#### Overview of this book

TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and allow you to dig deeper and gain more insights into your data than ever before. With the help of this book, you will work with recipes for training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and more. You will explore RNNs, CNNs, GANs, reinforcement learning, and capsule networks, each using Google's machine learning library, TensorFlow. Through real-world examples, you will get hands-on experience with linear regression techniques with TensorFlow. Once you are familiar and comfortable with the TensorFlow ecosystem, you will be shown how to take it to production. By the end of the book, you will be proficient in the field of machine intelligence using TensorFlow. You will also have good insight into deep learning and be capable of implementing machine learning algorithms in real-world scenarios.
Preface
Free Chapter
Getting Started with TensorFlow
The TensorFlow Way
Support Vector Machines
Nearest-Neighbor Methods
Natural Language Processing
Convolutional Neural Networks
Recurrent Neural Networks
Taking TensorFlow to Production
More with TensorFlow
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# Introduction

Of all the machine learning algorithms we have considered thus far, none have considered data as a sequence. To take sequence data into account, we extend neural networks that store outputs from prior iterations. This type of neural network is called an RNN. Consider the fully connected network formulation:

Here, the weights are given by A multiplied by the input layer, x, and then run through an activation function, , which gives the output layer, y.

If we have a sequence of input data, , we can adapt the fully connected layer to take prior inputs into account, as follows:

On top of this recurrent iteration to get the next input, we want to get the probability distribution output, as follows:

Once we have a full sequence output, , we can consider the target as a number or category by just considering the last output. See the following diagram for how a general...