Book Image

TensorFlow 1.x Deep Learning Cookbook

Book Image

TensorFlow 1.x Deep Learning Cookbook

Overview of this book

Deep neural networks (DNNs) have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. This exciting recipe-based guide will take you from the realm of DNN theory to implementing them practically to solve real-life problems in the artificial intelligence domain. In this book, you will learn how to efficiently use TensorFlow, Google’s open source framework for deep learning. You will implement different deep learning networks, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learning Networks (DQNs), and Generative Adversarial Networks (GANs), with easy-to-follow standalone recipes. You will learn how to use TensorFlow with Keras as the backend. You will learn how different DNNs perform on some popularly used datasets, such as MNIST, CIFAR-10, and Youtube8m. You will not only learn about the different mobile and embedded platforms supported by TensorFlow, but also how to set up cloud platforms for deep learning applications. You will also get a sneak peek at TPU architecture and how it will affect the future of DNNs. By using crisp, no-nonsense recipes, you will become an expert in implementing deep learning techniques in growing real-world applications and research areas such as reinforcement learning, GANs, and autoencoders.
Table of Contents (15 chapters)
14
TensorFlow Processing Units

TensorFlow - An Introduction

Anyone who has ever tried to write code for neural networks in Python using only NumPy, knows how cumbersome it is. Writing code for a simple one-layer feedforward network requires more than 40 lines, made more difficult as you add the number of layers both in terms of writing code and execution time.

TensorFlow makes it all easier and faster reducing the time between the implementation of an idea and deployment. In this book, you will learn how to unravel the power of TensorFlow to implement deep neural networks.

In this chapter, we will cover the following topics:

  • Installing TensorFlow
  • Hello world in TensorFlow
  • Understanding the TensorFlow program structure
  • Working with constants, variables, and placeholders
  • Performing matrix manipulations using TensorFlow
  • Using a data flow graph
  • Migrating from 0.x to 1.x
  • Using XLA to enhance computational performance
  • Invoking CPU/GPU devices
  • TensorFlow for deep learning
  • Different Python packages required for DNN-based problems