Updated project 2

doc/Projects/2018/Project2/html/Project2-bs.html

@@ -162,7 +162,7 @@ MathJax.Hub.Config({
 <center><b>Department of Physics, University of Oslo, Norway</b></center>
 <br>
 <p>
-<center><h4>Oct 9, 2018</h4></center> <!-- date -->
+<center><h4>Oct 12, 2018</h4></center> <!-- date -->
 <br>
 <p>
 </div> <!-- end jumbotron -->
@@ -429,7 +429,7 @@ standard gradient descent with a given learning rate, or even attempt
 to use the Newton-Raphson method.  Alternatively, it may be useful for
 the next part on neural networks to implement a stochastic gradient
 descent. For all  gradient methods, you can use <b>scikit-learn</b>'s toolbox for
-optimization methods instead of writing your own code.
+optimization methods in order to test your own results.
 
 <p>
 The notebook of <a href="https://physics.bu.edu/~pankajm/ML-Notebooks/HTML/NB_CVII-logreg_ising.html" target="_self">Mehta et al</a> is highly recommended in order to benchmark your code and results.
@@ -447,10 +447,11 @@ to use the codes in the above lecture slides as starting points.
 
 <p>
 Train your network and compare the results with those from your linear regression code. 
-You can test your results against a similar code using _scikit_learn_ (see the examples in teh above lecture notes) or <b>tensorflow/keras</b>.
+You can test your results against a similar code using _scikit_learn_ (see the examples in the above lecture notes) or <b>tensorflow/keras</b>.
 
 <p>
 You should have the same elements as in the regression examples, including the \( R2 \) score, the MSE, and bootstrap or cross-validation.
+You may also need to think of using another activation function than the standard logistic function.
 
 <p>
 A useful reference on the back progagation algorithm is <a href="http://neuralnetworksanddeeplearning.com/" target="_self">Nielsen's book</a>. It is an excellent read.

doc/Projects/2018/Project2/html/Project2.html

@@ -120,7 +120,7 @@ MathJax.Hub.Config({
 <center><b>Department of Physics, University of Oslo, Norway</b></center>
 <br>
 <p>
-<center><h4>Oct 9, 2018</h4></center> <!-- date -->
+<center><h4>Oct 12, 2018</h4></center> <!-- date -->
 <br>
 
 <h2 id="___sec0">Classification and Regression, from linear and logistic regression to neural networks  </h2>
@@ -385,7 +385,7 @@ standard gradient descent with a given learning rate, or even attempt
 to use the Newton-Raphson method.  Alternatively, it may be useful for
 the next part on neural networks to implement a stochastic gradient
 descent. For all  gradient methods, you can use <b>scikit-learn</b>'s toolbox for
-optimization methods instead of writing your own code.
+optimization methods in order to test your own results.
 
 <p>
 The notebook of <a href="https://physics.bu.edu/~pankajm/ML-Notebooks/HTML/NB_CVII-logreg_ising.html" target="_blank">Mehta et al</a> is highly recommended in order to benchmark your code and results.
@@ -403,10 +403,11 @@ to use the codes in the above lecture slides as starting points.
 
 <p>
 Train your network and compare the results with those from your linear regression code. 
-You can test your results against a similar code using _scikit_learn_ (see the examples in teh above lecture notes) or <b>tensorflow/keras</b>.
+You can test your results against a similar code using _scikit_learn_ (see the examples in the above lecture notes) or <b>tensorflow/keras</b>.
 
 <p>
 You should have the same elements as in the regression examples, including the \( R2 \) score, the MSE, and bootstrap or cross-validation.
+You may also need to think of using another activation function than the standard logistic function.
 
 <p>
 A useful reference on the back progagation algorithm is <a href="http://neuralnetworksanddeeplearning.com/" target="_blank">Nielsen's book</a>. It is an excellent read.

doc/Projects/2018/Project2/pdf/Project2.p.tex

@@ -155,7 +155,7 @@ Project 2 on Machine Learning, deadline November 5
 
 % --- begin date ---
 \begin{center}
-Oct 9, 2018
+Oct 12, 2018
 \end{center}
 % --- end date ---
 
@@ -401,7 +401,7 @@ standard gradient descent with a given learning rate, or even attempt
 to use the Newton-Raphson method.  Alternatively, it may be useful for
 the next part on neural networks to implement a stochastic gradient
 descent. For all  gradient methods, you can use \textbf{scikit-learn}'s toolbox for
-optimization methods instead of writing your own code.
+optimization methods in order to test your own results.
 
