 7, 8, 9, 12, 20], "come": [0, 2, 3, 5, 6, 7, 14, 15, 16, 23], "command": [0, 2, 3, 23], "comment": [2, 6, 7, 8, 23], "commerci": [2, 17, 23], "commit": 0, "commod": [2, 23], "common": [1, 2, 3, 5, 7, 8, 9, 11, 13, 15, 16, 20, 23], "commonli": [2, 3, 6, 8, 9, 11, 15, 16], "commun": [2, 14], "commut": 5, "commutatitav": 5, "compact": [2, 3, 5, 7, 8, 9, 11, 13, 14, 15, 16, 23], "compair": 2, "compar": [2, 5, 6, 7, 8, 13, 15, 18, 23], "comparison": [4, 6, 15], "compat": 9, "compet": 2, "competit": 12, "compil": [2, 3, 5, 6, 15, 17, 18, 23], "complet": [0, 1, 2, 4, 5, 6, 11, 14, 23], "completenn": 14, "complex": [1, 3, 7, 10, 11, 13, 14, 15, 23], "complic": [2, 3, 11, 15, 23], "compon": [1, 2, 3, 5, 6, 7, 8, 9, 11, 16, 17, 23], "components_": 13, "compos": [11, 14, 15, 16, 17, 23], "compphys": [1, 2, 8, 17, 19, 21, 22, 23], "compress": [2, 23], "compris": 8, "compromis": 7, "compulsori": [17, 23], "comput": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 17, 18, 19, 20, 22, 23], "computation": [2, 5, 8, 11, 15, 20, 23], "computationalscienceuio": 23, "concaten": [4, 6, 8, 16], "concav": [3, 15], "concentr": [2, 12], "concept": [2, 4, 17, 23], "conceptu": [14, 15], "concern": [2, 3, 6, 9, 23], "concic": 23, "conclud": [2, 7, 15], "conclus": 3, "cond": 4, "conda": [2, 3, 17, 23], "condit": [2, 4, 6, 7, 8, 10, 11, 13, 15, 20, 23], "conduct": 17, "condwav": 4, "confid": [2, 7, 8, 9, 10, 23], "configur": 5, "confirm": [7, 14], "confus": [7, 8, 9, 12, 18], "confusion_matrix": 11, "congruenti": 20, "conjug": [6, 10], "conjugaci": 15, "conjunct": 5, "connect": [2, 3, 5, 6, 11, 13, 14, 15, 18, 23], "consequ": [7, 8, 10, 12, 14, 15], "conserv": [7, 16], "consid": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 18, 20, 23], "consider": [2, 3, 7, 15, 23], "consist": [2, 3, 4, 5, 6, 8, 14, 15, 20], "constant": [1, 2, 4, 6, 7, 8, 10, 14, 15, 20, 23], "constitu": [2, 23], "constitut": [4, 8], "constrain": [3, 5, 7, 9, 13], "constraint": [7, 8, 10, 15], "construct": [2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 18, 20, 23], "contact": [2, 23], "contain": [0, 2, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 18, 20, 22, 23], "contemporari": 23, "content": [0, 3, 17, 18, 23], "context": [8, 12, 15], "contigu": 18, "continu": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 18, 20, 23], "contour": [11, 12, 15], "contourf": [10, 11, 12], "contrast": [3, 6, 11, 12, 14], "contribut": [2, 5, 7, 15, 20, 23], "contributor": 2, "control": [0, 2, 3, 5, 11, 15, 17, 23], "conv": [5, 6], "conv2d": [5, 6], "conv2dtranspos": 6, "convei": 23, "conveni": [2, 7, 8, 14, 15, 18, 23], "convent": 14, "converg": [3, 4, 6, 7, 10, 15, 16], "convert": [2, 3, 6, 7, 11, 13, 15, 18, 23], "converttomatrix": 6, "convex": [6, 7, 9], "convinc": 15, "convolut": [3, 6, 17, 23], "cool": [6, 11], "coolwarm": 8, "coordin": [7, 14, 16], "coorel": 2, "copi": [0, 2, 3, 16], "core": 12, "corel": 23, "coronari": 9, "corr": [2, 7, 9, 13], "correalt": [13, 17], "correct": [0, 2, 3, 4, 5, 6, 7, 9, 15, 18, 20, 23], "correctli": [3, 4, 8, 9, 12], "correl": [2, 3, 5, 7, 8, 9, 12, 14, 15, 17, 20, 23], "correlation_matrix": [2, 7, 9, 13], "correspond": [2, 5, 7, 8, 10, 11, 13, 14, 17, 18, 20, 23], "cortex": 14, "cosin": [5, 8], "cost": [1, 2, 4, 5, 7, 8, 9, 10, 11, 14, 15, 23], "cost_deep_grad": 4, "cost_funct": 4, "cost_function_deep": 4, "cost_function_deep_grad": 4, "cost_function_grad": 4, "cost_grad": 4, "cost_sum": 4, "costol": 15, "could": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "coulomb": [2, 23], "count": [2, 11, 19, 20, 21, 23], "countor": 15, "coupl": [6, 7, 8], "cours": [0, 1, 2, 3, 5, 7, 13, 21], "coursework": 0, "courvil": [22, 23], "cov": [7, 8, 13, 18, 20, 23], "cov_xi": [7, 13], "cov_xx": [7, 13], "cov_yi": [7, 13], "covari": [2, 9, 17, 18, 23], "covariance_matrix": [7, 13, 16], "cover": [2, 7, 17, 21, 22], "covert": [2, 23], "covxi": 20, "covxx": 20, "covxz": 20, "covyi": 20, "covyz": 20, "covzz": 20, "cpu": 3, "craft": 5, "creat": [0, 3, 5, 6, 7, 11, 12, 13, 14, 17, 23], "create_biases_and_weight": 3, "create_convolutional_neural_network_kera": 5, "create_neural_network_kera": 3, "create_x": [7, 13], "credit": [2, 9, 21, 23], "crim": 2, "crime": 2, "criteria": [2, 6, 11, 12, 16, 20, 23], "criterion": [11, 12, 15], "critic": 8, "cross": [2, 3, 5, 9, 11, 12, 15, 17, 20, 23], "cross_entropi": 6, "cross_val_scor": 8, "cross_valid": [9, 12], "crossvalid": 8, "crucial": [3, 20], "cs231": 5, "csr_matrix": [18, 23], "csv": [2, 6, 8, 9, 11, 23], "ctnk": 3, "cubic": 2, "cumbersom": 7, "cumsum": [12, 13, 23], "cumul": [9, 12, 20], "cumulative_heads_ratio": 12, "cup": 7, "current": [0, 1, 3, 4, 5, 6, 15, 16, 22], "curs": 2, "curv": [8, 9, 12, 14], "curvatur": 15, "custom": [8, 16], "custom_cmap": [11, 12], "custom_cmap2": [11, 12], "cutpoint": 11, "cv": [8, 9, 12], "cvxbook": 15, "cvxopt": [7, 10], "cycl": [3, 14], "d": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 21, 23], "d2_g_t": 4, "d_f": 15, "d_g_t": 4, "d_net_out": 4, "da": 5, "dagger": [7, 18], "dai": [3, 11, 17], "damp": 5, "darget": 11, "darkr": 20, "dat": [2, 23], "dat_id": [2, 8, 9, 11, 23], "data": [1, 4, 6, 7, 10, 12, 14, 15, 16, 18, 22], "data1": 16, "data2": 16, "data3": 16, "data4": 16, "data_id": [2, 8, 9, 11, 23], "data_indic": 3, "data_panda": 23, "data_path": [2, 8, 9, 11, 23], "databas": 3, "datafil": [2, 8, 9, 11, 23], "datafram": [2, 6, 7, 9, 11, 13, 23], "datapoint": [1, 3, 7, 8, 9, 13, 15], "datasci": [0, 1], "dataset": [1, 2, 6, 8, 9, 10, 11, 12, 13, 15, 16, 23], "datatyp": 6, "date": [0, 23], "daughter": 12, "david": [22, 23], "dbh": 3, "dbo": 3, "dcomposit": 18, "ddot": 4, "dead": 3, "deal": [2, 3, 5, 7, 8, 10, 13, 15, 16, 18, 20, 23], "dealt": 2, "debt": 9, "debug": [2, 7, 8], "decad": [2, 5], "decai": [2, 15, 20, 23], "decemb": [21, 23], "decent": 12, "decid": [2, 4, 5, 7, 8, 11], "decim": [2, 23], "decis": [2, 3, 10, 13, 17, 22, 23], "decision_funct": 10, "decision_tre": 11, "decisiontreeclassifi": [11, 12], "decisiontreeregressor": [2, 11, 12], "declar": [2, 6, 18, 23], "decompos": [7, 8, 18], "decomposit": [2, 8, 14, 23], "decompost": 7, "deconvolut": 5, "decorrel": [12, 15], "decreas": [3, 4, 6, 7, 8, 12, 13, 15], "deduc": [2, 23], "deep": [5, 9, 14, 15, 17, 22], "deep_neural_network": 4, "deep_param": 4, "deep_tree_clf": [11, 12], "deep_tree_clf1": 11, "deep_tree_clf2": 11, "deepen": [7, 17, 23], "deeper": [2, 5, 6, 23], "deeplearningbook": [22, 23], "deer": 5, "def": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 20, 23], "def_covari": 20, "default": [2, 3, 4, 6, 8, 9, 18, 23], "default_tim": 6, "defect": 7, "defici": 7, "defin": [1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20], "definit": [3, 4, 7, 8, 9, 10, 12, 13, 14, 15, 18, 20], "defint": 20, "degre": [0, 1, 5, 7, 8, 10, 11, 12, 13, 20, 23], "del": 3, "delet": [0, 8], "delimit": 6, "deliv": [0, 19, 23], "delta": [2, 4, 5, 8, 10, 14, 15, 16, 23], "delta_": [3, 18], "delta_0": 5, "delta_1": 5, "delta_2": 5, "delta_3": 5, "delta_4": 5, "delta_5": 5, "delta_h": [2, 3, 23], "delta_j": [5, 14], "delta_k": 14, "delta_l": [3, 5], "delta_momentum": 15, "delta_n": [2, 5, 23], "delug": 17, "delv": 2, "demand": 15, "demonstr": [2, 5, 7, 8, 9, 13, 14, 17, 23], "den": 6, "denomin": [3, 7], "denot": [3, 4, 8, 9, 15, 20], "dens": [3, 5, 6], "densiti": [2, 4, 8, 20, 23], "depart": [21, 23], "depend": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 13, 14, 15, 17, 18, 20, 23], "depict": 20, "deploy": [2, 17, 23], "depth": [2, 5, 11, 12, 18], "deriv": [2, 3, 4, 8, 9, 10, 12, 13, 15, 17, 23], "derivati": 15, "derivative_fn": 15, "descend": [7, 11, 13], "descent": [2, 3, 5, 9, 10, 14, 23], "describ": [2, 4, 6, 7, 8, 10, 12, 13, 14, 15, 18, 23], "descript": [2, 10, 11, 23], "design": [2, 3, 5, 6, 7, 8, 9, 12, 13, 14, 15, 23], "designmatrix": [2, 23], "desir": [2, 4, 6, 7, 15, 16, 23], "desktop": 0, "despit": [3, 14], "destroi": 18, "det": [7, 18], "detail": [2, 8, 13, 15, 16, 18], "detect": [5, 10, 14], "determin": [2, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 18, 20, 23], "determinist": [9, 15, 20], "dev": 3, "develop": [2, 5, 7, 10, 12, 13, 14, 17, 18, 23], "deviat": [2, 3, 4, 6, 7, 8, 20, 23], "devis": 14, "df": [6, 10, 13, 15, 23], "df1": 23, "di": 2, "diag": [7, 10], "diagnost": [3, 12], "diagon": [2, 7, 9, 15, 18, 20, 23], "diagonaliz": 7, "diagram": 12, "diagsvd": 8, "dice": [8, 20], "dict": [8, 10], "dictionari": 2, "did": [1, 2, 3, 7, 8, 9, 12, 13, 16, 23], "die": 3, "diff": 4, "diff1": 4, "diff2": 4, "diff_ag": 4, "diffeent": 10, "differ": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22, 23], "differenti": [1, 2, 5, 17, 18, 23], "difficult": [2, 3, 8, 12, 15, 20, 23], "difficulti": [2, 3, 15, 23], "diffonedim": 4, "digit": [2, 3, 5, 6, 8, 21, 23], "dilemma": 15, "dilut": 3, "dim": [6, 13, 16, 18], "dimens": [1, 2, 3, 4, 5, 6, 7, 10, 13, 16, 18, 23], "dimension": [2, 6, 7, 8, 11, 13, 15, 16, 17, 18, 23], "dimensionless": [2, 5, 23], "diment": 18, "dimnsion": 6, "diod": 5, "direct": [2, 3, 4, 6, 13, 14, 15, 16, 23], "directli": [3, 6, 7, 8, 20], "disadvantag": [2, 23], "disappear": [5, 8], "disc_loss": 6, "disc_tap": 6, "discard": [8, 13], "disciplin": [2, 5, 14], "disclaim": 20, "discord": 23, "discourag": [0, 15], "discov": [2, 23], "discover": 7, "discret": [3, 5, 7, 9, 15], "discrimin": [6, 9, 12, 13], "discriminator_loss": 6, "discriminator_loss_list": 6, "discriminator_model": 6, "discriminator_optim": 6, "discuss": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 22, 23], "diseas": 9, "disguis": 8, "disord": [3, 9], "displai": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 20, 23], "displaystyl": [2, 7, 23], "disregard": [2, 23], "dissimilar": [13, 16], "dist": 16, "distanc": [2, 10, 11, 13, 16, 20], "distance_list": 11, "distinct": [5, 9, 10, 11, 12, 16], "distinctli": 10, "distinguish": [2, 6, 9, 10, 20, 23], "distplot": 2, "distribut": [2, 3, 6, 8, 9, 12, 13, 15, 16, 17, 18, 23], "distrubut": [2, 17, 23], "dive": [2, 10, 18, 23], "diverg": [3, 15], "divid": [2, 3, 5, 7, 8, 9, 10, 11, 13, 14, 20, 23], "divis": [8, 10, 11, 15, 18, 20], "dna": 9, "dnn": [2, 3, 4, 6, 14, 23], "dnn1": 6, "dnn2_gru2": 6, "dnn_kera": 3, "dnn_model": 3, "dnn_numpi": 3, "dnn_scikit": [2, 3, 23], "do": [0, 1, 2, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 18, 23], "doc": [0, 1, 2, 17, 19, 21, 22, 23], "document": [0, 6, 15], "doe": [1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 13, 14, 15, 18, 20, 23], "doesn": [5, 11, 14], "dog": [3, 5, 6], "domain": [7, 10, 15], "domin": [2, 23], "don": [0, 1, 2, 3, 5, 7, 8, 10, 13, 15, 17, 23], "done": [1, 2, 4, 5, 6, 7, 8, 11, 12, 13, 15, 18, 23], "dot": [2, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "doubl": [1, 5, 6, 18, 23], "doubli": 3, "down": [2, 5, 8, 11, 13, 14, 15], "download": [0, 2, 3, 5, 7, 8, 18, 22, 23], "downsampl": 5, "dozen": 3, "dq": 8, "drag": 15, "dramat": 13, "drastic": 6, "draw": [6, 8, 12, 15], "drawback": [2, 3, 5, 15], "drawn": [3, 6, 8, 9, 13, 20, 23], "drive": [5, 6], "driven": 5, "drop": [2, 3, 7, 8, 13, 15, 20, 23], "dropna": [2, 8, 23], "dropout": 6, "dt": [4, 5, 15, 20], "dtype": [2, 3, 5, 6, 16, 18, 23], "dub": [2, 23], "due": [3, 4, 7, 8, 10, 12, 14, 15, 21, 23], "dummi": 2, "dure": [2, 3, 5, 6, 10, 11, 13, 17, 23], "dwell": 2, "dwh": 3, "dwo": 3, "dx": [4, 5, 10, 20], "dx_1": 20, "dx_1p": 8, "dx_2p": 8, "dx_mp": 8, "dx_n": 20, "dxp": 8, "dy": [3, 10, 20], "dynam": 6, "dz": 10, "e": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 21, 23], "e_": [2, 4, 23], "each": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23], "eapprox": [2, 23], "earli": [3, 15], "earlier": [2, 7, 9, 10, 11, 13, 14, 15, 23], "earthexplor": 8, "eas": [8, 11, 16], "easi": [0, 2, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 23], "easier": [0, 7, 8, 10, 11, 15, 20, 23], "easiest": 15, "easili": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 23], "eastern": [21, 23], "ebind": [2, 23], "eblock": 11, "econometr": 23, "economi": 7, "ecosystem": [17, 23], "ect": 19, "edg": 5, "edgecolor": 8, "edu": 15, "educ": [2, 23], "eff": 20, "effect": [1, 3, 6, 12, 15, 20], "effic": 3, "effici": [2, 5, 12, 15, 17, 18, 20, 23], "efron": 8, "egrad": 15, "eig": [7, 13, 15, 18, 20, 23], "eigen": 20, "eigenpair": [7, 13], "eigenvalu": [2, 7, 10, 13, 15, 18, 23], "eigenvector": [7, 13, 15], "eight": [18, 23], "eigval": [18, 20, 23], "eigvalu": [13, 15], "eigvec": [18, 20, 23], "eigvector": [13, 15], "eir": [21, 23], "eispack": [18, 23], "either": [3, 7, 8, 9, 10, 11, 12, 13, 15, 20, 23], "eivind": [21, 23], "eivinsto": [21, 23], "ekstr\u00f8m": 6, "elabor": 20, "elarn": 5, "electr": [2, 5, 14, 23], "electron": 23, "eleg": 13, "element": [3, 4, 5, 6, 7, 8, 9, 10, 13, 14, 15, 17, 18, 22], "elementari": [12, 15, 18], "elementwis": [5, 15], "elementwise_grad": [4, 15], "elessar": 23, "elif": 16, "elim": 18, "elimin": [5, 10], "elin": [21, 23], "els": [1, 3, 5, 6, 9, 11, 14, 15, 18], "elu": 3, "elus": [2, 23], "email": [19, 21, 23], "embed": [2, 13], "embodi": 8, "emit": 20, "emner": 22, "emphas": [2, 12, 17, 23], "emphasi": [2, 17, 22, 23], "empir": [3, 13, 20], "emploi": [2, 3, 7, 8, 13, 15, 20, 23], "employ": 2, "empti": [0, 8, 12], "emul": 14, "en": [17, 22], "enabl": 13, "enbodi": 8, "encod": [2, 5, 7, 11, 13, 16, 23], "encompass": [2, 20], "encount": [0, 2, 3, 7, 9, 15, 20, 23], "encourag": 0, "end": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 21, 23], "endpoint": [5, 8], "energi": [2, 6, 8], "enforc": 14, "eng": 22, "engin": [2, 3, 5, 6, 17, 23], "enorm": 5, "enough": [2, 8, 15, 23], "ensembl": [3, 11], "ensur": [2, 3, 4, 5, 7, 8, 13, 15, 20], "entail": 23, "enter": [7, 8], "enthought": [2, 17, 23], "entir": [3, 5, 9, 11, 17, 20, 23], "entiti": [11, 14, 18, 23], "entri": [2, 7, 10, 13, 14, 18, 23], "entropi": [3, 5, 9, 12, 15, 23], "enumer": [2, 3, 4, 5, 6, 8, 10, 23], "env": 20, "environ": [4, 17], "environemnt": 0, "eo": [2, 8, 23], "eol": [2, 23], "eosfit": [2, 23], "epoch": [2, 3, 5, 6, 14, 15, 23], "epsilon": [2, 7, 8, 9, 15, 23], "epsilon_": [2, 23], "epsilon_0": [2, 23], "epsilon_1": [2, 23], "epsilon_2": [2, 23], "epsilon_i": [2, 23], "eq": [5, 15, 16, 18, 20], "eqnarrai": [5, 7, 8], "equal": [1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 14, 15, 16, 18, 20, 23], "equat": [3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20], "equilibrium": [4, 