If you're aware of the buzz surrounding terms such as machine learning, artificial intelligence, or deep learning, you might know what neural networks are. Ever wondered how they help solve complex computational problems efficiently, or how to train efficient neural networks? This book will teach you both of these things, and more.You will start by getting a quick overview of the popular TensorFlow library and see 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 feedforward neural network. Next, you will master optimization techniques and algorithms for neural networks using TensorFlow. Further, you will learn how to implement some more complex types of neural networks such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Deep Belief Networks 0;(DBNs). 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 to leverage the power of TensorFlow to train neural networks of varying complexities, without any hassle.
Chapter 1, Maths for Neural Networks, covers the basics of algebra, probability, and optimization techniques for neural networks.
Chapter 2, Deep Feedforward Networks, explains the basics of perceptrons, neurons, and feedforward neural networks. You will also learn about various learning techniques and mainly the core learning algorithm called backpropagation.
Chapter 3, Optimization for Neural Networks, covers optimization techniques that are fundamental to neural network learning.
Chapter 4, Convolutional Neural Networks, discusses the CNN algorithm in detail. CNNs and their application to different data types will also be covered.
Chapter 5, Recurrent Neural Networks, covers the RNN algorithm in detail. RNNs and their application to different data types are covered as well.
Chapter 6, Generative Models, explains the basics of generative models and the different approaches to generative models.
Chapter 7, Deep Belief Networking, covers the basics of deep belief networks, how they differ from the traditional neural networks, and their implementation.
Chapter 8, Autoencoders, provides an introduction to autoencoders, which have recently come to the forefront of generative modeling.
Chapter 9, Deep Learning Research and Summary, discusses the current and future research details on deep learning. It also points the readers to papers for reference reading.
Appendix, Getting Started with TensorFlow, discusses environment setup of TensorFlow, comparison of TensorFlow with NumPy, and the concept if Auto differentiation
This book will guide you through the installation of all the tools that you need to follow the examples:
- Python 3.4 or above
- TensorFlow r.14 or above
This book is meant for developers with a statistical background who want to work with neural networks. Though we will be using TensorFlow as the underlying library for neural networks, this book can be used as a generic resource to bridge the gap between the math and the implementation of deep learning. If you have some understanding of Tensorflow and Python and want to learn what happens at a level lower than the plain API syntax, this book is for you.
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