Book Image

Neural Network Programming with TensorFlow

By : Ghotra, Dua
Book Image

Neural Network Programming with TensorFlow

By: Ghotra, Dua

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)

TensorBoard


TensorFlow has a powerful built-in visualization tool called TensorBoard. It allows developers to interpret, visualize, and debug computational graphs. To visualize graph and metrics automatically in TensorBoard, TensorFlow writes events related to the execution of a computational graph to a particular folder.

This example shows a computational graph of the analysis done earlier:

To examine the graph, click on the graph tab on the top panel of TensorBoard. If the graph has several nodes, visualizing it in a single view can be hard. To make our visualization more accessible, we can organize the related operations into groups using tf.name_scope with specific names.