14], "equiv": [5, 15, 18, 20], "equival": [2, 3, 7, 9, 10, 13, 15, 17, 18, 23], "erf": 20, "eriador": 23, "eridg": 23, "err": [2, 12], "err_": 8, "err_sqr": 4, "errat": 15, "erron": 4, "error": [0, 1, 3, 4, 6, 7, 8, 9, 11, 13, 14, 15, 17, 18, 20], "error_estimate_corr_tim": 20, "error_hidden": 3, "error_output": 3, "escap": 15, "especi": [0, 3, 5, 11, 14, 15], "essenti": [0, 2, 7, 8, 11, 12, 14, 16, 20], "establish": [1, 2, 8, 12, 13, 23], "estim": [2, 3, 7, 8, 9, 12, 13, 15, 17, 20, 23], "estimated_mse_fold": 8, "estimated_mse_kfold": 8, "estimated_mse_sklearn": 8, "et": [1, 2, 4, 6, 22, 23], "eta": [2, 3, 5, 10, 14, 15, 23], "eta0": [10, 15], "eta_": 15, "eta_t": 15, "eta_v": [2, 3, 5, 23], "etc": [2, 3, 5, 7, 9, 10, 11, 13, 14, 15, 16, 17, 18, 20], "ethic": 17, "euclidean": [2, 16], "evalu": [0, 1, 2, 4, 5, 6, 7, 8, 11, 15, 20, 23], "evalut": 15, "even": [2, 3, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "evenli": 6, "event": [7, 9, 12, 20], "eventu": [2, 7, 8, 13, 14, 15, 21], "everi": [0, 2, 3, 4, 5, 6, 7, 8, 11, 12, 13, 14, 15, 16, 17, 20, 21, 23], "everyth": [1, 6, 14], "everywher": [6, 15], "evolv": 2, "exact": [2, 7, 13, 14, 15, 18, 20, 23], "exactli": [2, 5, 6, 8, 14, 17], "exam": 23, "examin": 8, "exampl": [1, 7, 13, 14, 15, 17, 18, 20, 22], "exce": [3, 14, 15], "excel": [2, 3, 6, 7, 12, 23], "except": [5, 6, 8, 10, 11, 18], "excess": [2, 23], "excit": 2, "exclud": [3, 8, 14], "exclus": [2, 3, 5, 8, 20, 23], "execut": [0, 4, 7, 15], "exemplifi": 15, "exercic": [21, 23], "exercis": [7, 17, 19, 21], "exhaust": 8, "exhibit": [2, 7, 8, 10, 23], "exist": [2, 3, 4, 5, 7, 8, 9, 10, 11, 15, 18, 23], "exit": [7, 18], "exp": [1, 2, 3, 4, 7, 8, 9, 10, 12, 13, 14, 15, 20, 23], "exp_term": 3, "expand": [7, 9, 13, 15], "expans": [2, 5, 7, 10, 12, 14, 15, 23], "expect": [0, 2, 3, 7, 8, 9, 13, 14, 15, 17, 23], "expectation_value_of_h_wrt_p": 20, "expens": [1, 8, 12, 15], "experi": [0, 2, 3, 8, 10, 15, 17, 23], "experiment": [2, 6, 8, 11, 20, 23], "expert": [3, 11], "explain": [1, 2, 8, 11, 12, 13, 15, 23], "explained_variance_ratio_": 13, "explanatori": [2, 23], "explicit": [2, 5, 8, 15, 18, 23], "explicitli": [2, 6], "explod": 3, "exploit": [2, 5, 14, 15, 23], "explor": [3, 6, 8, 10, 15, 17], "expon": 3, "exponenti": [2, 3, 7, 8, 12, 15, 20, 23], "export": [0, 1, 11], "export_graphviz": 11, "export_text": 11, "exporttext": 11, "expos": 17, "express": [2, 4, 5, 7, 8, 9, 12, 14, 15, 18, 20, 23], "exptmean": 20, "exptvari": 20, "extend": [2, 4, 9, 13, 15, 17, 23], "extens": [0, 2, 14, 17, 23], "extent": [2, 3, 8, 22], "extern": [5, 8, 11], "extra": [0, 3, 5, 7, 21, 23], "extract": [1, 2, 5, 7, 8, 9, 10, 13, 15, 18, 23], "extrapol": [2, 23], "extrem": [0, 1, 2, 3, 6, 7, 8, 9, 10, 11, 15, 18], "extremum": 15, "extrins": 13, "ey": [2, 7, 8, 15, 16, 18, 23], "f": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 14, 15, 16, 18, 20, 21, 23], "f1": 15, "f11": [2, 23], "f12": [2, 23], "f13": [2, 23], "f1_grad": 15, "f1d": 15, "f2": 15, "f2_grad_x1": 15, "f2_grad_x1_analyt": 15, "f2_grad_x2": 15, "f2_grad_x2_analyt": 15, "f3": 15, "f3_grad": 15, "f3_grad_analyt": 15, "f4": 15, "f4_grad": 15, "f4_grad_analyt": 15, "f5": 15, "f5_grad": 15, "f6": 15, "f6_for": 15, "f6_for_grad": 15, "f6_grad_analyt": 15, "f6_while": 15, "f6_while_grad": 15, "f7": 15, "f7_grad": 15, "f7_grad_analyt": 15, "f8": 15, "f8_grad": 15, "f9": [2, 15, 23], "f9_altern": 15, "f9_alternative_grad": 15, "f9_grad": 15, "f_": 12, "f_0": [5, 12], "f_1": [12, 15], "f_2": [14, 15], "f_3": 14, "f_d": 20, "f_grad": 15, "f_grad_analyt": 15, "f_i": [1, 2, 8, 14], "f_m": [5, 12], "f_n": 5, "f_vec": 4, "face": 15, "facecolor": [8, 10, 20], "facil": [2, 17, 23], "facilit": 14, "fact": [2, 3, 5, 7, 11, 13, 14, 15, 23], "factor": [2, 3, 5, 7, 8, 11, 12, 13, 15, 18, 20, 23], "factori": 15, "fade": 8, "fafab0": [11, 12], "fail": [2, 8, 15, 21, 23], "failur": 9, "fairli": [3, 4, 20], "faisal": 1, "fake": 6, "fake_loss": 6, "fake_output": 6, "fall": [10, 11, 19], "fals": [1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 16, 18, 23], "famili": [2, 9, 10, 20], "familiar": [0, 2, 5, 7, 8, 10, 17, 18, 20, 23], "famou": [8, 14], "far": [1, 2, 5, 6, 7, 8, 10, 13, 14, 15, 16, 23], "fashion": [2, 11, 12, 23], "fast": [3, 5, 8, 12, 14, 15, 17, 20, 23], "faster": [3, 13, 15], "fastest": [15, 18], "favor": 9, "favorit": 20, "fc": 5, "featur": [0, 2, 3, 5, 7, 8, 9, 10, 12, 13, 14, 15, 17, 20, 23], "feature_nam": [2, 3, 9, 11], "feautur": 11, "fed": 3, "feed": [2, 4, 5, 13, 17, 23], "feed_forward": 3, "feed_forward_out": 3, "feed_forward_train": 3, "feedback": [6, 23], "feeddorward": 6, "feedforward": [3, 6, 14], "feel": [0, 1, 2, 7, 8, 13, 15, 17, 21, 23], "feet": 2, "fetch": [0, 8], "few": [3, 5, 6, 7, 11, 20, 23], "fewer": [2, 11, 13, 23], "ffnn": [3, 14], "field": [2, 5, 8, 14, 17], "fifth": [2, 8, 23], "fig": [2, 3, 4, 5, 6, 8, 9, 14, 15, 16, 23], "fig_id": [2, 8, 9, 11, 23], "figaxi": 20, "figsiz": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 23], "figur": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 23], "figure_id": [2, 8, 9, 11, 23], "figurefil": [2, 8, 9, 11, 23], "file": [0, 2, 6, 7, 8, 9, 11, 23], "file_prefix": 6, "filenam": 23, "fill": [7, 11], "filter": [5, 6], "final": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 19, 20, 21, 23], "financ": 2, "find": [0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20, 23], "fine": [2, 16, 23], "finish": 4, "finit": [5, 7, 8, 14, 15, 20], "finnicki": 0, "first": [0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 21, 22], "firsteigvector": 13, "fit": [3, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 20], "fit_beta": 8, "fit_intercept": [1, 2, 7, 8], "fit_mod": 11, "fit_transform": [0, 2, 8, 10, 11, 13], "fiti": [2, 23], "five": [2, 11, 23], "fix": [2, 5, 6, 8, 12, 13, 14, 15, 23], "flag": 6, "flat": [14, 15], "flatten": [3, 5, 6, 7, 18], "flexibl": [3, 8, 10, 12, 14, 23], "flip": [21, 23], "float": [2, 5, 6, 7, 11, 13, 15, 16, 18, 23], "float32": [6, 11], "float64": [6, 18, 23], "flop": [7, 18], "flow": [3, 6, 14], "fluctuat": 7, "fly": 13, "fm": [2, 23], "fmax": 5, "fmesh": 15, "fn": 9, "focu": [0, 2, 5, 6, 7, 8, 17, 22, 23], "focus": [3, 8, 9, 18], "fold": [8, 11], "folder": [0, 2, 6, 8, 23], "follow": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23], "font": [2, 9, 20, 23], "fontdict": 20, "fontsiz": [3, 8, 10, 11, 12, 20], "fontweight": 3, "footprint": 5, "foral": 10, "forc": [2, 7, 8, 12, 13, 23], "forcast": 6, "forecast": [6, 14], "forest": [2, 3, 11, 17, 23], "forget": 13, "form": [1, 2, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 17, 18, 20, 23], "formal": [5, 6, 16, 20], "format": [2, 3, 5, 6, 8, 9, 10, 11, 12, 13, 17, 20, 22], "format_data": 6, "formatstrformatt": [8, 15], "formul": [6, 8, 13, 16], "formula": [5, 15, 20], "forth": [6, 14], "fortran": [2, 17, 18, 23], "fortran2003": [17, 23], "fortran90": 20, "fortun": [2, 13], "forward": [2, 5, 8, 17, 18, 23], "foster": 23, "found": [3, 4, 6, 7, 8, 14, 15, 23], "foundat": [17, 23], "four": [6, 7, 8, 10, 14, 18, 19, 21, 23], "fourier": [2, 23], "fourierdef1": 5, "fourierdef2": 5, "fourierseriessign": 5, "fourth": [14, 23], "fp": 9, "frac": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "fraction": 11, "frame": 9, "framework": [3, 10, 12, 20], "frank": [7, 13], "frankefunct": [7, 8, 13], "fredli": [21, 23], "free": [0, 1, 2, 8, 13, 15, 17, 18, 20, 21, 22, 23], "freecodecamp": 17, "freedom": 7, "freeli": 2, "freez": 0, "frequenc": [5, 8, 9, 20], "frequent": [2, 10, 11, 15], "frequentist": 17, "fresh": 12, "fridai": [21, 23], "friedman": [8, 22, 23], "friendli": 6, "frodo": 23, "frog": 5, "from": [0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 13, 15, 16, 17, 18, 20, 21, 22, 23], "from_cod": 11, "from_logit": [5, 6], "from_tensor_slic": 6, "front": [2, 6, 7, 23], "frustrat": 0, "fulfil": [4, 7, 14], "full": [2, 3, 5, 7, 9, 11, 12, 15, 20, 23], "full_matric": 7, "fulli": [5, 8, 14, 20], "fun": [17, 23], "func": 4, "function": [0, 1, 4, 5, 6, 7, 11, 16, 17, 18], "functionali": 13, "fundament": [2, 8, 17, 23], "funtion": 4, "further": [4, 9, 11, 23], "furthermor": [2, 5, 7, 8, 9, 13, 14, 15, 17, 23], "futur": [2, 6, 10, 11, 23], "fy": [0, 19, 21, 22, 23], "fys5419": [22, 23], "fys5429": [22, 23], "f\u00f8470": [21, 23], "g": [0, 2, 3, 4, 5, 6, 8, 10, 11, 12, 13, 15, 20, 23], "g0": 4, "g_": [4, 11, 12], "g_0": 4, "g_1": [4, 12], "g_2": [4, 12], "g_analyt": 4, "g_dnn_ag": 4, "g_euler": 4, "g_i": 4, "g_m": [5, 12], "g_n": 5, "g_re": 4, "g_t": 4, "g_t_d2t": 4, "g_t_d2x": 4, "g_t_dt": 4, "g_t_hessian": 4, "g_t_hessian_func": 4, "g_t_jacobian": 4, "g_t_jacobian_func": 4, "g_trial": 4, "g_trial_deep": 4, "g_vec": 4, "gain": [3, 7, 9, 11, 12, 15], "galleri": [2, 23], "game": 6, "gamge": 23, "gamma": [2, 4, 10, 11, 12, 13, 15, 23], "gamma1": 10, "gamma2": 10, "gamma_": [2, 23], "gamma_0": 12, "gamma_1": 12, "gamma_1x": 12, "gamma_i": [2, 10, 20, 23], "gamma_j": 15, "gamma_k": 15, "gamma_m": 12, "gamma_x": [2, 23], "gap": 10, "gate": [6, 14], "gather": [2, 3, 14], "gaug": 14, "gaussbacksub": 18, "gaussian": [6, 7, 8, 10, 16, 20], "gaussian_point": 16, "gaussian_rbf": 10, "gave": 15, "gavra": 23, "gbc": 23, "gca": [4, 8, 10, 15], "gd": 3, "gd_clf": 12, "gdclassiffiercgain": 12, "gdclassiffierconfus": 12, "gdclassiffierroc": 12, "gdm": 15, "gdregress": 12, "ge": [3, 7, 9, 20], "gen_loss": 6, "gen_tap": 6, "gender": [2, 23], "genener": 6, "gener": [0, 1, 2, 3, 4, 5, 7, 8, 10, 12, 13, 14, 15, 16, 18, 20, 22], "generaliz": 1, "generallay": 14, "generate_and_save_imag": 6, "generate_imag": 6, "generate_latent_point": 6, "generate_simple_clustering_dataset": 16, "generated_imag": 6, "generator_loss": 6, "generator_loss_list": 6, "generator_model": 6, "generator_optim": 6, "genom": 17, "geodes": 13, "geometr": [2, 15, 23], "geometri": 7, "georg": 22, "geotif": 8, "geq": [4, 7, 10, 11, 15], "geron": [2, 22, 23], "get": [0, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 15, 17, 18, 20, 21, 23], "get_dummi": 11, "get_paramet": 4, "get_split": 11, "get_yaxi": 10, "get_yticklabel": 8, "gg": 23, "gh": 0, "gibb": [17, 23], "gif": 6, "gini": 12, "gini_index": 11, "ginvers": 15, "git": [0, 2, 17, 23], "giter": 15, "github": [2, 17, 19, 21, 22, 23], "gitignor": 0, "gitlab": [2, 17, 23], "give": [2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 20, 23], "given": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "global": [8, 9, 15], "glorot": 3, "gnew": 15, "go": [0, 1, 2, 3, 5, 7, 8, 10, 11, 13, 14, 15, 23], "goal": [2, 9, 11, 23], "goe": [0, 2, 3, 4, 7, 8, 15, 16, 18, 23], "golden": 15, "gone": 7, "gong": 3, "good": [0, 3, 5, 6, 7, 8, 11, 12, 13, 15, 17, 20, 22], "goodfellow": [6, 22, 23], "googl": [3, 6, 17, 23], "got": [3, 8], "gotten": 23, "gov": 8, "gp": 22, "gpu": [3, 15, 17, 23], "grad": [4, 15], "grad_analyt": 15, "grade": 19, "gradient": [2, 5, 6, 9, 10, 11, 14, 17, 23], "gradientboostingclassifi": 12, "gradientboostingregressor": 12, "gradients_of_discrimin": 6, "gradients_of_gener": 6, "gradienttap": 6, "gradual": [3, 16], "grai": [6, 8], "graph": [1, 3, 11, 13, 14, 15], "graph_from_dot_data": 11, "graphic": [2, 3, 11, 23], "grasp": 2, "gray_r": [3, 5], "grayscal": 5, "great": [0, 7, 15], "greater": [3, 9, 20], "greatli": 15, "greedi": 11, "green": [2, 5, 11, 20], "grei": 6, "grid": [3, 5, 8, 9, 10, 14, 20], "grossli": 15, "ground": [2, 23], "group": [0, 2, 8, 9, 11, 16, 17, 19, 21, 23], "groupbi": [2, 23], "grow": [3, 5, 11, 12], "growth": [2, 23], "gru": 6, "guarante": [2, 6, 15, 20, 23], "guess": [3, 6, 12, 15, 16], "guestrin": 12, "guid": 3, "h": [0, 2, 3, 7, 8, 10, 15, 20, 21, 22, 23], "h1": 4, "h_": [2, 15, 23], "h_1": [4, 15], "h_2": [4, 15], "h_m": 12, "ha": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "haanen": [21, 23], "habit": 2, "had": [2, 3, 8, 9, 15, 23], "hadamard": [3, 14, 15], "half": [3, 10, 11], "halv": 12, "hand": [2, 3, 4, 5, 7, 13, 14, 15, 17, 18, 20, 21, 22, 23], "handi": 5, "handl": [0, 2, 3, 4, 7, 11, 13, 17], "handle_unknown": 11, "handsid": 14, "handwrit": 14, "handwritten": [3, 7], "happen": [3, 4, 5, 6, 7, 8, 12, 15, 20], "hard": [3, 9, 10, 12, 15], "hardcopi": [17, 23], "harder": [2, 3], "harmon": 5, "hassl": [2, 17, 23], "hast": [17, 23], "hasti": [1, 2, 8, 22, 23], "hat": [1, 2, 3, 7, 8, 9, 11, 12, 13, 14, 15, 18], "have": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23], "haven": 3, "he": 9, "head": [2, 6, 12, 20], "header": [2, 23], "heads_proba": 12, "health": 2, "hear": [2, 15, 23], "heart": [2, 9, 23], "heatmap": [2, 3, 5, 9, 23], "heavili": 2, "heavisid": 3, "height": [3, 5, 8], "held": 15, "help": [0, 1, 2, 3, 6, 14, 15, 23], "helper": [6, 16], "henc": [2, 7, 8, 10, 11, 12, 14, 15, 23], "henrik": [21, 23], "her": 9, "here": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "hereaft": [2, 10, 14, 23], "hermitian": 18, "hessenberg": 18, "hessian": [2, 4, 7, 15], "heterogen": [11, 12], "hi": 9, "hidden": [3, 5, 6, 14], "hidden_bia": 3, "hidden_bias_gradi": 3, "hidden_layer_s": [2, 3, 23], "hidden_neuron": 6, "hidden_weight": 3, "hidden_weights_gradi": 3, "hierarch": 7, "high": [2, 3, 4, 5, 6, 7, 8, 11, 12, 13, 15, 16, 17, 18, 23], "higher": [2, 3, 5, 7, 8, 10, 15, 23], "highest": [3, 4], "highli": [2, 5, 6, 12, 17, 18, 22, 23], "highwai": 2, "hing": 10, "hint": [0, 1, 15], "hip": 17, "hire": 2, "hist": [6, 8, 9, 20], "histogram": [2, 8, 9, 20], "histor": [9, 13], "histori": [0, 5, 6, 14], "hitherto": 7, "hjorth": [21, 23], "hobbi": 20, "hoc": 7, "hoff": 22, "hold": [3, 5, 8, 15, 16], "holder": [2, 23], "home": 2, "homepag": 23, "homework": [8, 15], "homogen": [3, 5, 11, 12, 15], "honchar": 4, "hopefulli": [0, 2, 13, 20, 23], "horizont": 13, "horlyk": [21, 23], "hors": [5, 9], "hot": [3, 11], "hour": [3, 17, 19, 20, 21, 23], "how": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "howev": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "hspace": [2, 6, 10, 12, 20, 23], "hstack": 3, "htf": 23, "html": [1, 2, 17, 19, 21, 22, 23], "http": [0, 1, 2, 5, 6, 8, 15, 17, 18, 19, 21, 22, 23], "huang": [2, 23], "huber": [2, 23], "huge": [3, 5, 6, 17], "human": [2, 3, 5, 8, 11, 14], "humid": 11, "hundr": 3, "hungri": 3, "hybrid": 19, "hydrogen": [2, 23], "hyperbol": [3, 6, 14], "hyperparam": 10, "hyperparamet": [5, 6, 7, 8, 11, 15, 23], "hyperplan": 