Remove unnecessary link

Author: havk64

01_Introduction/readme.md

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-## [Ch 1: Getting Started with TensorFlow](#ch-1-getting-started-with-tensorflow)
+## Ch 1: Getting Started with TensorFlow
 
 This chapter intends to introduce the main objects and concepts in TensorFlow.  We also introduce how to access the data for the rest of the book and provide additional resources for learning about TensorFlow.
 

02_TensorFlow_Way/readme.md

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-## [Ch 2: The TensorFlow Way](#ch-2-the-tensorflow-way)
+## Ch 2: The TensorFlow Way
 
 After we have established the basic objects and methods in TensorFlow, we now want to establish the components that make up TensorFlow algorithms.  We start by introducing computational graphs, and then move to loss functions and back propagation.  We end with creating a simple classifier and then show an example of evaluating regression and classification algorithms.
 

03_Linear_Regression/readme.md

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-## [Ch 3: Linear Regression](#ch-3-linear-regression)
+## Ch 3: Linear Regression
 
 Here we show how to implement various linear regression techniques in TensorFlow.  The first two sections show how to do standard matrix linear regression solving in TensorFlow.  The remaining six sections depict how to implement various types of regression using computational graphs in TensorFlow.
 

04_Support_Vector_Machines/readme.md

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-## [Ch 4: Support Vector Machines](#ch-4-support-vector-machines)
+## Ch 4: Support Vector Machines
 
 This chapter shows how to implement various SVM methods with TensorFlow.  We first create a linear SVM and also show how it can be used for regression.  We then introduce kernels (RBF Gaussian kernel) and show how to use it to split up non-linear data. We finish with a multi-dimensional implementation of non-linear SVMs to work with multiple classes.
 

05_Nearest_Neighbor_Methods/readme.md

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-## [Ch 5: Nearest Neighbor Methods](#ch-5-nearest-neighbor-methods)
+## Ch 5: Nearest Neighbor Methods
 
 Nearest Neighbor methods are a very popular ML algorithm.  We show how to implement k-Nearest Neighbors, weighted k-Nearest Neighbors, and k-Nearest Neighbors with mixed distance functions.  In this chapter we also show how to use the Levenshtein distance (edit distance) in TensorFlow, and use it to calculate the distance between strings. We end this chapter with showing how to use k-Nearest Neighbors for categorical prediction with the MNIST handwritten digit recognition.
 

06_Neural_Networks/readme.md

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-## [Ch 6: Neural Networks](#ch-6-neural-networks)
+## Ch 6: Neural Networks
 
 Neural Networks are very important in machine learning and growing in popularity due to the major breakthroughs in prior unsolved problems.  We must start with introducing 'shallow' neural networks, which are very powerful and can help us improve our prior ML algorithm results.  We start by introducing the very basic NN unit, the operational gate.  We gradually add more and more to the neural network and end with training a model to play tic-tac-toe.
 

07_Natural_Language_Processing/readme.md

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-## [Ch 7: Natural Language Processing](#ch-7-natural-language-processing)
+## Ch 7: Natural Language Processing
 
  1. [Introduction](01_Introduction)
   * We introduce methods for turning text into numerical vectors. We introduce the TensorFlow 'embedding' feature as well.

08_Convolutional_Neural_Networks/readme.md

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-## [Ch 8: Convolutional Neural Networks](#ch-8-convolutional-neural-networks)
+## Ch 8: Convolutional Neural Networks
 
  1. [Introduction](01_Intro_to_CNN)
   * We introduce convolutional neural networks (CNN), and how we can use them in TensorFlow.

09_Recurrent_Neural_Networks/readme.md

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-## [Ch 9: Recurrent Neural Networks](#ch-9-recurrent-neural-networks)
+## Ch 9: Recurrent Neural Networks
 
  1. [Introduction](01_Introduction)
   * We introduce Recurrent Neural Networks and how they are able to feed in a sequence and predict either a fixed target (categorical/numerical) or another sequence (sequence to sequence).

10_Taking_TensorFlow_to_Production/readme.md

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-## [Ch 10:Taking TensorFlow to Production](#ch-10-taking-tensorflow-to-production)
+## Ch 10:Taking TensorFlow to Production
 
  1. [Implementing Unit Tests](01_Implementing_Unit_Tests)
   * We show how to implement different types of unit tests on tensors (placeholders and variables).