Book Image

TensorFlow Deep Learning Projects

By : Alexey Grigorev, Rajalingappaa Shanmugamani
Book Image

TensorFlow Deep Learning Projects

By: Alexey Grigorev, Rajalingappaa Shanmugamani

Overview of this book

TensorFlow is one of the most popular frameworks used for machine learning and, more recently, deep learning. It provides a fast and efficient framework for training different kinds of deep learning models, with very high accuracy. This book is your guide to master deep learning with TensorFlow with the help of 10 real-world projects. TensorFlow Deep Learning Projects starts with setting up the right TensorFlow environment for deep learning. You'll learn how to train different types of deep learning models using TensorFlow, including Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Generative Adversarial Networks. While doing this, you will build end-to-end deep learning solutions to tackle different real-world problems in image processing, recommendation systems, stock prediction, and building chatbots, to name a few. You will also develop systems that perform machine translation and use reinforcement learning techniques to play games. By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow, and will be able to build and train your own deep learning models with TensorFlow confidently.
Table of Contents (12 chapters)

Building GANs for Conditional Image Creation

Yann LeCun, Director of Facebook AI, has recently stated that "Generative Adversarial Networks is the most interesting idea in the last ten years in machine learning", and that is certainly confirmed by the elevated interest in academia about this deep learning solution. If you look at recent papers on deep learning (but also look at the leading trends on LinkedIn or Medium posts on the topic), there has really been an overproduction of variants of GANs.

You can get an idea of what a zoo the world of GANs has become just by glancing the continuously updated reference table, created by Hindu Puravinash, which can be found at https://github.com/hindupuravinash/the-gan-zoo/blob/master/gans.tsv or by studying the GAN timeline prepared by Zheng Liu, which can be found at https://github.com/dongb5/GAN-Timeline and can help you putting...