13, "h\u00f8rlyk": [21, 23], "i": [0, 1, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22], "i0": [2, 23], "i1": [2, 8, 10, 14, 23], "i2": [2, 10, 14, 23], "i3": [2, 14, 23], "i5": [2, 23], "i_": 15, "i_1": [7, 8], "i_2": [7, 8], "ian": 22, "ic": 3, "id": [9, 15], "ida": [21, 23], "idea": [2, 3, 4, 5, 6, 8, 11, 12, 14, 15, 18], "ideal": [2, 4, 8, 10, 15, 20, 23], "idem": 8, "ident": [7, 8, 14, 15, 18], "identifi": [2, 3, 9, 11, 13, 14, 15, 16, 23], "ieor": 20, "ifi": 22, "ifs": [17, 23], "ignor": [0, 2, 3, 5, 11], "ii": [18, 20], "iii": [18, 23], "ij": [1, 2, 3, 5, 8, 10, 14, 16, 18, 20, 23], "ik": [2, 18, 23], "illustr": [7, 9, 12, 14, 15, 16, 17, 23], "im": 8, "imag": [3, 5, 6, 8, 11, 13, 14, 16, 22, 23], "image_at_epoch_": 6, "image_batch": 6, "image_height": 5, "image_path": [2, 8, 9, 11, 23], "image_width": 5, "imageio": 8, "images_from_seed_imag": 6, "imagin": 3, "immedi": [2, 5, 6, 8, 17, 23], "implement": [2, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 20, 23], "impli": [5, 7, 8, 9, 15, 18], "implicit": 5, "implicitli": [13, 20], "import": [0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20], "importantli": 5, "impos": [2, 8, 13, 14, 23], "imposs": [2, 7, 23], "impress": [2, 14, 23], "improv": [0, 2, 6, 7, 11, 12, 13, 15], "impur": 11, "imread": 8, "imshow": [3, 5, 6, 8], "in3050": [22, 23], "in3310": 23, "in4080": [22, 23], "in4300": [22, 23], "in4310": 22, "in5400": 5, "in5550": 22, "in_out_neuron": 6, "inaccur": 15, "inact": 14, "inadequ": [2, 23], "inch": 8, "includ": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 13, 14, 17, 20, 21, 22, 23], "include_bia": [8, 11], "incom": [1, 14], "incorrect": 3, "incoveni": 10, "increas": [2, 3, 5, 6, 7, 8, 11, 14, 15, 20, 23], "increasingli": 20, "ind": 8, "inde": [2, 4, 6, 7, 8, 15, 23], "indefinit": 6, "independ": [2, 7, 8, 9, 10, 14, 15, 20, 23], "index": [2, 3, 5, 6, 12, 16, 17, 18, 20, 22, 23], "index_col": [2, 23], "indic": [1, 2, 3, 5, 6, 7, 8, 11, 12, 13, 15, 23], "indispens": 8, "individu": [3, 8, 9, 12, 14, 20, 23], "indu": 2, "indx": 18, "indx1": 4, "indx2": 4, "indx3": 4, "ineffici": [5, 15], "inequ": [10, 15], "inertia": 15, "inf1000": [17, 23], "inf1100": [17, 23], "inf1100l": [17, 23], "inf1110": [17, 23], "inf3000": 23, "infeas": 11, "infer": [2, 3, 6, 8, 22, 23], "inferenc": 3, "infil": [2, 8, 9, 11, 23], "infin": [7, 8, 9, 13], "infinit": 5, "infinitesim": 20, "influenc": [8, 12], "influenti": 3, "info": 23, "inform": [2, 3, 5, 6, 8, 11, 13, 14, 15, 16, 18, 22, 23], "inforom": 0, "infti": [5, 8, 15, 20], "ingeni": 15, "ingredi": [2, 11, 23], "inher": 8, "inherit": [18, 23], "initi": [2, 3, 4, 8, 12, 15, 16, 18, 20, 23], "inject": 16, "inlin": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "inner": [2, 15], "inp": 6, "inplac": 15, "input": [1, 2, 3, 5, 6, 7, 8, 9, 10, 14, 15, 16, 20, 23], "input_dim": 3, "input_shap": [5, 6], "inputs": 3, "inputs_shuffl": [2, 3], "insert": [5, 7, 8, 10, 12, 20], "insid": [2, 6, 9], "insight": [2, 3, 7, 17, 23], "insist": [8, 15], "inspir": [2, 3, 14, 23], "instabl": 4, "instal": [0, 2, 3, 7, 8, 11], "instanc": [1, 2, 3, 4, 6, 8, 11, 13, 15, 23], "instanti": 12, "instead": [2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 15, 16, 18, 20, 23], "institut": 3, "instruct": [2, 3, 23], "int": [2, 3, 4, 5, 6, 7, 8, 13, 15, 16, 18, 20], "int32": 12, "int_": [5, 8, 20], "int_0": 20, "int_a": 20, "intak": 2, "integ": [3, 4, 15, 16, 18, 20, 23], "integer_vector": 3, "integr": [5, 8, 20, 23], "intellig": [2, 16, 22, 23], "intend": 12, "intens": 3, "intention": 16, "interact": [2, 8, 11, 14, 17, 23], "intercept": [1, 2, 8, 10, 13, 15, 23], "intercept_": [2, 8, 10, 11, 15, 23], "interchang": [7, 14, 18], "interconnect": 3, "interest": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 14, 17, 20, 23], "interfac": [2, 3, 18], "interior": [2, 11, 23], "intermedi": 18, "intern": [3, 12, 14], "interpol": [3, 5, 6, 8, 14], "interpr": 7, "interpret": [0, 1, 2, 3, 8, 11, 12, 14, 15, 18, 20], "interv": [2, 5, 7, 8, 9, 15, 20, 23], "intial": 15, "intract": [2, 6], "intrins": [5, 13, 18, 20, 23], "intro": [17, 22, 23], "introduc": [2, 3, 7, 8, 10, 12, 14, 18, 20, 23], "introduct": [3, 4, 6, 15, 22], "introductori": [2, 6, 18, 22, 23], "intuit": [2, 7, 8, 10, 14, 15, 23], "inv": [2, 7, 15, 23], "invalu": [2, 15, 17, 23], "invari": 3, "invd": 7, "inver": 10, "invers": [2, 5, 8, 15, 23], "inverse_transform": 10, "invert": [1, 2, 7, 9, 12, 15, 23], "invh": 15, "invok": [2, 10], "involv": [2, 4, 8, 9, 13, 14], "io": [2, 17, 19, 21, 22, 23], "ip": [2, 10, 20, 23], "ipca": 13, "ipynb": [17, 23], "ipython": [2, 7, 9, 11, 13, 16, 17, 23], "iq": 8, "iri": [10, 11], "irreduc": 8, "irrelev": 7, "irrespect": [2, 23], "isn": 7, "isnul": 2, "isomap": 13, "isotop": 23, "issu": [0, 3, 11, 18], "it_arrai": 15, "item": [2, 15, 23], "items": [18, 23], "iter": [3, 4, 6, 8, 10, 15, 16, 20], "its": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "itself": [7, 8, 14, 20], "j": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 14, 15, 16, 18, 20, 22, 23], "j1": 18, "j_": 8, "j_lasso_sk": 8, "j_ridge_sk": 8, "j_sk": 8, "jackknif": [8, 17, 23], "jacobian": [4, 15], "jason": 6, "jax": [17, 23], "jensen": [21, 23], "jerom": 22, "ji": [14, 18], "jit": 15, "jj": [2, 7, 8, 23], "jk": [2, 3, 8, 14, 18, 23], "jl": [2, 23], "jm": 18, "jnp": 15, "job": [0, 4, 10, 12], "join": [2, 6, 8, 9, 11, 23], "joint": [6, 7], "judg": 15, "judgement": 8, "julia": [17, 18], "jump": 20, "junk": 6, "jupit": 23, "jupyt": [0, 1, 2, 17, 22, 23], "just": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20, 23], "justif": 2, "justifi": [5, 12], "k": [2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23], "k0": 9, "k1": 9, "kaggl": 8, "kappa_d": 20, "karl": [21, 23], "karush": 10, "katrin": [21, 23], "keep": [0, 2, 3, 6, 7, 8, 13, 15, 16, 18, 23], "keepdim": [3, 8, 12, 18], "kei": [2, 3, 5, 8, 14], "kept": [6, 8, 16], "kera": [2, 6, 17, 23], "kernel": [2, 3, 5, 17, 23], "kernel_regular": [3, 5], "kernel_s": 6, "kernelpca": 13, "kev": [2, 23], "kevin": [22, 23], "keyword": [18, 23], "kfold": 8, "kg": 3, "ki": 18, "kick": [3, 15], "kiener": 4, "kilomet": 8, "kind": [2, 4, 5, 6, 10, 14, 15, 16, 23], "kj": [8, 14, 18], "kjm": [17, 23], "kkt": 10, "kl": 20, "km": [14, 23], "kmean": 16, "kmeanspoint": 16, "kn_k": 16, "know": [0, 1, 2, 3, 4, 7, 8, 10, 15, 17, 23], "knowledg": [2, 17, 23], "known": [3, 5, 6, 7, 8, 9, 10, 11, 14, 18, 20, 22], "kondev": [2, 23], "kp": 20, "kpca": 13, "kroneck": 16, "kuhn": 10, "kvalsund": [21, 23], "kwown": [2, 23], "l": [2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 14, 15, 18, 20, 23], "l0": 9, "l1": [2, 3, 5, 9, 23], "l1_l2": [3, 5], "l1regl": 7, "l2": [3, 5], "l_": 18, "l_1": 9, "l_2": [9, 15], "l_j": 14, "la": 15, "la_i": 14, "la_k": 14, "lab": [17, 23], "label": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 20, 23], "labelencod": [9, 12], "labels": [8, 10, 11], "labels_shuffl": [2, 3], "laboratori": 19, "lack": [2, 23], "lagari": 4, "lagrang": [10, 13], "lambda": [2, 3, 4, 5, 7, 8, 9, 10, 12, 14, 15, 20, 23], "lambda_": 13, "lambda_0": 13, "lambda_1": [7, 10, 13], "lambda_2": [10, 13], "lambda_i": [10, 13], "lambda_iy_i": 10, "lambda_jy_iy_j": 10, "lambda_k": 10, "lambda_n": [7, 10], "lamda": 3, "land": [2, 10], "landmark": 10, "landscap": 15, "langl": [2, 8, 13, 20, 23], "languag": [2, 3, 6, 10, 17, 18, 22, 23], "lapack": [18, 23], "laplac": 7, "laptop": [0, 17], "larg": [2, 3, 4, 6, 7, 8, 10, 11, 12, 13, 15, 17, 18, 20, 22, 23], "larger": [2, 5, 7, 8, 10, 12, 13, 15, 20, 23], "largest": [6, 10, 13], "lasso": [2, 9, 17, 23], "lasso_sk": 8, "last": [1, 2, 3, 5, 6, 7, 8, 9, 10, 14, 18, 20, 21, 23], "latent": 6, "latent_dim": 6, "latent_point": 6, "latent_space_value_rang": 6, "later": [0, 2, 3, 6, 9, 10, 14, 15, 16, 17, 23], "latest": [0, 6, 17], "latest_checkpoint": 6, "latex": 23, "latter": [2, 5, 8, 9, 10, 13, 15, 18, 20, 23], "lattic": 14, "law": 2, "layer": [2, 6, 15, 23], "lbfg": [9, 11, 12], "lcc": [7, 8], "lda": 13, "ldot": [2, 8, 13, 23], "le": [7, 9, 12, 15, 20], "lead": [1, 2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "leaf": 11, "leaki": 3, "leakyrelu": 6, "lear": 15, "learn": [5, 6, 7, 8, 9, 10, 11, 12, 14, 18, 21, 22], "learnabl": 5, "learner": 12, "learnig": 23, "learning_r": [10, 12], "learning_rate_init": [2, 3, 23], "learning_schedul": 15, "least": [2, 9, 10, 12, 13, 17, 18, 20], "leat": 15, "leav": [2, 3, 5, 7, 8, 11, 13, 23], "lectur": [2, 3, 7, 12, 13, 14, 15, 17, 18, 19, 21, 22], "lecturenot": [2, 17, 22, 23], "left": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "leftarrow": [10, 14], "legend": [0, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 23], "len": [1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 18, 23], "length": [1, 2, 3, 5, 6, 10, 11, 15, 17, 23], "length_of_sequ": 6, "leq": [2, 7, 9, 10, 15, 16, 20, 23], "less": [2, 3, 5, 6, 7, 8, 10, 11, 15, 17, 20, 23], "lessen": 3, "let": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "letter": [1, 2, 18, 20, 23], "level": [2, 3, 7, 8, 11, 17, 18, 19, 21, 23], "li": [10, 13], "liblinear": 12, "librari": [2, 3, 4, 5, 6, 7, 8, 11, 12, 13, 18, 20, 22], "licens": [2, 3, 17, 23], "lie": [2, 8, 13, 20, 23], "life": [2, 3, 10, 14, 23], "lifetim": 15, "like": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 17, 18, 20, 23], "likelihood": [2, 3, 7, 11, 23], "lim_": 20, "limit": [2, 7, 8, 10, 14, 18, 23], "lin_clf": 10, "lin_model": 2, "lin_reg": 11, "linalg": [2, 4, 7, 8, 10, 13, 15, 18, 20, 23], "line": [0, 1, 2, 5, 8, 10, 13, 15, 23], "line1": 10, "line2": 10, "line3": 10, "line_model": 0, "line_ms": 0, "line_predict": 0, "linear": [1, 3, 5, 7, 8, 9, 11, 12, 13, 14, 17, 20], "linear_model": [0, 1, 2, 7, 8, 9, 10, 11, 12, 13, 15, 23], "linear_regress": 8, "linearli": 7, "linearloc": [8, 15], "linearregress": [0, 1, 2, 8, 9, 11, 23], "linearsvc": 10, "liner": [3, 5], "linerar": 12, "linewidth": [2, 4, 6, 8, 10, 11, 12], "link": [0, 2, 6, 11, 14, 17, 19, 21, 23], "linlag": 7, "linpack": [18, 23], "linreg": [2, 23], "linspac": [1, 2, 4, 5, 6, 8, 10, 11, 12, 15, 18, 20, 23], "linu": 6, "linux": [2, 3, 17, 23], "liquid": [2, 23], "list": [0, 2, 3, 4, 5, 6, 11, 17, 23], "listedcolormap": [11, 12], "literatur": [3, 9, 16, 22], "littl": [3, 5, 11, 14], "live": [1, 10], "ll": [2, 20, 23], "lle": 2, "lloyd": [6, 16], "lmb": [2, 4, 7, 8], "lmbd": [2, 3, 5, 23], "lmbd_val": [2, 3, 5, 23], "lmbda": 15, "ln": [3, 15], "load": [2, 3, 6, 8, 9, 11, 12], "load_boston": 2, "load_breast_canc": [3, 9, 11, 12, 13], "load_data": [5, 6], "load_digit": [3, 5], "load_iri": [10, 11], "loc": [2, 5, 8, 9, 10, 11, 12, 23], "local": [0, 2, 3, 5, 9, 14, 15], "locat": [0, 4, 5, 10], "log": [0, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 15, 18, 23], "log10": [2, 7, 8], "log_": [2, 23], "log_clf": 12, "logarithm": [2, 7, 9, 18, 23], "logic": [2, 3, 11, 23], "login": 0, "logist": [2, 3, 4, 10, 11, 12, 13, 14, 15, 17], "logisticregress": [9, 11, 12, 13], "logit": 9, "logreg": [9, 11, 12, 13], "logspac": [2, 3, 5, 7, 8, 23], "long": [2, 3, 5, 6, 14, 15, 23], "longer": [4, 5, 10, 12, 16, 18, 20, 23], "loocv": 8, "look": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 18, 20, 23], "loop": [1, 3, 6, 8, 12, 14, 16, 17, 18, 23], "lose": 3, "loss": [2, 3, 5, 6, 7, 8, 9, 10, 12, 13, 15, 18, 23], "loss_fil": 6, "lossfil": 6, "lost": 6, "lot": [1, 2, 3, 6, 8], "low": [2, 8, 11, 12, 13, 23], "lower": [1, 2, 3, 5, 8, 11, 12, 18], "lowercas": [18, 23], "lowest": [11, 15, 20], "lr": [3, 5, 6, 12], "lstat": 2, "lstm": 6, "lstm_2layer": 6, "lstsq": [2, 23], "lt": 8, "lu": [2, 7, 23], "lubksb": 18, "luckili": 4, "ludcmp": 18, "lux": 18, "lvert": 3, "lw": [2, 23], "m": [0, 2, 3, 4, 5, 7, 8, 10, 11, 12, 13, 14, 15, 18, 20, 21, 22, 23], "m_": [11, 14], "m_1": 16, "m_h": [2, 23], "m_k": 16, "m_l": 14, "m_n": [2, 23], "m_p": [2, 23], "m_t": 15, "ma": 13, "machin": [0, 1, 3, 5, 6, 7, 8, 9, 11, 12, 13, 14, 18, 22], "machinelearn": [1, 2, 8, 17, 19, 21, 22, 23], "mackai": 22, "made": [2, 3, 5, 6, 7, 8, 9, 11, 13, 14, 23], "mae": [2, 23], "magic": 6, "magnitud": [3, 8, 9, 15], "mai": [2, 3, 4, 5, 7, 8, 9, 10, 11, 13, 14, 15, 17, 18, 20, 23], "mail": [19, 21], "main": [2, 3, 5, 6, 7, 8, 9, 11, 18, 22], "mainli": [2, 7, 8, 9, 11, 23], "maintain": 8, "major": [3, 8, 11, 12, 15, 18, 23], "make": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 13, 14, 15, 17, 18, 20, 22], "make_axes_locat": 8, "make_moon": [10, 11, 12], "make_pipelin": [2, 8, 12], "makedir": [2, 8, 9, 11, 23], "makeplot": [2, 23], "malcondit": 18, "malign": [3, 9, 11], "mammographi": 7, "manag": [0, 2, 4, 5, 17, 23], "mandatori": [21, 23], "mani": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 13, 15, 16, 17, 18, 20, 22, 23], "manifold": 13, "manner": 5, "manual": 8, "map": [2, 3, 4, 8, 9, 10, 13, 14, 16, 20, 23], "margin": [2, 7, 10], "marit": [2, 23], "mark": 23, "marker": [2, 9, 18, 23], "markov": [17, 23], "marsaglia": 20, "mass": [2, 3, 7, 15], "massag": [2, 23], "masses2016": [2, 23], "masses2016ol": [2, 23], "masses2016tre": 2, "masseval2016": [2, 23], "master": [19, 21], "mat": [17, 23], "mat1100": [17, 23], "mat1110": [17, 23], "mat1120": [17, 23], "match": [0, 3, 6, 7, 15, 16], "materi": [0, 6, 7, 9, 15, 18, 19, 21], "math": [5, 9, 14, 15, 18, 20, 22, 23], "mathbb": [2, 6, 7, 8, 9, 10, 13, 14, 15, 16, 18, 20, 23], "mathbf": [2, 7, 8, 9, 10, 15, 18, 23], "mathcal": [3, 7, 8, 9, 15], "matheemat": 5, "mathemat": [2, 8, 13, 14, 15, 17, 18, 20, 22, 23], "mathemati": 23, "mathrm": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 23], "matmul": [3, 4, 7], "matnat": 22, "matplotlib": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "matric": [1, 2, 3, 5, 6, 8, 9, 10, 13, 15, 17], "matrix": [2, 4, 5, 6, 8, 9, 10, 12, 15, 20], "matshow": 3, "matter": [4, 5, 15], "max": [2, 3, 4, 5, 6, 11, 12, 14, 15, 21, 23], "max_depth": [2, 11, 12], "max_diff": 4, "max_diff1": 4, "max_diff2": 4, "max_it": [2, 3, 10, 15, 23], "max_iter": 16, "max_leaf_nod": 12, "max_sampl": 12, "maxdegre": [2, 8, 12], "maxdepth": 12, "maxim": [3, 6, 7, 9, 10, 13], "maximum": [2, 4, 5, 7, 9, 10, 11, 12, 15, 16, 23], "maxpolydegre": [7, 8], "maxpooling2d": 5, "mbox": [7, 8], "mcculloch": 14, "md": 13, "mdoel": 6, "mean": [0, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "mean_absolute_error": [2, 23], "mean_divisor": 16, "mean_i": 