typo in dim red

2019-10-17 14:28:10 +02:00
parent 22b05b3422
commit 9ba4369c85
8 changed files with 6 additions and 9 deletions
@@ -156,8 +156,7 @@ turning an intractable problem into a tractable one.
 <p>
 Here we will discuss some of the most popular dimensionality
 reduction techniques: the principal component analysis PCA, Kernel PCA, and Locally Linear Embedding (LLE).
-
-<p>
+Furthermore, we will start by looking at some simple preprocessing of the data which allow us to rescale the data.
 </div>
 </div>

@@ -172,8 +172,7 @@ turning an intractable problem into a tractable one.
 <p>
 Here we will discuss some of the most popular dimensionality
 reduction techniques: the principal component analysis PCA, Kernel PCA, and Locally Linear Embedding (LLE).
-
-
+Furthermore, we will start by looking at some simple preprocessing of the data which allow us to rescale the data.
 </div>
 </section>

@@ -154,8 +154,7 @@ turning an intractable problem into a tractable one.
 <p>
 Here we will discuss some of the most popular dimensionality
 reduction techniques: the principal component analysis PCA, Kernel PCA, and Locally Linear Embedding (LLE).
-
-
+Furthermore, we will start by looking at some simple preprocessing of the data which allow us to rescale the data.
 </div>


@@ -159,8 +159,7 @@ turning an intractable problem into a tractable one.
 <p>
 Here we will discuss some of the most popular dimensionality
 reduction techniques: the principal component analysis PCA, Kernel PCA, and Locally Linear Embedding (LLE).
-
-
+Furthermore, we will start by looking at some simple preprocessing of the data which allow us to rescale the data.
 </div>


@@ -27,6 +27,7 @@
    "\n",
    "Here we will discuss some of the most popular dimensionality\n",
    "reduction techniques: the principal component analysis PCA, Kernel PCA, and Locally Linear Embedding (LLE).\n",
+    "Furthermore, we will start by looking at some simple preprocessing of the data which allow us to rescale the data.\n",
    "\n",
    "\n",
    "\n",
@@ -15,7 +15,7 @@ turning an intractable problem into a tractable one.

 Here we will discuss some of the most popular dimensionality
 reduction techniques: the principal component analysis PCA, Kernel PCA, and Locally Linear Embedding (LLE).
-
+Furthermore, we will start by looking at some simple preprocessing of the data which allow us to rescale the data. 
 !eblock