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)

The OpenAI version

As stated in the documentation available at its website (https://gym.openai.com/), OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms. The toolkit actually consists of a Python package that runs with both Python 2 and Python 3, and the website API, which is useful for uploading your own algorithm's performance results and comparing them with others (an aspect of the toolkit that we won't be exploring, actually).

The toolkit embodies the principles of reinforcement learning, where you have an environment and an agent: the agent can perform actions or inaction in the environment, and the environment will reply with a new state (representing the situation in the environment) and a reward, which is a score telling the agent if it is doing well or not. The Gym toolkit provides everything with the environment, therefore...