20, "mean_matrix": 16, "mean_squared_error": [0, 2, 6, 8, 9, 12, 23], "mean_squared_log_error": [2, 23], "mean_vector": 16, "mean_x": 20, "meaning": [2, 6, 9, 23], "meansquarederror": [2, 23], "meant": [5, 9, 12, 15], "measur": [1, 2, 3, 4, 7, 8, 11, 13, 14, 16, 20, 23], "mechan": [2, 6, 20, 23], "median": [2, 23], "medicin": 14, "medium": [6, 10, 15], "medv": 2, "meet": [2, 21], "mehta": [2, 23], "memori": [5, 6, 13, 14, 15, 18], "mention": [2, 14, 15, 20, 23], "mere": [2, 23], "meshgrid": [4, 7, 8, 10, 11, 12, 13], "mess": 0, "messag": [7, 15], "messi": 4, "met": [2, 5, 10], "meteorolog": 11, "meter": 8, "method": [0, 1, 2, 3, 4, 5, 6, 7, 9, 10, 13, 14, 16, 17, 18, 20, 22], "metion": 8, "metric": [0, 2, 3, 5, 8, 9, 11, 12, 16, 23], "metropoli": [17, 23], "mev": [2, 20, 23], "mgd": 15, "mglearn": [17, 23], "mgrid": 15, "mi": 12, "mia": [21, 23], "michigan": 23, "microsoft": 22, "mid": 3, "midel": 6, "midpoint": 11, "might": [0, 2, 3, 4, 6, 8, 11, 15], "mild": 11, "millimet": 8, "million": [2, 23], "mimic": 14, "min": [2, 4, 7, 10, 11], "min_": [2, 4, 7, 16, 23], "min_samples_leaf": 11, "mind": [0, 2, 8, 15, 23], "mindboard": 6, "mine": [17, 23], "mini": [3, 13, 14, 15], "minibatch": [3, 13, 15], "minibathc": 15, "minim": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], "minima": [2, 3, 9, 15, 23], "minimum": [2, 3, 4, 8, 10, 11, 13, 15], "minmaxscal": 2, "minor": 20, "minst": 3, "minu": 9, "mirjalili": 23, "mirror": 11, "misc": 8, "misclassif": [10, 11, 12], "misclassifi": [10, 12], "miser": 2, "mismatch": 3, "miss": [2, 9, 12], "mistak": 6, "mit": 22, "mix": [3, 4, 23], "mixtur": 15, "mk": [11, 18], "mkdir": [2, 8, 9, 11, 23], "ml": [2, 3, 12, 15, 18], "mlab": 20, "mlbook": 23, "mle": [7, 9], "mlp": 3, "mlpclassifi": 3, "mlpregressor": [2, 23], "mm": 18, "mn": [14, 20], "mnist": [3, 13], "mod": 20, "mode": [19, 21, 23], "model": [1, 4, 5, 7, 9, 10, 11, 12, 13, 15, 16, 17, 20, 22], "model_select": [0, 1, 2, 3, 5, 7, 8, 9, 11, 12, 13, 23], "moder": 12, "modern": [2, 8, 9, 17, 23], "modif": [4, 14, 15], "modifi": [2, 3, 5, 7, 9, 10, 12, 14, 15, 23], "modul": [1, 2, 18, 23], "modular": 20, "modulo": 20, "moe": 13, "moment": [7, 8, 15, 20], "mondai": [21, 23], "monitor": 15, "monoton": [7, 14, 20], "mont": [2, 8, 17, 20, 22, 23], "montli": 1, "moor": [7, 8], "more": [1, 2, 3, 4, 6, 7, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20], "moreov": [2, 5], "morten": [21, 23], "mortenhj": 23, "most": [0, 1, 2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20, 23], "mostli": [3, 13], "motion": [2, 15], "motiv": [3, 6], "move": [0, 1, 2, 6, 7, 8, 9, 11, 14, 15, 16, 20], "mpl": [2, 9, 23], "mpl_toolkit": [4, 8, 15], "mplot3d": [4, 8, 15], "mplregressor": 3, "mse": [0, 1, 2, 6, 7, 8, 11, 12, 23], "mse_simpletre": 12, "mselassopredict": 7, "mselassotrain": 7, "mseownridgepredict": 8, "msepredict": 7, "mseridgepredict": [2, 7, 8], "msetrain": 7, "msle": [2, 23], "mt": [9, 14], "mu": [2, 8, 13, 15, 20, 23], "mu0": 20, "mu1": 20, "mu2": 20, "mu_": [8, 20], "mu_i": 8, "mu_n": 13, "mu_x": 20, "much": [0, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 18, 20, 23], "multi": [2, 3, 5, 9, 17, 23], "multiclass": [3, 9], "multidimension": [13, 14, 23], "multilay": 3, "multinomi": 9, "multipl": [0, 4, 6, 7, 8, 9, 14, 15, 20], "multipli": [5, 7, 8, 13, 15, 18, 20], "multiplum": 10, "multivari": [2, 4, 12, 13, 17, 20, 23], "multivariate_norm": [13, 16], "multpli": 1, "murphi": [13, 22, 23], "must": [0, 3, 4, 7, 8, 10, 12, 14, 15, 16, 20], "mutat": 9, "mutual": [3, 5, 8, 15], "mx_": 20, "my": 23, "myriad": [2, 17, 23], "mz1": 20, "mz2": 20, "m\u00f8svatn": 8, "n": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 18, 20, 23], "n1": 18, "n2": 18, "n_": [3, 4, 5, 10, 14, 20], "n_0": [14, 20], "n_boostrap": [8, 12], "n_bootstrap": 8, "n_categori": [3, 5], "n_cluster": 16, "n_compon": 13, "n_epoch": 15, "n_estim": 12, "n_examples_to_gener": 6, "n_featur": 3, "n_filter": 5, "n_hidden": 4, "n_hidden_neuron": [2, 3, 23], "n_i": 20, "n_input": [2, 3, 5], "n_instanc": 11, "n_job": 12, "n_k": 16, "n_l": [14, 20], "n_layer": 3, "n_m": 11, "n_neuron": 3, "n_neurons_connect": 5, "n_neurons_layer1": 3, "n_neurons_layer2": 3, "n_point": 16, "n_sampl": [8, 10, 11, 12, 16], "n_split": 8, "n_step": 6, "n_t": 4, "n_x": 4, "nabla": [3, 15], "nabla_": [4, 15], "nabla_w": 15, "nag": 15, "naimi": [2, 23], "naiv": 9, "naive_kmean": 16, "name": [0, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 20, 21, 23], "narrow": 15, "nation": [3, 7], "nativ": [17, 23], "natur": [2, 3, 6, 10, 11, 14, 15, 20, 22, 23], "navier": 14, "navig": 0, "nb": 20, "nb_": 18, "nbconvert": 23, "nd": 16, "ndarrai": 8, "ne": [11, 12, 18, 20], "nearest": [3, 5, 8, 13], "nearli": 15, "neat": 23, "neccesari": 8, "necess": 4, "necessari": [2, 3, 5, 6, 10, 16, 23], "necessarili": [2, 6, 13, 20, 23], "necesserali": 7, "neck": 9, "need": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20], "neg": [2, 3, 5, 7, 8, 9, 12, 15, 18, 20, 23], "neg_mean_squared_error": 8, "neglect": 20, "neglig": 20, "neighbor": [5, 8, 13], "neither": [6, 15], "neq": [15, 16, 20], "nervou": 14, "nest": [11, 14], "nesterov": 15, "net": [4, 6, 14], "netlib": [18, 23], "network": [2, 11, 15, 17, 22], "neural": [2, 15, 17, 22], "neural_network": [2, 3, 4, 23], "neuralnetwork": 3, "neuron": [3, 4, 5, 6, 14], "neutral": [2, 23], "neutron": [2, 23], "never": [3, 6, 8, 11, 20], "new": [0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 23], "new_chang": 15, "new_hobbit": 23, "newaxi": [2, 5, 8, 11], "newli": [2, 23], "newton": [3, 9, 10, 15, 20], "next": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 15, 16, 23], "next_guess": 15, "next_input": 6, "ng": 3, "ni": 16, "nice": [2, 3, 7, 13, 23], "niter": 15, "nitric": 2, "nlambda": [2, 7, 8], "nlp": 22, "nm": 20, "nm_n": [2, 23], "nmse": 8, "nn": [4, 7, 8, 14, 18], "nn_model": 3, "nnmin": 4, "node": [3, 5, 11, 12, 14], "nois": [2, 6, 7, 8, 10, 11, 12, 15, 23], "noise_dimens": 6, "noisi": [3, 8], "non": [2, 3, 5, 7, 8, 9, 11, 12, 13, 14, 15, 16, 18, 20, 23], "none": [2, 3, 4, 6, 7, 11, 12, 15, 20, 23], "nonlinear": [5, 8, 10, 11, 13, 14], "nonneg": [8, 11, 15], "nonparametr": 8, "nonsens": 20, "nonsingular": 18, "nonumb": [5, 9, 10, 15, 18], "nor": [3, 6, 15], "norm": [2, 3, 7, 8, 10, 13, 15, 23], "normal": [1, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 20, 23], "normali": [18, 23], "norwai": [8, 23], "notat": [2, 4, 7, 8, 15, 16, 20, 23], "note": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 13, 14, 15, 16, 17, 18, 20, 22, 23], "notebook": [0, 1, 2, 3, 5, 11, 17, 23], "noth": [3, 4, 7, 10, 14, 16, 20], "notic": [6, 7, 14, 15, 18, 20, 23], "notion": 5, "novel": [5, 8, 12], "novemb": [3, 21, 23], "now": [0, 1, 2, 4, 6, 7, 8, 9, 10, 12, 13, 14, 16, 17, 18, 20, 23], "nowadai": [2, 3, 5, 11, 17, 23], "nox": 2, "np": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 23], "npr": 4, "nsampl": 8, "nt": 4, "nu": 20, "nuclear": 7, "nuclei": [2, 20, 23], "nucleon": [2, 23], "nucleu": [2, 23], "num": 6, "num_coordin": 4, "num_hidden_neuron": 4, "num_it": 4, "num_neuron": 4, "num_neurons_hidden": 4, "num_point": 4, "num_tre": 12, "num_valu": 4, "number": [1, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 21, 23], "numberid": 9, "numberparamet": 5, "numer": [2, 7, 8, 11, 12, 13, 14, 15, 17, 18, 22, 23], "numpi": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 20], "nunmpi": 7, "nx": 4, "ny": 20, "o": [2, 3, 6, 7, 8, 9, 10, 11, 13, 18, 21, 22, 23], "obei": [8, 13, 15], "object": [0, 2, 3, 6, 10, 12, 18, 23], "obliqu": 7, "observ": [2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20], "obtain": [2, 3, 7, 8, 9, 10, 11, 12, 14, 15, 16, 18, 20, 23], "obviou": [7, 8, 13, 20], "obviouli": 23, "obvious": [2, 6, 7, 8, 18, 23], "occupi": 2, "occur": [2, 8, 10, 11, 18, 20, 23], "octob": [21, 23], "od": 2, "odd": [2, 5, 9, 23], "odenum": 4, "odesi": 4, "oen": 2, "off": [3, 5, 6, 7, 11, 15, 20], "offer": [8, 13, 17, 18, 19, 21, 23], "offic": [21, 23], "offici": [19, 23], "often": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "ofter": [18, 23], "ol": [2, 15, 23], "old": [0, 3, 7, 12, 15], "ols_paramet": 1, "ols_sk": 8, "ols_svd": 8, "olsbeta": [2, 7], "omega": [4, 5, 8], "omega_0": 5, "omit": [2, 7, 23], "onc": [3, 8, 11, 13, 15], "one": [0, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, 20, 21, 23], "onehot": 3, "onehot_vector": 3, "onehotencod": 11, "ones": [1, 2, 4, 7, 8, 10, 11, 12, 13, 15, 18, 23], "ones_lik": 6, "onl": 5, "onli": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "onlin": [0, 13, 19], "onto": [7, 13], "open": [0, 2, 3, 6, 8, 9, 11, 17, 19, 21, 23], "oper": [0, 1, 2, 3, 5, 7, 8, 12, 13, 14, 15, 17, 20, 23], "operation": 20, "oplu": 20, "opmiz": 15, "opportun": 2, "oppos": [8, 15], "opposit": [3, 7, 10], "opt": [3, 7, 23], "optim": [1, 2, 4, 5, 6, 7, 8, 9, 11, 12, 13, 16], "optimis": [3, 5], "option": [0, 2, 3, 5, 7, 8, 10, 13, 18], "optmiz": [3, 10, 15], "oral": 23, "orang": 2, "order": [0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 18, 20, 23], "ordinari": [2, 4, 5, 9, 13, 15, 17], "oreilli": [22, 23], "org": [1, 2, 5, 6, 17, 18, 22, 23], "organ": [8, 9, 12, 18], "orient": [3, 7, 20], "origin": [0, 2, 5, 7, 8, 10, 13, 14, 15, 18, 23], "orthogn": 7, "orthogon": [2, 7, 8, 10, 13, 15, 18, 23], "orthonorm": 7, "os": [21, 23], "oscar": 3, "oscil": [5, 15], "oslo": [2, 17, 19, 21, 23], "osx": [2, 17, 23], "other": [1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 15, 16, 17, 19, 20, 21, 22], "otherwis": [2, 3, 6, 9, 15, 18, 23], "ouput": [7, 9, 14], "our": [0, 1, 3, 4, 5, 8, 9, 10, 11, 12, 14, 16, 17, 18, 20], "ourmodel": 2, "ourselv": [2, 7, 8, 10, 13, 15, 23], "out": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 20, 23], "out_fil": 11, "outcom": [2, 9, 11, 12, 14, 20], "outdoor": 11, "outer": [8, 14, 15], "outfil": 6, "outlier": [2, 10, 23], "outlin": [8, 12, 13], "outlook": 11, "outperform": 12, "output": [2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 18, 20, 23], "output_bia": 3, "output_bias_gradi": 3, "output_shap": 6, "output_weight": 3, "output_weights_gradi": 3, "outputlayer1": 14, "outputlayer2": 14, "outsid": 6, "over": [0, 1, 2, 3, 5, 6, 7, 8, 11, 12, 14, 15, 18, 23], "over1": 15, "overal": [3, 12], "overcast": 11, "overcom": [14, 15], "overdetermin": [2, 23], "overfit": [2, 3, 5, 8, 11, 12, 15], "overflow": 7, "overhead": 14, "overlap": [5, 9, 10, 11], "overlin": [2, 7, 8, 11, 12, 13, 16, 18, 23], "overst": 2, "overtrain": 6, "overview": 5, "own": [1, 6, 7, 8, 10, 14, 15, 17, 18], "owner": 2, "ownmsepredict": 2, "ownmsetrain": 2, "ownridgebeta": [2, 8], "ownypredictridg": 2, "ownytilderidg": 2, "oxid": 2, "p": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 23], "p0": 4, "p1": 4, "p_": [4, 6, 10, 11], "p_hidden": 4, "p_i": [7, 20], "p_j": 20, "p_n": 20, "p_output": 4, "p_x": 20, "pack": [2, 23], "packag": [0, 2, 3, 5, 6, 7, 10, 13, 15, 17, 20], "packtpub": 23, "packtpublish": 23, "pad": [5, 6], "page": [2, 17, 23], "pai": [0, 2, 3, 11, 15], "pair": [2, 4, 5, 11, 17, 20, 23], "panda": [2, 6, 7, 8, 9, 11, 13, 17], "panel": 23, "paper": 3, "paradigm": [2, 23], "parallel": [12, 15, 17, 18, 23], "param": 4, "paramat": 4, "paramet": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 20], "parameter": [2, 8, 12, 23], "parametr": [2, 8, 23], "paramt": [5, 7], "part": [2, 3, 5, 7, 8, 12, 18, 19, 20, 21, 23], "partial": [1, 2, 3, 7, 8, 9, 10, 12, 13, 14, 15, 20, 23], "particip": [17, 19, 21, 23], "particl": [2, 6, 15, 20, 23], "particular": [1, 2, 3, 4, 5, 7, 8, 11, 12, 13, 14, 15, 20, 22, 23], "particularli": [7, 8, 10, 13, 15, 20], "partit": [3, 6, 11], "partli": [8, 23], "partner": 0, "pass": [4, 5, 14, 16], "past": [12, 20], "patch": [8, 20], "path": [2, 6, 8, 9, 11, 17, 23], "patient": 9, "patter": 6, "pattern": [2, 5, 6, 14, 22, 23], "pauli": [2, 23], "pc": [13, 17], "pca": [2, 9, 17, 23], "pd": [2, 6, 7, 8, 9, 11, 13, 23], "pde": 4, "pdf": [0, 1, 2, 5, 6, 7, 8, 11, 22, 23], "pedagog": [2, 23], "penal": 8, "penalti": [8, 15], "penros": [7, 8], "pentagon": 15, "peopl": [2, 3, 11, 15, 17], "per": [2, 3, 8, 19, 21, 23], "percentag": [2, 12, 13, 21], "perceptron": [2, 3, 9, 23], "peregrin": 23, "perfect": [2, 3, 15, 23], "perfectli": [6, 8], "perform": [1, 2, 4, 5, 6, 7, 8, 10, 12, 13, 14, 15, 16, 17, 18, 20, 23], "performac": 6, "perhap": [2, 7, 15, 23], "perimet": 3, "period": [3, 6, 20], "permiss": 0, "permut": 13, "persist": 15, "person": [1, 7, 8, 9, 19, 21, 23], "perspect": 22, "pertin": [14, 23], "petal": [10, 11], "peter": 22, "phantom": 20, "phase": [8, 14], "phenomena": 20, "phi": 10, "phi_k": 10, "philosophi": 15, "phone": [21, 23], "photo": 6, "phrase": [2, 23], "physic": [2, 3, 6, 9, 14, 15, 20, 21, 22, 23], "pi": [4, 5, 7, 8, 9, 11, 14, 15, 20], "pick": [3, 11, 12, 13, 15, 16], "pickl": 3, "pictur": [2, 23], "pie": [17, 23], "piec": [13, 16], "pillow": [2, 17, 23], "pinv": [7, 8, 15], "pip": [0, 2, 3, 17, 23], "pip3": [2, 3, 23], "pipelin": [2, 8, 10, 12], "pippin": 23, "pit": 6, "pitfal": 8, "pitt": 14, "pixel": [3, 5, 6], "pixel_height": [3, 5], "pixel_width": [3, 5], "place": [0, 2, 6, 8, 10, 15, 18, 23], "plai": [2, 5, 6, 7, 8, 10, 13, 17, 23], "plain": [10, 12, 14, 15, 16], "plan": [8, 11, 21, 22, 23], "plane": [10, 11], "plateau": 7, "platform": [17, 23], "plausibl": 14, "pleas": [15, 21, 23], "plenti": 3, "plethora": [5, 14], "plot": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "plot_confusion_matrix": [9, 12], "plot_count": 8, "plot_cumulative_gain": [9, 12], "plot_data": 3, "plot_dataset": 10, "plot_decision_boundari": [11, 12], "plot_import": 12, "plot_max": 6, "plot_min": 6, "plot_model": 6, "plot_numb": 6, "plot_predict": 10, "plot_regression_predict": 11, "plot_result": 6, "plot_roc": [9, 12], "plot_surfac": [4, 8, 15], "plot_train": 11, "plot_tre": [11, 12], "plt": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "plu": [2, 5, 7, 9, 23], "pm": 10, "pmatrix": 4, "pml": 22, "pn": 5, "png": [2, 6, 8, 9, 11, 23], "point": [2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 21, 23], "point_1": 6, "point_2": 6, "poisson": [17, 20, 23], "poli": [8, 10], "poly100_kernel_svm_clf": 10, "poly3": 2, "poly3_plot": 2, "poly_featur": [0, 10, 11], "poly_features10": 11, "poly_fit": 11, "poly_fit10": 11, "poly_kernel_svm_clf": 10, "poly_model": 0, "poly_ms": 