In this chapter, we addressed some of the areas where research has been done on improving accuracy and avoiding overfitting. We also looked at some of the newer areas such as video classification. While it is outside the scope of this book to cover all the research areas in detail, we sincerely advise you to explore the research websites of Google, Facebook, and Baidu, in addition to Tier 1 ACM and IEEE conferences, to skim through new research being done.

#### Neural Network Programming with TensorFlow

##### By :

#### Neural Network Programming with TensorFlow

##### By:

#### Overview of this book

If you're aware of the buzz surrounding the terms such as "machine learning," "artificial intelligence," or "deep learning," you might know what neural networks are. Ever wondered how they help in solving complex computational problem efficiently, or how to train efficient neural networks? This book will teach you just that.
You will start by getting a quick overview of the popular TensorFlow library and how it is used to train different neural networks. You will get a thorough understanding of the fundamentals and basic math for neural networks and why TensorFlow is a popular choice Then, you will proceed to implement a simple feed forward neural network. Next you will master optimization techniques and algorithms for neural networks using TensorFlow. Further, you will learn to implement some more complex types of neural networks such as convolutional neural networks, recurrent neural networks, and Deep Belief Networks. In the course of the book, you will be working on real-world datasets to get a hands-on understanding of neural network programming. You will also get to train generative models and will learn the applications of autoencoders.
By the end of this book, you will have a fair understanding of how you can leverage the power of TensorFlow to train neural networks of varying complexities, without any hassle. While you are learning about various neural network implementations you will learn the underlying mathematics and linear algebra and how they map to the appropriate TensorFlow constructs.

Table of Contents (11 chapters)

Preface

Free Chapter

Maths for Neural Networks

Deep Feedforward Networks

Optimization for Neural Networks

Convolutional Neural Networks

Recurrent Neural Networks

Generative Models

Deep Belief Networking

Autoencoders

Research in Neural Networks

Getting started with TensorFlow

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