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.