0, "poly_predict": 0, "polydegre": [2, 7, 8, 12], "polygon": 15, "polym": 14, "polynomi": [0, 2, 7, 8, 9, 10, 11, 12, 13, 23], "polynomial_featur": [0, 1, 8], "polynomial_svm_clf": 10, "polynomialfeatur": [0, 1, 2, 8, 10, 11], "polytrop": [2, 8, 23], "pool": 5, "pool_siz": 5, "poor": [3, 15], "poorli": 2, "popul": [2, 7, 23], "popular": [2, 3, 5, 8, 9, 10, 11, 13, 14, 17, 18, 20], "popularli": [2, 23], "portabl": 12, "portion": [13, 15], "pose": [2, 6, 7, 8, 13, 20, 23], "posit": [2, 3, 4, 5, 7, 9, 10, 12, 13, 15, 16, 18, 20, 23], "possibl": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 20, 21, 23], "possibli": [8, 10, 15], "posterior": 7, "postpon": [2, 23], "postul": 7, "potenti": [2, 5, 7, 8, 14, 15, 23], "pott": 14, "power": [2, 3, 7, 8, 10, 11, 14, 15, 23], "pp": [7, 8], "practic": [1, 2, 7, 8, 9, 10, 20], "practition": [2, 3, 5, 23], "pre": 23, "preced": [3, 13, 14, 20], "preceed": 6, "preceq": 10, "precis": [2, 4, 7, 13, 15, 18, 20, 23], "pred": 8, "predicit": 2, "predict": [0, 1, 2, 3, 7, 8, 9, 10, 11, 12, 17, 22, 23], "predict_prob": 3, "predict_proba": [9, 12], "predictor": [2, 7, 8, 9, 11, 12, 13, 23], "prefer": [2, 3, 8, 10, 11, 13, 15, 17, 23], "prepar": [2, 8, 18, 23], "preprocess": [0, 1, 2, 6, 8, 9, 10, 11, 12, 13], "prerequisit": 2, "presenc": 15, "present": [2, 7, 8, 9, 11, 14, 15, 18, 20, 23], "preserv": [5, 13, 18], "press": [0, 15, 22], "pretrain": [3, 6], "pretti": [2, 6, 10, 11, 17, 23], "prev_centroid": 16, "prevent": [15, 20], "previou": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 13, 14, 15, 18, 20], "previous": [4, 5, 11, 12, 20], "price": [2, 6, 11, 15], "primal": 10, "primari": [2, 9, 23], "prime": 20, "princip": [2, 7, 9, 17, 23], "principl": [2, 8, 9, 10, 16, 23], "print": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 20, 23], "print_funct": [10, 11], "printout": [2, 23], "prior": [2, 7, 8, 23], "privat": 2, "prob": [3, 20], "probabilist": [2, 22, 23], "probabl": [2, 3, 5, 6, 8, 9, 12, 15, 17, 23], "problem": [2, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 17, 18, 20], "probml": 22, "proce": [2, 7, 8, 9, 10, 11, 12, 13, 15, 18, 23], "procedur": [4, 6, 7, 8, 10, 12, 13, 15], "proceed": 18, "process": [2, 4, 6, 8, 11, 12, 14, 15, 17, 18, 20, 22, 23], "prod": 22, "prod_": [3, 7, 9], "produc": [2, 5, 6, 7, 8, 11, 12, 13, 14, 15, 17, 18, 20, 23], "product": [1, 2, 3, 5, 7, 8, 9, 10, 14, 15, 17, 18, 23], "profess": [2, 23], "program": [0, 2, 3, 6, 7, 8, 10, 14, 16, 17, 18, 19, 20, 21, 23], "programm": 18, "progress": [3, 6, 16], "prohibit": 8, "project": [0, 2, 3, 4, 5, 7, 13, 15, 17, 19], "project_root_dir": [2, 8, 9, 11, 23], "promin": 14, "promis": 10, "promot": [21, 23], "prone": [0, 11], "pronounc": [15, 17, 23], "proof": [2, 13, 14, 15, 23], "propag": [4, 5, 15], "proper": [2, 4, 8, 9], "properli": [3, 8, 10, 12, 15], "properti": [1, 2, 3, 5, 14, 15, 18, 23], "proport": [2, 3, 7, 11, 13, 15, 20, 23], "propos": [3, 6, 8, 12, 23], "propto": [7, 15], "proton": [2, 23], "prove": [5, 15], "provid": [2, 3, 5, 6, 7, 8, 10, 11, 12, 14, 15, 17, 18, 20, 23], "proxi": [3, 15], "prune": 11, "pseudo": [18, 20], "pseudoinv": 7, "pseudoinvers": [7, 8], "pseudorandom": [8, 20], "psychologi": [2, 23], "pt": 15, "public": [0, 2, 17, 23], "pull": 0, "punish": [2, 3, 23], "pure": [5, 11, 20], "purest": 11, "puriti": 11, "purpos": [2, 5, 12, 14, 16, 23], "push": 0, "put": 3, "py": 7, "pycod": 23, "pydata": 17, "pydot": 11, "pyhton2": 23, "pylab": [2, 9, 23], "pypi": 17, "pyplot": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "pythagora": 7, "python": [3, 4, 5, 7, 8, 10, 13, 14, 15, 16, 20], "python2": [2, 23], "python3": [2, 17, 23], "pytorch": [2, 17, 23], "q": [7, 8, 10, 13, 20], "qp": 10, "qquad": [4, 13, 15, 18], "qr": [7, 8, 18], "quad": [3, 15, 18], "quadrat": [2, 10, 11, 15, 23], "qualit": [6, 11, 20], "qualiti": [2, 11, 17, 23], "quantifi": 3, "quantil": 12, "quantit": [2, 8, 11, 23], "quantiti": [1, 2, 4, 7, 8, 9, 11, 12, 13, 14, 16, 18, 20, 23], "quantum": [6, 14, 22, 23], "quartil": 2, "quench": 7, "queri": 11, "question": [2, 7, 8, 11, 13, 14, 15, 21, 23], "qugan": 6, "quick": [6, 20], "quickli": [3, 5, 11, 13, 15], "quit": [0, 3, 7, 8, 11, 12, 14], "quot": 6, "r": [0, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20], "r2": [2, 7, 8, 23], "r2_score": [2, 23], "r2score": [2, 23], "r_1": 11, "r_2": 11, "r_j": 11, "r_m": 11, "rad": 2, "radial": [2, 10, 14], "radioact": 20, "radiu": [2, 3], "rain": 11, "ramp": 3, "ran0": 20, "ran1": 20, "ran2": 20, "ran3": 20, "rand": [0, 2, 6, 7, 8, 11, 12, 15, 18, 23], "randint": [8, 11, 15], "randn": [0, 2, 3, 4, 7, 8, 11, 13, 15, 23], "random": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 15, 16, 17, 18, 23], "random_forest_model": 12, "random_index": 15, "random_indic": [3, 5], "random_st": [2, 9, 10, 11, 12, 13], "randomforestclassifi": 12, "randomli": [3, 8, 11, 15, 16], "rang": [2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 16, 18, 20, 23], "rangl": [2, 8, 13, 20, 23], "rangle_x": 20, "rank": 7, "rankdir": 6, "raphson": [3, 10, 15], "rapidli": 2, "rare": [3, 15, 23], "raschka": 23, "rasckha": 23, "rate": [2, 3, 4, 5, 6, 10, 11, 12, 14, 15], "rather": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "ratio": [6, 9, 11, 12, 13], "rational": [2, 23], "ravel": [7, 8, 9, 10, 11, 12, 13, 15, 18], "raw": 5, "rbf": [10, 13, 14], "rbf_kernel_svm_clf": 10, "rbf_pca": 13, "rc": [2, 20], "rcond": [2, 23], "rcparam": [2, 3, 5, 9, 10, 11, 12, 20, 23], "re": [0, 4, 6, 15], "reach": [3, 6, 7, 8, 11, 12, 14, 15, 16], "read": [1, 2, 4, 5, 6, 7, 8, 9, 10, 13, 14, 18, 20, 22], "read_csv": [2, 8, 9, 11, 23], "read_fwf": [2, 23], "reader": [2, 8, 18, 20, 23], "readi": [2, 3, 7, 8, 10, 12, 13, 14, 18, 23], "readili": 3, "readm": 0, "readthedoc": 17, "real": [1, 2, 3, 6, 9, 12, 13, 14, 18], "real_loss": 6, "real_output": 6, "realist": 10, "realiti": 20, "realiz": [3, 14], "realli": [2, 3, 23], "rearrang": 15, "reason": [2, 3, 5, 6, 12, 15, 22, 23], "reassign": 3, "recal": [7, 8, 11, 12, 13, 14, 18, 20, 23], "recast": 5, "receiv": [3, 5, 12, 14, 20], "recent": [2, 8, 15, 22], "recept": [5, 14], "receptive_field": 5, "recip": [2, 8, 9, 18, 23], "reciproc": 7, "recogn": [2, 6, 7, 12, 23], "recognit": [2, 3, 5, 14, 22, 23], "recommen": 23, "recommend": [0, 2, 4, 5, 6, 7, 8, 10, 15, 17, 18, 22], "reconsid": 11, "reconstruct": 13, "record": [12, 19, 21, 23], "recreat": 0, "rectangl": [11, 15], "rectangular": 7, "rectifi": [3, 5, 14], "recur": [2, 17, 23], "recurr": [2, 3, 17, 23], "recurs": [11, 17, 18, 23], "red": [2, 5, 6, 8, 10, 11], "redefin": [2, 12, 23], "reduc": [3, 5, 7, 8, 11, 12, 13, 15, 23], "reduct": [2, 12, 13, 17, 20, 23], "refer": [2, 3, 4, 5, 7, 8, 13, 14, 15, 16, 18, 22, 23], "referenc": 4, "refin": 14, "refit": 8, "reflect": [2, 3, 6, 7, 20, 23], "refresh": [17, 23], "refreshprogrammingskil": 23, "reg": [12, 13], "regard": [3, 11, 15], "regardless": [1, 14], "region": [5, 6, 8, 11, 14], "regist": [8, 20], "reglasso": 7, "regr_1": [2, 11], "regr_2": [2, 11], "regr_3": [2, 11], "regress": [1, 3, 10, 13, 14, 17, 18], "regressor": [2, 9, 12, 23], "regridg": [2, 7, 8], "regular": [2, 5, 6, 7, 8, 9, 11, 15, 21, 23], "regularli": 0, "reilli": [2, 22, 23], "reinforc": [2, 10, 17, 23], "reiter": 3, "reject": 9, "rel": [2, 6, 8, 9, 11, 14, 15, 20, 23], "relat": [2, 3, 5, 6, 7, 13, 15, 16, 18, 20, 23], "relationship": [2, 6, 11, 23], "relativeerror": [2, 23], "releas": [3, 17, 23], "relev": [2, 3, 7, 9, 13, 17, 20, 23], "reli": [2, 8, 10], "reliabl": [9, 20], "relu": [5, 6, 23], "remain": [3, 4, 6, 8, 14, 18, 20], "remaind": 20, "reman": 4, "remark": 3, "rememb": [2, 10, 15, 18, 23], "remind": [2, 7, 13, 15, 18, 20], "remot": 0, "remov": [2, 6, 7, 8], "renam": 0, "render": [2, 23], "reorder": [7, 9], "reorgan": [2, 23], "repeat": [2, 3, 5, 6, 7, 8, 11, 12, 13, 15, 16, 18, 20, 23], "repeated": 23, "repeatedli": [2, 8, 12, 15], "repet": 5, "repetit": [8, 23], "rephras": 15, "replac": [2, 3, 5, 6, 7, 8, 12, 14, 16, 17, 23], "replica": 8, "repo": 0, "report": 23, "repositori": [2, 6, 23], "repres": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 20, 23], "represent": [2, 3, 5, 8, 20, 23], "representd": 5, "reproduc": [0, 1, 2, 7, 8, 11, 14, 17, 20, 23], "repuls": [2, 23], "request": [2, 15], "requir": [0, 2, 3, 5, 6, 7, 8, 10, 11, 13, 14, 15, 18, 23], "res1": 4, "res2": 4, "res3": 4, "res_analyt": 4, "res_analytical1": 4, "res_analytical2": 4, "res_analytical3": 4, "resaml": 8, "resampl": [2, 9, 12, 17, 23], "rescal": [2, 13, 14], "rescu": 7, "reseach": 8, "research": [2, 6, 15, 17, 22, 23], "resembl": [8, 20], "reserv": [3, 7, 8, 20], "reshap": [2, 3, 4, 5, 6, 8, 10, 11, 12, 18, 23], "residenti": 2, "residu": [2, 7, 15, 23], "resiz": 7, "resourc": 23, "respect": [1, 2, 3, 4, 5, 7, 8, 9, 10, 12, 13, 14, 15, 16, 20, 23], "respond": 14, "respons": [2, 9, 11, 14, 23], "rest": [2, 7], "restat": [2, 14, 23], "restor": 6, "restored_discrimin": 6, "restored_gener": 6, "restrict": [2, 5, 11, 14, 23], "result": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "retail": 2, "retain": [7, 8], "retriev": 23, "return": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 15, 16, 18, 20, 23], "return_data": 16, "return_sequ": 6, "return_x_i": 11, "reus": [3, 5, 8], "reveal": [2, 14, 23], "revers": [3, 18], "review": [17, 18], "revisit": 16, "revolut": 23, "reward": [2, 6, 23], "rewrit": [1, 2, 5, 7, 8, 9, 10, 12, 13, 14, 15, 18, 20], "rewritten": [4, 8, 10, 12, 20], "rewrot": 15, "rf": 12, "rgb": 5, "rgoj5yh7evk": 17, "rh": 8, "rho": [2, 12, 15, 23], "rho_1": 12, "rho_2": 12, "rho_m": 12, "rich": [2, 23], "ride": 11, "rideclass": 11, "ridedata": 11, "ridg": [9, 13, 15, 17, 23], "ridge_sk": 8, "ridgebeta": 7, "right": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 16, 18, 20, 23], "right_sid": 4, "rightarrow": [2, 3, 7, 8, 10, 13, 14, 15, 20, 23], "rigor": [2, 23], "ring": 8, "rise": [2, 23], "risk": [2, 15, 23], "rival": 6, "river": 2, "rlm": 23, "rm": [2, 20], "rmse": 2, "rmsporp": 15, "rmsprop": [3, 5, 6, 15], "rnd_clf": 12, "rng": 20, "rnn": [6, 14], "rnn1": 6, "rnn2": 6, "rnn_2layer": 6, "rnn_input": 6, "rnn_output": 6, "rnn_train": 6, "rntrick1": 20, "rntrick2": 20, "rntrick3": 20, "rntrick4": 20, "ro": [2, 15, 23], "robert": 22, "robust": [2, 23], "robustscal": 2, "roc": [9, 12], "role": [2, 4, 7, 8, 10, 17, 23], "roll": 8, "room": [2, 21, 23], "root": [0, 2, 7, 11, 15, 20], "rot": 23, "rotat": [3, 10, 11, 12], "rotation_matrix": 11, "roughli": [3, 5], "round": [2, 9, 11, 15], "routin": [15, 18, 23], "row": [1, 2, 3, 4, 7, 8, 11, 13, 18, 23], "rr": 7, "rrr": 7, "rug": 15, "rule": [2, 3, 7, 8, 15, 23], "run": [0, 2, 3, 4, 6, 7, 8, 10, 11, 13, 15, 17, 23], "runtim": [0, 3, 8, 16], "rust": [2, 17, 18, 23], "rvert": 3, "rvert_2": 3, "s_": [5, 8], "s_1": 8, "s_i": [8, 9], "s_j": 8, "s_k": 8, "saddl": 15, "sai": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 18, 20, 23], "said": [8, 11, 15], "sake": [2, 7, 9, 13, 23], "sale": [2, 23], "sam": 23, "same": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 14, 16, 18, 20, 23], "samm": 12, "sampl": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 16, 17, 18, 20, 23], "sample_vari": 16, "sampleexptvari": 20, "samwis": 23, "sastri": 13, "satisfactori": [2, 23], "satisfi": [3, 4, 5, 8, 10, 15, 18, 20], "satur": [3, 8], "save": [2, 6, 8, 9, 11, 15, 23], "save_fig": [2, 8, 9, 11, 12, 23], "savefig": [2, 6, 8, 9, 11, 20, 23], "savetxt": 6, "saw": 7, "scalabl": 12, "scalar": [4, 7, 8, 12], "scale": [2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 21, 23], "scale_mean": 6, "scale_std": 6, "scaler": [2, 9, 10, 11, 12, 13], "scan": [7, 9], "scari": 7, "scatter": [0, 2, 3, 8, 9, 10, 11, 16, 23], "scenario": [8, 15], "schedul": 15, "scheme": [3, 15], "schrage": 20, "sch\u00f8yen": 8, "scienc": [2, 3, 12, 14, 15, 17, 19, 20, 21, 22], "scientif": [2, 17, 23], "scientist": [2, 23], "scikit": [0, 1, 5, 7, 8, 10, 11, 12, 15, 17, 18, 22], "scikit_learn": 2, "scikitlearn": 23, "scikitplot": [9, 12], "scipi": [2, 5, 7, 8, 15, 17, 18, 23], "scl": 8, "scm": 0, "score": [1, 2, 3, 5, 8, 9, 11, 12, 13, 21, 23], "scores_kfold": 8, "scratch": [1, 3, 15], "sdg": 15, "seaborn": [2, 3, 5, 8, 9, 23], "seamless": [2, 17, 23], "search": [0, 2, 3, 5, 7, 11, 15, 23], "sebastian": 23, "sebastianraschka": 23, "sec": 8, "second": [0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 16, 17, 18, 20, 21, 23], "secondeigvector": 13, "secondli": 14, "section": [1, 6, 13, 18, 20], "sector": 2, "see": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 17, 18, 20, 23], "seed": [2, 3, 4, 5, 6, 7, 8, 10, 11, 13, 15, 16, 20, 23], "seed_imag": 6, "seek": [3, 4, 10], "seem": [3, 5, 6], "seemingli": [2, 23], "seen": [2, 3, 5, 7, 12, 14, 20, 23], "segment": 15, "seismic": 8, "seldomli": [2, 23], "select": [0, 3, 7, 8, 10, 11, 12, 13, 19, 20, 21, 22, 23], "selevet": 0, "self": [3, 7], "sell": 6, "semest": [9, 19], "semi": [10, 15], "semilogx": 8, "send": [7, 14, 15, 21, 23], "senior": [19, 21], "sens": [2, 6, 8, 10, 23], "sensibl": 5, "sensit": [2, 7, 8, 11, 15, 23], "sent": 4, "sentenc": [6, 14], "separ": [2, 3, 4, 6, 8, 10, 11, 14, 16, 17, 20, 23], "septemb": 23, "sequenc": [5, 6, 9, 11, 12, 14, 15, 17, 18, 20, 23], "sequenti": [3, 5, 6, 12, 14, 20], "seri": [2, 3, 4, 5, 6, 7, 8, 12, 13, 14, 15, 18, 23], "serif": [2, 9, 20, 23], "serv": [2, 3, 4, 5, 7, 9, 15, 22, 23], "session": [0, 3, 19, 21, 23], "set": [1, 3, 6, 7, 8, 9, 10, 12, 13, 15, 16, 17, 18, 20, 21], "set_major_formatt": 8, "set_major_loc": 8, "set_tick": [3, 10], "set_ticklabel": 3, "set_titl": [2, 3, 4, 5, 9, 14, 16, 23], "set_xlabel": [2, 3, 4, 5, 9, 14, 23], "set_xlim": [9, 14], "set_xticklabel": 3, "set_ylabel": [2, 3, 4, 5, 9, 23], "set_ylim": [9, 14], "set_ytick": 9, "set_yticklabel": [3, 8], "set_zlim": 8, "seth": 6, "setminu": 8, "setosa": [10, 11], "setosa_or_versicolor": 10, "setp": 8, "setup": [3, 6, 8, 10, 17, 23], "sever": [1, 2, 5, 7, 8, 9, 10, 11, 13, 14, 15, 17, 18, 20, 23], "sgd": [3, 5], "sgd_clf": 10, "sgdclassifi": 10, "sgdreg": 15, "sgdregressor": 15, "sgn": 7, "shallow": 15, "shape": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 18, 23], "share": [0, 3, 5, 23], "shareabl": 0, "she": 9, "shift": [0, 3, 8, 14, 20], "ship": 5, "shire": 23, "short": [6, 7], "shortcom": 15, "shorten": 6, "shorter": 20, "shorthand": 23, "shortli": [18, 23], "should": [0, 2, 