TensorFlow 1.x Deep Learning Cookbook

Key Features
Develop your skills to implement advance techniques in deep learning using Google's Tensorflow 1.x
Implement real-world and practical examples to illustrate deep learning techniques.
Hands-on recipes to learn how to design and train a multi-layer neural network with TensorFlow 1.x
Book Description
Deep neural networks (DNN) in the past few years have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. The AI, ML community is filled with excitement on buzz word "Deep networks". Director of DARPA's Information Innovation Office, John Launchbury calls the success of DNNs as the second wave of AI.

In this book you will learn the use of Tensorflow, Google's framework for deep learning, for implementing different deep learning networks like Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Deep Q-learning Networks (DQN).

You will understand how to implement different deep neural architectures in Tensorflow. You will learn the performance of different DNNs on some popularly used data sets like MNIST, CIFAR-10, Youtube8m etc. You will learn to use Keras as backend. We will not only learn about the different mobile and embedded platforms supported by Tensorflow but also how to setup cloud platforms for deep learning applications. This exciting recipe based guide will take you from the realm of theory of DNNs to practically implementing them for solving the real life AI-driven problems.

What you will learn
Install Tensorflow and use it for CPU and GPU options.
Implement DNNs and apply the knowledge to solve different AI-driven problems.
Use Tensorflow to implement DNNs and apply the knowledge to solve different AI-driven problems.
Peek into different data sets available with the Tensorflow, how to access them and use them in your code.
Learn the use of Tensorboard to understand the architecture, optimize the learning process and peek inside the neural network black box.
Use different regression techniques for the task of prediction and classification. You will apply them for predicting house prices and identification of handwritten digits.
Implement single and multilayer Perceptrons in Tensorflow and use them for the identification of handwritten digits
Implement CNN in Tensorflow, and use it to classify CIFAR-10 images.
Process images and use CNN to differentiate between cats and Dogs.
Understand RNN and implement it to perform the task of text generation.
Learn about restricted Boltzmann Machines, implement them in Tensorflow and use it for recommending movies.
Understand the implementation of Autoencoders, and deep belief networks, use them for emotion detection.
Different Reinforcement Learning methods and their implementation. Use them for making a game playing agent.
GANs and its implementation in Tensorflow