Take the next step in implementing various common and not-so-common neural networks with Tensorflow 1.x

Key Features:

• Skill up and implement tricky neural networks using Google's TensorFlow 1.x
• An easy-to-follow guide that lets you explore reinforcement learning, GANs, autoencoders, multilayer perceptrons and more.
• Hands-on recipes to work with Tensorflow on desktop, mobile, and cloud environment

Book Description:

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.

What You Will Learn:

• Leverage different data sets such as MNIST, CIFAR-10, and Youtube8m with TensorFlow and learn how to access and use them in your code
• Use TensorBoard to understand neural network architectures, optimize the learning process, and peek inside the neural network black box
• Use different regression techniques for prediction and classifi cation problems
• Build single and multilayer perceptrons in TensorFlow
• Implement a CNN and a RNN in TensorFlow, and use them to solve real-world problems
• Learn how Restricted Boltzmann Machines can be used to recommend movies
• Understand the implementation of autoencoders and deep belief networks, and use them for emotion detection
• Master the different reinforcement learning methods in order to implement game playing agents

Who this book is for:

This book is intended for data analysts, data scientists, machine learning practitioners and deep learning enthusiasts who want to perform deep learning tasks on a regular basis and are looking for a handy guide they can refer to. People who are slightly familiar with neural networks, and now want to gain expertise in working with different types of neural networks and datasets, will find this book quite useful