4, 5, 7, 8, 10, 11, 13, 14, 18, 20, 23], "show": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "show_shap": 6, "shown": [2, 6, 7, 10, 14, 15, 18], "shrink": [5, 7, 8, 10, 13], "shrinkag": [7, 8], "shrunk": 13, "shuffl": [2, 3, 6, 8, 15], "side": [2, 4, 7, 10, 14, 15, 18, 23], "sigh": [17, 23], "sigma": [2, 3, 7, 8, 9, 12, 13, 14, 15, 18, 20, 23], "sigma0": 20, "sigma1": 20, "sigma2": 20, "sigma_": [7, 18, 23], "sigma_0": 7, "sigma_1": 7, "sigma_2": 7, "sigma_fn": [9, 14], "sigma_i": [2, 7, 23], "sigma_j": 7, "sigma_m": [8, 20], "sigma_n": [13, 20], "sigma_t": 15, "sigma_x": 20, "sigmoid": [3, 4, 6, 9, 10, 12, 14], "sigmundson": 8, "sign": [3, 4, 9, 10, 12, 20, 21], "signal": [3, 5, 12, 14], "signifi": 6, "signific": 3, "significantli": [3, 15, 20], "sim": [6, 7, 8, 15, 20], "similar": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 16, 17, 18, 23], "similarli": [2, 3, 5, 7, 10, 12, 15, 20, 23], "simpl": [1, 3, 4, 5, 7, 8, 9, 10, 12, 13, 14, 16, 17, 18, 20], "simplepredict": 12, "simpler": [1, 2, 3, 7, 8, 9, 15, 17, 23], "simplernn": 6, "simplest": [2, 3, 5, 6, 11, 12, 14, 16, 23], "simpletre": 12, "simpli": [2, 3, 4, 6, 7, 8, 10, 11, 12, 13, 14, 17, 18, 20, 23], "simplic": [4, 7, 8, 9, 10, 11, 12, 13, 14, 16], "simplicti": 7, "simplifi": [2, 8, 11, 17, 23], "simplist": [5, 8, 20], "simul": 8, "simultan": 8, "sin": [2, 3, 4, 5, 6, 11, 14, 15, 18, 23], "sinc": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 15, 18, 20, 22, 23], "sine": [5, 14], "singl": [2, 3, 4, 5, 7, 8, 9, 10, 11, 14, 15, 18, 20, 23], "singular": [2, 8, 15, 18, 23], "sinusoid": 5, "site": [2, 19], "situat": [2, 6, 7, 9, 15, 20, 23], "six": [5, 20], "size": [2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 15, 18, 20, 23], "skelarn": 0, "sketch": 12, "ski": 11, "skill": 2, "skip": 13, "skl": [2, 8, 23], "sklearn": [0, 2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 15, 16, 23], "skplt": [9, 12], "sl": 8, "slack": 10, "slice": [4, 18, 23], "slide": [1, 2, 5, 20, 23], "slight": [8, 15], "slightli": [3, 4, 5, 7, 8, 9, 12, 20], "slope": [10, 13, 14], "slow": [2, 4, 10, 15], "slower": [7, 18, 23], "slowest": 18, "slowli": 14, "slp": 3, "small": [2, 3, 4, 5, 7, 8, 10, 11, 12, 13, 14, 15, 17, 18, 20, 23], "smaller": [2, 3, 4, 7, 8, 10, 11, 13, 15, 20, 23], "smallest": [2, 6, 16, 23], "smallest_row_index": 16, "smooth": [2, 5, 8, 15, 23], "sn": [2, 3, 5, 8, 9, 23], "sne": 13, "sneak": 23, "so": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23], "soar": 8, "social": 2, "soft": [3, 9, 12, 14], "soften": 10, "softmax": [5, 9], "softwar": [2, 10, 17, 18], "sol": 10, "sole": [2, 8, 23], "solid": [2, 9], "solut": [2, 3, 4, 5, 7, 8, 10, 12, 13, 15, 18, 20, 23], "soluton": 4, "solv": [1, 2, 3, 5, 7, 8, 10, 12, 13, 14, 15, 18, 23], "solve_expdec": 4, "solve_ode_deep_neural_network": 4, "solve_ode_neural_network": 4, "solve_pde_deep_neural_network": 4, "solveod": 4, "solveode_popul": 4, "solver": [4, 9, 10, 11, 12, 18, 23], "some": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 16, 20, 23], "some_model": 8, "somehow": 6, "someon": 1, "someth": [0, 2, 3, 5, 6, 9, 11, 13, 20, 23], "sometim": [2, 3, 13, 14, 15, 16, 23], "soon": [18, 21], "sophist": [2, 23], "sopt": 15, "sort": [7, 8, 11, 13, 20], "sound": [5, 7], "sourc": [2, 3, 5, 8, 17, 18, 20, 23], "space": [2, 3, 6, 7, 10, 11, 13, 14, 15, 16, 20], "span": [2, 5, 7, 11, 13, 18, 23], "spare": 3, "spars": [5, 8, 18, 23], "sparse_mtx": [18, 23], "sparsecategoricalcrossentropi": 5, "sparsiti": 12, "spatial": [3, 4, 5, 14], "speak": 20, "special": [8, 9, 12, 14, 15, 18, 20, 23], "specif": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 17, 18, 20, 22, 23], "specifi": [2, 5, 7, 8, 9, 11, 13, 15, 16, 20, 23], "specifici": [2, 12, 23], "spectacular": 5, "spectral": 3, "speech": [2, 3, 5, 6, 14], "speed": [3, 4, 6, 15], "spend": [1, 20], "sphere": 2, "spin": 8, "spite": 2, "spline": 10, "split": [1, 3, 5, 6, 7, 8, 10, 11, 12, 13, 16, 20], "splite": 2, "splitter": [3, 12], "spontan": 20, "spot": 5, "spread": [2, 13, 20, 23], "springer": [22, 23], "spuriou": 15, "sqrsignal": 5, "sqrt": [2, 5, 6, 7, 8, 10, 12, 13, 15, 20], "squar": [0, 3, 4, 5, 6, 9, 10, 11, 13, 15, 16, 17, 18, 20], "squarederror": 12, "squaredeuclidean": 16, "squash": 14, "srtm": 8, "srtm_data_norway_1": 8, "stabil": 7, "stabl": [1, 2, 6, 7, 8, 11, 17, 23], "stack": [5, 6], "stage": [0, 7, 15], "stai": [2, 4, 6, 7, 13, 23], "stand": [2, 7, 11, 14, 23], "standard": [2, 3, 6, 7, 8, 9, 10, 12, 14, 18, 20, 23], "standardscal": [2, 8, 9, 10, 11, 12, 13], "stanford": 15, "start": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 18, 20, 21, 23], "start_tim": 16, "stat": 8, "state": [3, 4, 6, 7, 8, 9, 10, 12, 13, 14, 15, 17, 20], "statement": [2, 9, 18, 23], "statist": [2, 3, 5, 6, 9, 11, 12, 13, 14, 15, 16, 18, 22], "statu": [0, 2, 9, 23], "stavang": 8, "std": [2, 6, 8, 23], "steep": 15, "step": [0, 2, 3, 4, 6, 8, 9, 11, 12, 13, 14, 15, 16, 18, 23], "step_fn": [9, 14], "step_length": 15, "steps_list": 11, "stereo": 5, "still": [2, 4, 5, 7, 8, 13, 15, 20], "stimuli": 14, "stk": [22, 23], "stk2100": [22, 23], "stk3155": [0, 19, 21], "stk4021": [22, 23], "stk4051": [22, 23], "stk4155": [19, 21], "stk5000": 22, "stochast": [2, 3, 7, 8, 10, 13, 14, 23], "stock": 6, "stoke": 14, "stone": [2, 9], "stop": [3, 6, 11, 15, 16], "storag": 7, "store": [2, 3, 4, 5, 8, 13, 15, 20, 23], "storehaug": [21, 23], "str": [3, 5, 6], "straight": [2, 8, 10, 15, 23], "straightforward": [2, 4, 5, 7, 8, 10, 11, 12, 15, 18, 23], "strategi": [2, 3, 11, 23], "stratifi": 8, "strength": [2, 7, 16], "stretch": 13, "strict": [10, 15], "strictli": [10, 15], "stride": [6, 18], "strike": 8, "string": 3, "stroke": 9, "strong": [5, 8, 11, 12, 14, 18, 20], "strongli": [0, 2, 10, 17, 18], "stronli": 2, "structur": [2, 3, 4, 5, 8, 11, 12, 14, 17, 23], "stuck": [3, 15], "student": [0, 2, 19, 21, 22, 23], "studi": [2, 5, 6, 7, 8, 9, 10, 13, 14, 15, 17, 22, 23], "studier": 22, "style": [2, 9, 11, 18, 23], "st\u00f8land": [21, 23], "sub": [11, 14], "subdivid": [2, 18, 23], "subfield": 2, "subject": [8, 10, 20], "submit": 23, "subplot": [2, 3, 5, 6, 8, 9, 10, 11, 12, 16, 23], "subplots_adjust": [10, 20], "subprogram": [18, 23], "subract": 2, "subroutin": [2, 23], "subscript": 3, "subsequ": [3, 6, 7, 8, 14, 18, 20], "subset": [3, 8, 11, 14, 15, 17, 23], "subspac": [2, 10, 13], "substanti": [11, 12], "substep": 13, "substitut": [1, 5, 8, 14, 18], "subsubset": 11, "subtask": 8, "subtl": 3, "subtract": [2, 6, 7, 8, 13, 15, 18, 20], "subtre": 11, "succeed": [2, 6, 23], "success": [5, 9, 11, 15, 20], "successfulli": [6, 11], "sudo": [2, 17, 23], "suffer": [2, 3, 4, 7, 12, 23], "suffici": [3, 8, 10, 13, 15], "suggest": [3, 15, 22], "suit": [10, 14], "suitabl": [0, 2, 20], "sum": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "sum_": [1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "sum_i": [2, 4, 7, 8, 10, 15], "sum_j": 8, "sum_ja_": 2, "sum_k": [8, 10, 14, 18], "sum_logist": 15, "sum_m": 5, "sum_n": 5, "sum_nx_": 5, "summar": [7, 8, 11], "summari": [3, 5, 6, 12, 19], "summat": [1, 2, 5], "sunni": 11, "super": 7, "superfici": 5, "superscript": [3, 14], "supervis": [2, 7, 8, 9, 11, 14, 17, 23], "supplement": 9, "support": [2, 3, 11, 12, 13, 15, 17, 23], "suppos": [2, 7, 8, 9, 10, 12, 13, 14, 15, 18, 23], "suppress": [7, 15], "sure": [1, 2, 3, 6, 8], "surf": 8, "surfac": [2, 8, 23], "surpass": 8, "surpris": [2, 23], "surround": [5, 17], "survei": [2, 7, 8, 23], "svc": [10, 11, 12], "svd": [2, 8, 13, 23], "svdinv": 7, "svm": [10, 11, 12, 13], "svm_clf": [10, 12], "swath": 7, "switch": 2, "sy": 15, "symbol": [3, 7, 13, 15, 17, 20, 23], "symmeteri": 3, "symmetr": [2, 7, 10, 13, 14, 15, 18, 23], "symmetri": 8, "sympi": [2, 17, 23], "synonim": 20, "syntax": 15, "system": [0, 2, 3, 5, 6, 8, 9, 11, 12, 14, 15, 17, 18, 23], "systemat": [6, 8], "t": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23], "t0": [5, 8, 15], "t1": [4, 15], "t2": 4, "t3": 4, "t_": 4, "t_0": [4, 11, 15], "t_1": 15, "t_b": 12, "t_i": [3, 4, 7, 14], "t_j": 14, "t_k": 11, "tabl": [11, 20, 21, 23], "tabul": [2, 23], "tabular": 23, "tackl": 6, "tag": [4, 5, 6, 7, 8, 9, 14, 15, 16, 18, 20], "taht": [2, 23], "tail": 20, "tailor": [4, 10, 13, 23], "taiwan": [2, 23], "take": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 20, 23], "taken": [2, 3, 5, 8, 12, 15, 18, 23], "tan": 5, "tangent": [3, 6, 14, 15], "tanh": [3, 6, 9, 10, 14], "target": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 23], "target_nam": 11, "task": [2, 3, 5, 8, 11, 13, 14, 16, 23], "tau": [5, 7, 20], "taught": 23, "tax": 2, "taylor": [4, 15], "taylornr": 15, "tc": 10, "teach": [0, 19, 23], "team": 3, "teaser": 2, "technic": [2, 7, 8, 15, 23], "techniqu": [2, 3, 10, 12, 15, 17, 20, 22, 23], "technologi": [2, 3], "tell": [1, 2, 6, 8, 12, 13, 15, 20], "temp": 3, "temp1": 3, "temp2": 3, "temperatur": [2, 11, 23], "temporarili": 3, "ten": [5, 23], "tend": [5, 7, 8, 10, 11, 12, 14, 15, 16], "tendenc": [2, 23], "tension": 8, "tensor": 5, "tensorflow": [2, 4, 6, 10, 16, 17, 18, 22, 23], "term": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 23], "term1": [7, 8, 13], "term2": [7, 8, 13], "term3": [7, 8, 13], "term4": [7, 8, 13], "termin": [0, 2, 6, 7, 11, 12, 15, 23], "terrain": 8, "terrain1": 8, "test": [1, 5, 6, 7, 8, 9, 10, 11, 12, 15, 18, 20], "test_acc": 5, "test_accuraci": [3, 5], "test_error": 8, "test_imag": [5, 6], "test_ind": 8, "test_input": 6, "test_label": [5, 6], "test_loss": 5, "test_pr": 3, "test_predict": 3, "test_rnn": 6, "test_scor": [9, 12], "test_siz": [0, 2, 3, 5, 7, 8, 12, 23], "test_split": 11, "testerror": [2, 8], "testi": 6, "testpredict": 6, "testx": 6, "text": [2, 3, 4, 6, 7, 10, 11, 13, 15, 18, 20, 22], "textbook": 1, "textual": 11, "textur": 3, "tf": [3, 5, 6, 15, 16], "th": [2, 3, 4, 7, 8, 9, 11, 14, 15, 16, 18, 20, 23], "than": [2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 14, 15, 17, 20, 23], "thank": [6, 8], "theano": [3, 17, 23], "thei": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 18, 20, 23], "them": [2, 3, 5, 6, 8, 10, 11, 12, 13, 14, 15, 18, 23], "theme": [0, 2, 23], "themselv": [2, 20, 23], "thenc": 8, "theorem": [4, 8, 9], "theoret": [2, 6, 12, 23], "theori": [2, 3, 5, 10, 11, 14, 15, 17, 22, 23], "thereaft": [2, 7, 8, 13, 14, 18, 23], "therebi": [2, 7, 9, 13, 23], "therefor": [2, 3, 4, 5, 6, 8, 9, 10, 13, 15, 20, 23], "therein": 13, "thereof": [2, 8, 15, 23], "theta": [3, 6, 15, 20], "theta_": [3, 15], "theta_i": 3, "theta_linreg": 15, "theta_t": 15, "thi": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 22], "thing": [0, 1, 2, 3, 4, 6, 7, 9, 11, 20, 23], "think": [2, 3, 5, 6, 8, 11, 14, 15, 16, 20, 23], "third": [2, 5, 8, 15, 21, 23], "thirti": 9, "thorughout": 23, "those": [2, 5, 7, 8, 10, 11, 12, 13, 18, 23], "though": [1, 3, 4, 5, 6, 15, 18, 20], "thought": [8, 16, 20], "thousand": [2, 3], "three": [2, 3, 5, 7, 8, 10, 11, 14, 18, 19, 20, 21, 23], "threshold": [3, 5, 11, 12, 13, 14, 15], "through": [0, 2, 3, 4, 5, 6, 7, 8, 10, 13, 14, 15, 16, 17, 18, 20, 23], "throughout": [0, 2, 6, 7, 16, 17, 18, 20, 23], "throw": [5, 8, 20], "thu": [2, 3, 4, 7, 8, 9, 10, 12, 13, 14, 15, 21, 23], "thumb": [2, 8, 23], "tibshirani": [8, 22, 23], "tick_param": 8, "ticker": [8, 15, 20], "tif": 8, "tight_layout": [3, 9], "tightli": 13, "tild": [2, 7, 8, 9, 13, 20, 23], "till": [2, 6, 9, 10, 11, 12, 14, 18, 23], "time": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 23], "timeit": 6, "timer": 6, "tini": 3, "tip": 5, "titl": [0, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 15, 20, 23], "tmp": 15, "tn": [4, 5, 9], "to_categor": [3, 5, 6], "to_categorical_numpi": 3, "to_numer": [2, 8, 23], "todai": 5, "togeth": [2, 5, 8, 10, 13, 15, 17, 23], "toi": 16, "told": 15, "toler": [4, 16], "tolist": 6, "tomographi": 14, "too": [2, 4, 6, 7, 8, 11, 13, 15, 20, 22, 23], "took": [10, 23], "tool": [0, 2, 3, 5, 8, 15, 17], "toolbox": 10, "top": [2, 5, 7, 8, 11, 12, 17, 23], "topic": [2, 7, 8, 9, 10, 17], "topolog": [5, 14], "topologi": [3, 14], "torkjellsdatt": [21, 23], "toss": [12, 20], "total": [2, 3, 4, 5, 6, 8, 9, 10, 12, 13, 14, 15, 16, 18, 20, 21, 23], "total_loss": 6, "totalclustervari": 16, "totalscatt": 16, "toward": [0, 3, 4, 9, 14, 15], "town": 2, "tp": [6, 9], "tpng": 11, "tpu": [15, 17, 23], "tqdm": 8, "track": [0, 5, 15, 16, 18], "tract": 2, "tractabl": [2, 23], "trade": [7, 11], "tradeoff": [2, 7, 23], "tradit": [2, 3, 6, 8, 23], "train": [1, 4, 5, 7, 8, 10, 11, 12, 13, 14, 15], "train_accuraci": [2, 3, 5, 23], "train_dataset": 6, "train_end": [2, 3], "train_error": 8, "train_imag": [5, 6], "train_ind": 8, "train_label": [5, 6], "train_pr": 3, "train_siz": [2, 3, 5], "train_step": 6, "train_test_split": [0, 1, 2, 3, 5, 7, 8, 9, 11, 12, 13, 23], "train_test_split_numpi": [2, 3], "trainable_vari": 6, "trained_model": 8, "trainerror": 2, "traini": 6, "training_checkpoint": 6, "training_dataset": 6, "training_gradi": 15, "trainingerror": 8, "trainpredict": 6, "trainscor": 6, "trainx": 6, "trait": [2, 23], "trajectori": 6, "transfer": 11, "transform": [2, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 23], "transit": [8, 14], "translat": [3, 6, 8, 12, 23], "transpos": [3, 7, 13, 18], "travers": [2, 7], "treat": [2, 3, 5, 8, 14, 15, 20, 23], "tree": [2, 3, 17, 23], "tree_clf": [11, 12], "tree_clf_": 11, "tree_clf_sr": 11, "tree_reg": 11, "tree_reg1": 11, "tree_reg2": 11, "trend": 20, "treue": 9, "trevor": 22, "tri": [1, 4, 5, 6, 11, 15], "triain": 2, "trial": [2, 4, 6, 8, 15, 20, 23], "triangl": 15, "triangular": 18, "trick": [5, 6, 10, 13, 15, 20], "trickier": 20, "tridiagon": 18, "trillion": 17, "trivial": [2, 3, 7, 13, 20, 23], "troubl": [0, 2, 10, 14], "truck": 5, "true": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 18, 20, 23], "true_beta": 8, "true_fun": 8, "try": [0, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, 20, 23], "tucker": 10, "tuesdai": [21, 23], "tumor": [9, 11], "tumour": 9, "tunabl": 3, "tune": [6, 11, 15, 18, 23], "turn": [2, 3, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "tutori": [3, 6], "tv": 4, "tveito": 4, "tweak": [3, 6, 12, 20], "twice": 15, "twist": 13, "two": [0, 2, 3, 4, 6, 7, 8, 9, 11, 12, 13, 14, 15, 18, 19, 20, 22, 23], "tx": 15, "tx_1": 15, "txt": [0, 6], "ty": 15, "type": [2, 3, 5, 8, 10, 12, 15, 18, 20], "typic": [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 