 
 The notebook of \href{{https://physics.bu.edu/~pankajm/ML-Notebooks/HTML/NB_CVII-logreg_ising.html}}{Mehta et al} is highly recommended in order to benchmark your code and results.
@@ -416,10 +416,10 @@ now the network to find the optimal weights and biases. You are free
 to use the codes in the above lecture slides as starting points.
 
 Train your network and compare the results with those from your linear regression code. 
-You can test your results against a similar code using _scikit_learn_ (see the examples in teh above lecture notes) or \textbf{tensorflow/keras}. 
+You can test your results against a similar code using _scikit_learn_ (see the examples in the above lecture notes) or \textbf{tensorflow/keras}. 
 
 You should have the same elements as in the regression examples, including the $R2$ score, the MSE, and bootstrap or cross-validation.
-
+You may also need to think of using another activation function than the standard logistic function.
 
 A useful reference on the back progagation algorithm is \href{{http://neuralnetworksanddeeplearning.com/}}{Nielsen's book}. It is an excellent read.
 

doc/Projects/2018/Project2/pdf/Project2.tex

@@ -125,7 +125,7 @@ Project 2 on Machine Learning, deadline November 5
 
 % --- begin date ---
 \begin{center}
-Oct 9, 2018
+Oct 12, 2018
 \end{center}
 % --- end date ---
 
@@ -371,7 +371,7 @@ standard gradient descent with a given learning rate, or even attempt
 to use the Newton-Raphson method.  Alternatively, it may be useful for
 the next part on neural networks to implement a stochastic gradient
 descent. For all  gradient methods, you can use \textbf{scikit-learn}'s toolbox for
-optimization methods instead of writing your own code.
+optimization methods in order to test your own results.
 
 
 The notebook of \href{{https://physics.bu.edu/~pankajm/ML-Notebooks/HTML/NB_CVII-logreg_ising.html}}{Mehta et al} is highly recommended in order to benchmark your code and results.
@@ -386,10 +386,10 @@ now the network to find the optimal weights and biases. You are free
 to use the codes in the above lecture slides as starting points.
 
 Train your network and compare the results with those from your linear regression code. 
-You can test your results against a similar code using _scikit_learn_ (see the examples in teh above lecture notes) or \textbf{tensorflow/keras}. 
+You can test your results against a similar code using _scikit_learn_ (see the examples in the above lecture notes) or \textbf{tensorflow/keras}. 
 
 You should have the same elements as in the regression examples, including the $R2$ score, the MSE, and bootstrap or cross-validation.
-
+You may also need to think of using another activation function than the standard logistic function.
 
 A useful reference on the back progagation algorithm is \href{{http://neuralnetworksanddeeplearning.com/}}{Nielsen's book}. It is an excellent read.
 

doc/src/Projects/2018/Project2/Project2.do.txt

@@ -255,7 +255,7 @@ standard gradient descent with a given learning rate, or even attempt
 to use the Newton-Raphson method.  Alternatively, it may be useful for
 the next part on neural networks to implement a stochastic gradient
 descent. For all  gradient methods, you can use _scikit-learn_'s toolbox for
-optimization methods instead of writing your own code.
+optimization methods in order to test your own results.
 
 
 The notebook of "Mehta et al":"https://physics.bu.edu/~pankajm/ML-Notebooks/HTML/NB_CVII-logreg_ising.html" is highly recommended in order to benchmark your code and results.
@@ -271,10 +271,10 @@ now the network to find the optimal weights and biases. You are free
 to use the codes in the above lecture slides as starting points.
 
 Train your network and compare the results with those from your linear regression code. 
-You can test your results against a similar code using _scikit_learn_ (see the examples in teh above lecture notes) or _tensorflow/keras_. 
+You can test your results against a similar code using _scikit_learn_ (see the examples in the above lecture notes) or _tensorflow/keras_. 
 
 You should have the same elements as in the regression examples, including the $R2$ score, the MSE, and bootstrap or cross-validation.
-
+You may also need to think of using another activation function than the standard logistic function.
 
 A useful reference on the back progagation algorithm is "Nielsen's book":"http://neuralnetworksanddeeplearning.com/". It is an excellent read.