12, 14, 15, 20, 23], "u": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 22, 23], "u_": 18, "u_i": 14, "u_m": 12, "ua": [2, 23], "ubuntu": [2, 17, 23], "uci": 2, "uio": [0, 21, 22], "un": 16, "unabl": 0, "unari": [18, 23], "unbalanc": [8, 11], "unbias": [2, 7, 8, 23], "uncent": 8, "uncertainti": [2, 7, 23], "uncertitud": 20, "unchang": [3, 5], "uncorrel": [12, 20], "undefin": 7, "under": [2, 3, 7, 8, 12, 15, 17, 23], "underdetermin": [2, 23], "underfit": [3, 8], "underflowproblem": 7, "undergo": 7, "undergradu": [19, 21], "underli": [2, 3, 11, 15, 20, 23], "underset": [6, 16], "understand": [0, 2, 3, 5, 7, 8, 12, 15, 16, 17, 23], "understood": [10, 15], "undesir": 10, "undetermin": [7, 10], "undo": 6, "unexpect": 8, "unexpected": 20, "unfair": 8, "unfortun": [3, 10, 11, 12], "unicode_liter": [10, 11], "uniform": [2, 3, 7, 8, 13, 15, 20, 23], "uniformli": [15, 20], "unifrompdf": 20, "unimport": 15, "union": [7, 8], "uniqu": [2, 4, 8, 15, 16, 18, 23], "unique_cluster_label": 16, "unit": [2, 3, 5, 6, 7, 12, 14, 20, 23], "unitari": [7, 8, 18], "unitarili": [18, 23], "uniti": 20, "univari": 20, "univers": [2, 3, 4, 15, 17, 19, 21, 23], "unix": 3, "unknow": [2, 18, 23], "unknown": [2, 3, 5, 6, 7, 8, 10, 12, 15, 18, 23], "unknowwn": 14, "unlabel": 3, "unless": [2, 5, 8, 13, 15, 23], "unlik": [3, 5, 10, 15], "unnecessarili": 11, "unord": 5, "unravel": 3, "unrol": [5, 13], "unseen": [0, 2, 9, 11], "unstabl": 3, "unsupervis": [2, 3, 6, 14, 17, 23], "unsymmetr": [18, 23], "until": [3, 4, 6, 11, 14, 15, 16], "untouch": 2, "unusu": 14, "up": [1, 3, 5, 6, 7, 8, 10, 12, 13, 15, 16, 17, 18, 20, 21], "updat": [0, 3, 4, 12, 14, 15, 16], "uploa": 23, "upload": [0, 17, 22], "upon": [2, 3, 8, 9, 13, 18], "upper": [1, 2, 10, 11, 18], "uppercas": [18, 23], "upsampl": 6, "upscal": 6, "url": 23, "us": [0, 6, 7, 8, 10, 11, 12, 13, 14, 16, 18, 20, 22], "usa": 23, "usag": [2, 10, 17, 23], "usd": 2, "usd10000": 2, "use_bia": 6, "usecol": [2, 23], "useless": 3, "user": [2, 3, 4, 6, 8, 9, 17, 18, 23], "usernam": 0, "usetex": 20, "usg": 8, "usr": 20, "usual": [2, 5, 6, 9, 14, 15, 16, 23], "ut": 7, "util": [3, 5, 6, 8, 9, 12, 16, 23], "ux": 18, "v": [0, 2, 4, 6, 7, 8, 13, 15, 17], "v0": 20, "v1": 20, "v2": 20, "v_0": 13, "va": 3, "vahid": 23, "val": 15, "val_accuraci": 5, "val_loss": 6, "vale": 4, "valid": [2, 3, 6, 9, 11, 12, 15, 17, 20, 23], "validation_data": 5, "validation_split": 6, "valu": [1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 23], "valuat": 11, "valy": 6, "van": [2, 23], "vandenbergh": [10, 15], "vandermond": [2, 23], "vanilla": [2, 8, 13, 16], "vanish": [3, 6, 15, 20], "var": [7, 8, 12, 13, 20], "var_x": 20, "varabl": 10, "varepsilon": [7, 8], "varepsilon_": [7, 8], "varepsilon_i": [7, 8], "vari": [2, 3, 5, 7, 8, 12, 23], "variabl": [2, 3, 4, 7, 8, 9, 10, 12, 13, 14, 15, 16, 18, 23], "varianc": [2, 3, 7, 9, 11, 12, 13, 15, 16, 17, 18, 20, 23], "variance_i": [7, 13], "variance_x": [7, 13], "variant": [2, 3, 8, 10, 14, 15, 23], "variat": [5, 6, 13, 23], "varieti": [2, 5, 14, 17, 23], "variou": [1, 3, 5, 7, 8, 9, 10, 11, 13, 14, 15, 17, 18, 20], "varydimens": 6, "vastli": 5, "vaue": 3, "vault": 2, "vdot": [4, 15], "vec": 8, "vector": [2, 3, 4, 5, 6, 7, 8, 9, 11, 12, 13, 15, 16, 17], "vector_mean": 16, "ventur": [2, 10, 17, 23], "venv": 0, "verbos": [3, 5, 6], "veri": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 20, 22, 23], "verifi": [5, 13, 18, 23], "versatil": [10, 23], "versicolor": [10, 11], "version": [0, 2, 5, 12, 15, 16, 17, 18, 20, 23], "versu": 3, "vert": [1, 2, 3, 7, 8, 9, 10, 11, 13, 15, 23], "vert_1": [7, 8], "vert_2": [7, 8, 13], "via": [2, 7, 8, 9, 10, 11, 12, 13, 14, 17, 18, 19, 20, 21, 23], "vidal": 13, "video": [2, 3, 14, 17, 19, 21, 23], "view": [3, 5, 7, 8, 14, 15, 20, 22, 23], "violat": 10, "virginica": 11, "viridi": [2, 3, 4, 5, 23], "virtual": 3, "viscos": 15, "viscou": 15, "visibl": 0, "vision": [2, 5], "visual": [2, 5, 13, 14, 17, 23], "visualis": 3, "visualstudio": [0, 1], "viz": [8, 10, 20], "vmap": 15, "vmax": [3, 8], "vmin": [3, 8], "voic": 5, "volum": [2, 5, 23], "vote": 12, "voting_clf": 12, "votingclassifi": 12, "votingsimpl": 12, "vstack": [7, 13, 18, 20, 23], "vt": 7, "w": [2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 18, 20, 23], "w1": 10, "w2": [10, 13], "w3": 10, "w_": [3, 14], "w_1": [10, 18], "w_1x_": 10, "w_1x_1": 10, "w_2": [10, 18], "w_2x_": 10, "w_2x_2": 10, "w_3": 18, "w_4": 18, "w_hidden": 4, "w_i": [3, 4, 12], "w_ix_i": 14, "w_j": 18, "w_m": 18, "w_output": 4, "w_px_": 10, "w_px_p": 10, "wa": [2, 3, 5, 6, 7, 8, 9, 12, 13, 14, 16, 18, 23], "wai": [0, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 18, 20, 23], "walk": 11, "walker": 20, "wang": [2, 23], "want": [0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 14, 15, 16, 17, 20, 23], "warn": 6, "warrant": 8, "wast": 5, "watch": 17, "wave": 5, "wavelet": 10, "we": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22], "weak": [11, 12, 16], "weather": [3, 14], "web": [17, 19, 21, 23], "webpag": 23, "websit": [8, 18, 19, 23], "wedg": [10, 20], "wednesdai": [21, 23], "wee": 13, "week": [2, 7, 8, 9, 19, 21], "weekli": [0, 1, 17, 19, 21, 22, 23], "weight": [2, 3, 4, 5, 8, 9, 11, 12, 14, 15, 20], "weigth": 4, "welcom": [0, 10, 17], "well": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 14, 15, 17, 18, 20, 22, 23], "went": 10, "were": [2, 3, 5, 6, 7, 8, 9, 10, 12, 13, 14, 16, 20, 23], "wessel": [2, 23], "what": [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20], "whatev": 5, "when": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "whenev": [0, 15, 20], "where": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 23], "wherea": [8, 20], "wherein": [3, 14], "whether": [2, 5, 7, 9, 11, 20, 23], "which": [0, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 23], "whichev": [3, 5], "while": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 20, 23], "white": 11, "who": [0, 2], "whole": [3, 5, 6, 7, 11, 13, 15], "whose": [2, 8, 12, 20], "whow": 13, "why": [0, 1, 2, 3, 5, 8, 15], "wide": [2, 3, 5, 8, 9, 14, 17, 18, 23], "widehat": 8, "width": [2, 5, 10, 11, 23], "wieringen": [2, 23], "win": 12, "wind": 11, "wing": [21, 23], "winther": 4, "wiothout": 8, "wiscons": 9, "wisconsin": 12, "wisdom": 8, "wise": [2, 3, 7, 14, 15], "wish": [2, 4, 7, 9, 10, 13, 15, 16, 18, 23], "with_std": 2, "wither": 8, "within": [2, 4, 5, 6, 9, 11, 14, 15, 16, 20, 22, 23], "withinclust": 16, "without": [0, 2, 3, 7, 8, 10, 11, 13, 14, 15, 23], "won": [0, 2, 23], "wonder": 10, "word": [2, 3, 5, 6, 7, 8, 9, 16, 20, 23], "work": [0, 1, 2, 3, 6, 8, 9, 10, 11, 15, 17, 19, 20, 21, 23], "workshop": 23, "world": [1, 2, 10], "worldwid": [2, 23], "worri": 0, "wors": [2, 3, 5, 6, 8, 23], "worth": 11, "would": [1, 2, 3, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 20, 23], "wrap": [8, 18, 23], "write": [0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 14, 15, 18, 23], "written": [1, 2, 4, 5, 7, 13, 14, 15, 17, 18, 20, 23], "wrong": [0, 3, 10], "wrongli": 12, "wrote": [7, 13], "wrt": [12, 15], "wth": [12, 15], "www": [17, 18, 22, 23], "wx_1": 10, "x": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "x0": 10, "x1": [6, 10, 11, 12, 15], "x1_exampl": 10, "x1d": 10, "x2": [10, 11, 12, 15], "x2d": [10, 13], "x2d_train": 13, "x2dsl": 13, "x3": 10, "x_": [2, 4, 5, 7, 8, 10, 12, 13, 15, 16, 18, 20, 23], "x_0": [2, 7, 13, 18, 23], "x_1": [2, 4, 7, 8, 9, 10, 11, 12, 13, 15, 18, 20, 23], "x_2": [2, 4, 7, 8, 9, 10, 11, 12, 13, 15, 18, 20, 23], "x_3": [10, 18, 20], "x_4": 18, "x_center": 13, "x_data": 3, "x_data_ful": 3, "x_hidden": 4, "x_i": [2, 3, 4, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "x_input": 4, "x_ix_": [2, 23], "x_iy_i": 10, "x_j": [1, 2, 4, 10, 11, 14, 20], "x_jy_j": 10, "x_k": [14, 16, 18, 20], "x_l": 20, "x_m": [8, 14, 18, 20], "x_n": [2, 4, 5, 8, 10, 13, 14, 15, 18, 20, 23], "x_new": [11, 12], "x_offset": 8, "x_output": 4, "x_p": [5, 9, 11], "x_poli": 11, "x_poly10": 11, "x_pred": 6, "x_prev": 4, "x_reduc": 13, "x_scale": 10, "x_small": 15, "x_test": [0, 1, 2, 3, 5, 7, 8, 9, 11, 12, 13, 23], "x_test_own": 8, "x_test_scal": [2, 8, 9, 11, 12, 13], "x_tot": 6, "x_train": [0, 1, 2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 23], "x_train_mean": 8, "x_train_own": 8, "x_train_scal": [2, 8, 9, 11, 12, 13], "x_val": 3, "xarrai": [17, 23], "xavier": 3, "xbkjd4ccgq": 23, "xbnew": 15, "xcode": [2, 17, 23], "xdclassiffierconfus": 12, "xdclassiffierroc": 12, "xg_clf": 12, "xgb": 12, "xgbclassifi": 12, "xgboost": 11, "xgboot": 12, "xgbregressor": 12, "xgparam": 12, "xgtree": 12, "xi": [10, 15], "xi_": 10, "xi_1": 10, "xi_i": 10, "xk": 10, "xla": [15, 17, 23], "xlabel": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 20, 23], "xlim": [8, 12], "xm": 11, "xmesh": 15, "xnew": [2, 15, 23], "xp": 20, "xpanda": 2, "xpd": [7, 13], "xplot": 2, "xscale": 2, "xsr": 11, "xt_x": 15, "xtest": 8, "xtick": [5, 8, 10, 11], "xtrain": 8, "xu": [2, 23], "xx": [2, 18, 23], "xy": [2, 8, 10, 18, 23], "xytext": 10, "xz": [18, 23], "y": [0, 1, 2, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "y1": 6, "y2": 6, "y3": 6, "y_": [2, 3, 7, 8, 12, 13, 18, 23], "y_0": [2, 7, 13, 18, 23], "y_1": [2, 7, 10, 11, 13, 15, 18, 23], "y_1y_1": 10, "y_1y_1k": 10, "y_1y_2": 10, "y_1y_2k": 10, "y_1y_n": 10, "y_1y_nk": 10, "y_2": [2, 7, 10, 11, 13, 18, 23], "y_2y_1": 10, "y_2y_1k": 10, "y_2y_2": 10, "y_2y_2k": 10, "y_3": [2, 11, 18], "y_4": 18, "y_data": [2, 3, 7, 8, 23], "y_data_ful": 3, "y_decis": 10, "y_fit": 2, "y_i": [2, 3, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 23], "y_if_": 12, "y_ix_": [2, 23], "y_ix_i": [9, 10, 15], "y_iy_jk": 10, "y_j": [8, 10, 14], "y_k": 14, "y_m": 18, "y_model": [2, 6, 7, 8, 23], "y_n": [10, 15], "y_ny_1": 10, "y_ny_1k": 10, "y_ny_2": 10, "y_ny_2k": 10, "y_ny_n": 10, "y_ny_nk": 10, "y_offset": 8, "y_plot": 11, "y_pred": [2, 3, 6, 8, 9, 10, 11, 12], "y_pred1": 11, "y_pred2": 11, "y_pred_rf": 12, "y_pred_tre": 12, "y_proba": [9, 12], "y_scaler": 8, "y_test": [0, 1, 2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 23], "y_test_onehot": 3, "y_test_predict": 2, "y_tot": 6, "y_train": [0, 1, 2, 3, 5, 6, 7, 8, 9, 11, 12, 13, 23], "y_train_mean": 8, "y_train_onehot": 3, "y_train_predict": 2, "y_train_scal": 8, "y_val": 3, "ye": [5, 8, 9], "year": [2, 17, 23], "yet": [2, 3, 8, 10, 13, 15, 23], "yi": 15, "yield": [2, 4, 7, 8, 10, 12, 14, 15, 16, 18, 20, 23], "yk": 10, "ylabel": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 20, 23], "ylim": [5, 8], "ym": 11, "ymesh": 15, "yn": 2, "yo": [10, 11, 12], "yoshua": [3, 22], "you": [0, 1, 2, 3, 5, 6, 7, 8, 10, 11, 12, 13, 15, 17, 18, 20, 21, 22, 23], "young": 2, "your": [0, 3, 4, 6, 7, 8, 10, 13, 15, 17, 18], "your_model_object": 1, "yourself": [13, 15, 23], "youtub": 17, "ypred": 8, "ypredict": [2, 15, 23], "ypredict2": 15, "ypredictlasso": 7, "ypredictol": [2, 7], "ypredictown": 8, "ypredictownridg": 8, "ypredictridg": [2, 7, 8], "ypredictskl": 8, "yridg": 23, "ytest": 8, "ytick": [5, 8, 10, 11], "ytild": [2, 8, 23], "ytildelasso": 7, "ytildenp": [2, 23], "ytildeol": [2, 7], "ytildeownridg": 8, "ytilderidg": [7, 8], "ytrain": 8, "yuxi": 23, "yx": [18, 23], "yy": [18, 23], "yz": [18, 23], "z": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 18, 20, 23], "z_": [3, 4, 14, 18, 23], "z_0": [18, 23], "z_1": [18, 23], "z_2": [18, 23], "z_c": 3, "z_h": 3, "z_hidden": 4, "z_i": [3, 14], "z_j": [3, 14], "z_k": 14, "z_m": 3, "z_mod": 11, "z_o": 3, "z_output": 4, "zaman": 20, "zaxi": 8, "zero": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20, 23], "zeros_lik": 6, "zfill": 6, "zip": [6, 8], "zm_h": [2, 23], "zn": 2, "zone": 2, "zoom": 23, "zx": [18, 23], "zy": [18, 23], "zz": [18, 23], "\u00f8yvind": 8}, "titles": ["Exercises week 34", "Exercises week 35", "<span class=\"section-number\">3. </span>Linear Regression", "<span class=\"section-number\">14. </span>Building a Feed Forward Neural Network", "<span class=\"section-number\">15. </span>Solving Differential Equations  with Deep Learning", "<span class=\"section-number\">16. </span>Convolutional Neural Networks", "<span class=\"section-number\">17. </span>Recurrent neural networks: Overarching view", "<span class=\"section-number\">4. </span>Ridge and Lasso Regression", "<span class=\"section-number\">5. </span>Resampling Methods", "<span class=\"section-number\">6. </span>Logistic Regression", "<span class=\"section-number\">8. </span>Support Vector Machines, overarching aims", "<span class=\"section-number\">9. </span>Decision trees, overarching aims", "<span class=\"section-number\">10. </span>Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods", "<span class=\"section-number\">11. </span>Basic ideas of the Principal Component Analysis (PCA)", "<span class=\"section-number\">13. </span>Neural networks", "<span class=\"section-number\">7. </span>Optimization, the central part of any Machine Learning algortithm", "<span class=\"section-number\">12. </span>Clustering and Unsupervised Learning", "Applied Data Analysis and Machine Learning", "<span class=\"section-number\">2. </span>Linear Algebra, Handling of Arrays and more Python Features", "Course setting", "<span class=\"section-number\">1. </span>Elements of Probability Theory and Statistical Data Analysis", "Teachers and Grading", "Textbooks", "Week 34: Introduction to the course, Logistics and Practicalities"], "titleterms": {"": [10, 12], "1": [0, 1, 2, 23], "2": [0, 1, 2, 23], "2023": 21, "3": [0, 1, 2, 23], "34": [0, 23], "35": 1, "4": [0, 1, 2], "5": [1, 2], "A": [2, 3, 6, 10, 11, 23], "And": 23, "In": 21, "Ising": 8, "The": [0, 2, 3, 4, 5, 7, 8, 9, 10, 11, 13, 14, 17, 23], "To": 23, "With": 6, "about": 23, "activ": [3, 14], "ad": [2, 8, 23], "adaboost": 12, "adagrad": 15, "adam": 15, "adapt": 12, "adjust": 3, "adversari": 6, "again": [5, 11], "ai": 23, "aim": [10, 11, 23], "aka": 23, "algebra": [18, 23], "algorithm": [11, 12, 13, 14], "algortithm": 15, "all": 10, "an": [0, 2, 6, 12, 23], "analys": 7, "analysi": [2, 7, 8, 13, 17, 20, 23], "analyt": [1, 2], "ani": 15, "anoth": 11, "appli": 17, "approach": [2, 10, 16, 23], "approxim": 14, "architectur": 3, "arrai": [18, 23], "assist": 21, "autocorrel": 20, "autograd": [4, 15], "automat": 15, "back": [3, 13, 14], "background": 17, "bag": 12, "base": 15, "basic": [2, 7, 9, 11, 12, 13, 18], "batch": 3, "bay": 7, "befor": 13, "better": 10, "bia": 8, "binari": 3, "bind": 23, "bird": 12, "boost": 12, "bootstrap": [8, 12], "boston": 2, "breast": 3, "brief": 23, "bring": 14, "build": [3, 5, 11], "c": 23, "can": 23, "cancer": [3, 9, 11, 13], "cart": 11, "case": [10, 12, 20], "central": [15, 17, 20], "chain": 14, "chang": 12, "channel": 23, "chi": [2, 23], "choos": 3, "cifar01": 5, "classic": 13, "classif": [3, 11, 12], "classifi": 10, "clip": 3, "cluster": 16, "cnn": 5, "code": [0, 1, 2, 3, 4, 7, 11, 13, 14, 15, 16, 23], "collect": [3, 5], "commun": 23, "compar": [1, 4, 12], "complex": [2, 8], "complic": 8, "compon": 13, "comput": 11, "computerlab": 23, "con": 11, "concept": 20, "conjug": 15, "contn": 23, "convex": [10, 15], "convolut": [5, 14], "correl": 13, "cost": [3, 12], "cours": [17, 19, 22, 23], "covari": [7, 13, 20], "cover": 23, "creat": 1, "cross": 8, "cython": 23, "data": [0, 2, 3, 5, 8, 9, 11, 13, 17, 20, 23], "dataset": [3, 5], "deadlin": 23, "deadllin": 21, "decai": 4, "decis": [11, 12], "decomposit": [7, 13, 18], "deep": [3, 4, 23], "defin": [3, 23], "degre": 2, "deliver": [0, 1], "dens": [2, 23], "deriv": [1, 7, 14], "descent": [4, 12, 15], "detail": [5, 23], "develop": 3, "diagon": 13, "differ": 10, "differenti": [4, 15], "diffus": 4, "dimension": [4, 5, 10], "disadvantag": 11, "discret": 20, "distribut": [7, 20], "do": 3, "domain": 20, "down": 3, "dropout": 3, "element": [2, 20, 23], "elimin": 18, "energi": 23, "ensembl": 12, "entropi": 11, "environ": [0, 2, 23], "equat": [2, 4, 14, 23], "error": [2, 12, 23], "essenti": 23, "etc": 23, "euler": 4, "evalu": 3, "exampl": [2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 23], "exercis": [0, 1, 2, 8, 23], "expect": 20, "experi": 20, "explor": [2, 23], "exponenti": 4, "express": 1, "extrapol": 6, "extrem": [12, 23], "ey": 12, "fall": 21, "famili": [3, 23], "famou": 18, "featur": [1, 11, 18], "feed": [3, 14], "final": 14, "find": 1, "fine": 3, "first": [6, 14, 23], "fit": [0, 1, 2, 12, 23], "forc": 5, "forest": 12, "format": 23, "forward": [3, 4, 14], "fourier": 5, "frank": 8, "freedom": 2, "frequentist": [2, 23], "from": [7, 12, 14], "full": 4, "function": [2, 3, 8, 9, 10, 12, 13, 14, 15, 20, 23], "further": [5, 7], "gan": 6, "gaussian": 18, "gd": 15, "gener": [6, 11, 23], "geometr": 13, "gini": 11, "github": 0, "goal": [0, 1], "good": [2, 23], "grade": [21, 23], "gradient": [3, 4, 12, 15], "growth": 4, "ha": 17, "handl": [18, 23], "hidden": 4, "hous": 2, "how": 1, "hyperparamet": 3, "hyperplan": 10, "i": [2, 3, 23], "id3": 11, "idea": 13, "ii": 23, "implement": [1, 3], "implic": 7, "import": [7, 18, 23], "improv": 3, "includ": 15, "increment": 13, "index": 11, "inform": 21, "input": 4, "instal": [17, 23], "instructor": 21, "interpret": [7, 13, 23], "introduc": [13, 15], "introduct": [2, 8, 17, 18, 23], "invers": [7, 18], "iter": 12, "jax": 15, "julia": 23, "jungl": 12, "kera": [3, 5], "kernel": [10, 13], "lagrangian": 10, "lasso": [7, 8], "later": 7, "layer": [3, 4, 5, 14], "learn": [0, 1, 2, 3, 4, 13, 15, 16, 17, 23], "least": [1, 7, 8, 23], "lectur": 23, "level": 12, "librari": [17, 23], "likelihood": 9, "limit": [3, 15, 20], "linear": [0, 2, 10, 15, 18, 23], "link": [7, 13, 22], "logist": [9, 23], "lu": 18, "machin": [2, 10, 15, 17, 23], "main": [20, 23], "make": [2, 11, 12, 23], "mani": [12, 14], "mass": 23, "materi": 23, "math": 7, "mathemat": [5, 7, 10], "matric": [7, 18, 23], "matrix": [1, 3, 7, 13, 14, 18, 23], "matter": [2, 23], "mean": 2, "meet": [7, 12, 20, 23], "mercer": 10, "method": [8, 11, 12, 15, 23], "minim": 23, "ml": 23, "mlp": 14, "mnist": [5, 6], "model": [0, 2, 3, 6, 8, 14, 23], "momentum": 15, "moon": [10, 11], "more": [5, 8, 18, 23], "multilay": 14, "multipl": [3, 5], "multipli": 10, "need": 23, "network": [3, 4, 5, 6, 9, 14, 23], "neural": [3, 4, 5, 6, 9, 14, 23], "new": 6, "non": 10, "normal": [2, 3], "notat": 14, "now": [3, 11, 15], "nuclear": [2, 23], "numba": 23, "number": [2, 4, 20], "numer": [4, 20], "numpi": [18, 23], "object": 5, "obtain": 13, "od": 4, "off": 8, "ol": [0, 1, 7, 8], "one": [4, 14], "oper": 18, "optim": [3, 10, 15, 17, 23], "order": 15, "ordinari": [1, 7, 8, 23], "organ": [2, 23], "oslo": 22, "other": [6, 11, 13, 14, 18, 23], "our": [2, 6, 7, 13, 15, 23], "outcom": [17, 23], "output": 4, "overarch": [2, 6, 10, 11, 23], "overview": [12, 23], "own": [2, 12, 13, 23], "packag": [18, 23], "panda": 23, "paramet": 23, "part": [15, 17], "partial": 4, "pass": 3, "pca": 13, "pdf": 20, "perceptron": 14, "perform": [3, 11], "period": 5, "perspect": 3, "point": 6, "poisson": 4, "polynomi": [1, 5], "popul": 4, "popular": 23, "practic": [15, 21, 23], "pre": [3, 5], "predict": 6, "prerequisit": [5, 17, 23], "princip": 13, "principl": 5, "pro": 11, "probabl": [7, 20], "problem": [3, 4, 15, 23], "procedur": [11, 23], "process": [3, 5], "program": [4, 15], "project": [8, 21, 23], "prop": 15, "propag": [3, 14], "properti": [7, 20], "python": [0, 2, 11, 17, 18, 23], "quick": 10, "r": 23, "random": [12, 13, 20], "read": [11, 23], "real": [8, 23], "recommend": 23, "recurr": [6, 14], "reduc": 2, "reduct": 5, "reformul": 4, "regress": [0, 2, 7, 8, 9, 11, 12, 15, 23], "regular": 3, "relev": 22, "relu": 3, "remark": 5, "remind": [8, 10, 23], "replac": 15, "repositori": 0, "requir": [4, 17], "resampl": 8, "rescal": 8, "resourc": 4, "revisit": 15, "rewrit": 23, "ridg": [2, 7, 8], "rm": 15, "rule": 14, "same": 15, "sampl": 13, "schedul": 23, "schemat": 11, "scheme": 4, "scienc": 23, "scikit": [2, 3, 13, 23], "second": 15, "semest": 21, "set": [0, 2, 4, 5, 11, 14, 19, 23], "setup": 0, "sgd": 15, "should": 3, "similar": 15, "simpl": [2, 6, 11, 15, 23], "singl": 12, "singular": [7, 13], "sklearn": 1, "soft": 10, "softmax": 3, "softwar": 23, "solv": 4, "solver": 15, "some": [15, 18], "specifi": 4, "split": [0, 2, 23], "squar": [1, 2, 7, 8, 12, 23], "standard": 15, "state": [2, 23], "statist": [7, 8, 17, 20, 23], "steepest": [12, 15], "stochast": [15, 20], "strongli": 23, "suggest": 23, "summari": [21, 23], "superposit": 5, "supervis": 3, "support": 10, "svd": 7, "systemat": 5, "take": 1, "teach": 21, "teacher": [21, 23], "techniqu": [8, 13], "technologi": 17, "tensorflow": [3, 5], "tent": [21, 23], "test": [0, 2, 3, 23], "text": 23, "textbook": [22, 23], "theorem": [7, 10, 13, 14, 20], "theori": 20, "thi": 23, "tip": 15, "togeth": 14, "tool": 23, "top": 3, "topic": 23, "toward": 13, "trade": 8, "tradeoff": 8, "train": [0, 2, 3, 6, 23], "transform": 5, "tree": [11, 12], "tune": 3, "two": [5, 10, 17], "type": [4, 6, 14, 23], "uio": 23, "univers": [14, 22], "unsupervis": 16, "up": [0, 2, 4, 11, 14, 23], "us": [1, 2, 3, 4, 5, 9, 15, 17, 23], "v": 5, "valid": 8, "valu": [7, 13, 20], "variabl": 20, "varianc": 8, "variou": [2, 23], "vector": [1, 10, 14, 18, 23], "view": [2, 6, 12], "virtual": 0, "visual": [3, 11], "wai": 11, "wave": 4, "we": 23, "week": [0, 1, 23], "what": [2, 23], "which": 3, "why": 23, "wisconsin": 9, "write": [6, 13], "xgboost": 12, "your": [1, 2, 12, 23]}})
\ No newline at end of file
diff --git a/doc/LectureNotes/_build/jupyter_execute/E1.ipynb b/doc/LectureNotes/_build/jupyter_execute/E1.ipynb
index 65a994b52..cdc395c9b 100644
--- a/doc/LectureNotes/_build/jupyter_execute/E1.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/E1.ipynb
@@ -125,7 +125,7 @@
     "\n",
     "There are many ways to manage your packages in Python, and you are free to use any approach you want, but in this course we encourage you to use something called a virtual environment. A virtual environemnt is a folder in your project which contains a Python runtime executable as well as all the packages you are using in the current project. In this way, each of your projects has its required set of packages installed in the same folder, so that if anything goes wrong while managing your packages it only affects the one project, and if multiple projects require different versions of the same package, you don't need to worry about messing up old projects. Also, it's easy to just delete the folder and start over if anything goes wrong.\n",
     "\n",
-    "Virtual environments are typically created, activated, managed and updated using terminal commands, but for now we recommend that you let VS Code handle it for you to make the coding experience much easier.\n"
+    "Virtual environments are typically created, activated, managed and updated using terminal commands, but for now we recommend that you let VS Code handle it for you to make the coding experience much easier. If you are familiar with another approach for virtual environments that works for you, feel free to keep doing it that way.\n"
    ]
   },
   {
@@ -178,7 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 1,
    "id": "b58fb9bf",
    "metadata": {},
    "outputs": [],
@@ -192,13 +192,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 2,
    "id": "0208e9ca",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -214,10 +214,12 @@
     "\n",
     "line_model = LinearRegression().fit(x, y)\n",
     "line_predict = line_model.predict(x)\n",
+    "#line_mse = ...\n",
     "\n",
     "#poly_features = ...\n",
     "#poly_model = LinearRegression().fit(..., y)\n",
     "#poly_predict = ...\n",
+    "#poly_mse = ...\n",
     "\n",
     "plt.scatter(x, y, label = \"Data\")\n",
     "plt.scatter(x, line_predict, label = \"Line model\")\n",
@@ -243,7 +245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": null,
    "id": "0f8d75fb",
    "metadata": {},
    "outputs": [],
@@ -269,14 +271,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": null,
    "id": "a03e0388",
    "metadata": {},
    "outputs": [],
    "source": [
     "polynomial_features = ...\n",
     "\n",
-    "#X_train, X_test, y_train, y_test = train_test_split(polynomial_features, y, test_size=0.2)"
+    "#X_train, X_test, y_train, y_test = train_test_split(polynomial_features, y, test_size=0.2)\n",
+    "\n"
    ]
   },
   {
diff --git a/doc/LectureNotes/_build/jupyter_execute/E2.ipynb b/doc/LectureNotes/_build/jupyter_execute/E2.ipynb
index 56979ea3b..1512be049 100644
--- a/doc/LectureNotes/_build/jupyter_execute/E2.ipynb
+++ b/doc/LectureNotes/_build/jupyter_execute/E2.ipynb
@@ -224,12 +224,16 @@
    "source": [
     "With the expression for $\\boldsymbol{\\hat{\\beta}_{OLS}}$, you now have what you need to implement OLS regression with your input data and target data $\\boldsymbol{y}$. But before you can do that, you need to set up you input data as a feature matrix $\\boldsymbol{X}$.\n",
     "\n",
-    "In a feature matrix, each row is a datapoint and each column is a feature of that data. If you want to predict someones spending based on their income and number of children, for instance, you would create a row for each person in your dataset, and in each column put a 1 for the intercept, the montly income and the number of children."
+    "In a feature matrix, each row is a datapoint and each column is a feature of that data. If you want to predict someones spending based on their income and number of children, for instance, you would create a row for each person in your dataset, with the montly income and the number of children as columns.\n",
+    "\n",
+    "We typically also include an intercept in our models. The intercept is a value that is added to our prediction regardless of the value of the other features. The intercept tries to account for constant effects in our data that are not dependant on anything else. In our current example, the intercept could account for living expenses which are typical regardless of income or childcare expenses.\n",
+    "\n",
+    "We calculate the optimal intercept by including a feature with the constant value of 1 in our model, which is then multplied by some parameter $\\beta_0$ from the OLS method into the optimal intercept value (which will be $\\beta_0$). In practice, we include the intercept in our model by adding a column of ones to the start of our feature matrix."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": null,
    "id": "e5ff2a69",
    "metadata": {},
    "outputs": [],
@@ -239,7 +243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": null,
    "id": "a3cf2792",
    "metadata": {},
    "outputs": [],
@@ -260,7 +264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": null,
    "id": "5ad87a65",
    "metadata": {},
    "outputs": [],
@@ -281,7 +285,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "id": "8f3f68aa",
    "metadata": {},
    "outputs": [],
@@ -312,7 +316,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "id": "d7476c84",
    "metadata": {},
    "outputs": [],
@@ -332,7 +336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": null,
    "id": "91496e40",
    "metadata": {},
    "outputs": [],
@@ -358,7 +362,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": null,
    "id": "034f502c",
    "metadata": {},
    "outputs": [],
@@ -376,7 +380,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": null,
    "id": "29171358",
    "metadata": {},
    "outputs": [],
@@ -396,21 +400,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": null,
    "id": "1e346f4c",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Ellipsis"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "..."
    ]
@@ -425,21 +418,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
    "id": "ceb57457",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Ellipsis"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "..."
    ]
diff --git a/doc/LectureNotes/_build/jupyter_execute/c62786c19b580c6638248aa3cfe7dea30bdbc00a922e7e45a45a5a7a053bdb38.png b/doc/LectureNotes/_build/jupyter_execute/c62786c19b580c6638248aa3cfe7dea30bdbc00a922e7e45a45a5a7a053bdb38.png
new file mode 100644
index 000000000..000d16539
Binary files /dev/null and b/doc/LectureNotes/_build/jupyter_execute/c62786c19b580c6638248aa3cfe7dea30bdbc00a922e7e45a45a5a7a053bdb38.png differ
diff --git a/doc/LectureNotes/_build/jupyter_execute/fbd74da3fcd21613c4c425622594644bbfe45b6a7fd50ac62da4e3136a737128.png b/doc/LectureNotes/_build/jupyter_execute/fbd74da3fcd21613c4c425622594644bbfe45b6a7fd50ac62da4e3136a737128.png
deleted file mode 100644
index 6c951d90d..000000000
Binary files a/doc/LectureNotes/_build/jupyter_execute/fbd74da3fcd21613c4c425622594644bbfe45b6a7fd50ac62da4e3136a737128.png and /dev/null differ