Book Image

Keras Deep Learning Cookbook

By : Rajdeep Dua, Sujit Pal, Manpreet Singh Ghotra
Book Image

Keras Deep Learning Cookbook

By: Rajdeep Dua, Sujit Pal, Manpreet Singh Ghotra

Overview of this book

Keras has quickly emerged as a popular deep learning library. Written in Python, it allows you to train convolutional as well as recurrent neural networks with speed and accuracy. The Keras Deep Learning Cookbook shows you how to tackle different problems encountered while training efficient deep learning models, with the help of the popular Keras library. Starting with installing and setting up Keras, the book demonstrates how you can perform deep learning with Keras in the TensorFlow. From loading data to fitting and evaluating your model for optimal performance, you will work through a step-by-step process to tackle every possible problem faced while training deep models. You will implement convolutional and recurrent neural networks, adversarial networks, and more with the help of this handy guide. In addition to this, you will learn how to train these models for real-world image and language processing tasks. By the end of this book, you will have a practical, hands-on understanding of how you can leverage the power of Python and Keras to perform effective deep learning
Table of Contents (17 chapters)
Title Page
Copyright and Credits
Packt Upsell


Reinforcement learning is a subset of machine learning, where AI agents learn from the environment by interacting with it and improving their performance. This branch of AI learns by trial and error instead of human supervision. The following diagram illustrates how an AI agent acts on the environment and receives feedback after each action. Feedback is made up of two parts: reward and the next state of the environment. Rewards are defined by a human:

Google's DeepMind published a paper in 2013 about Playing Atari with Deep Reinforcement Learning. In this paper, a new algorithm called Deep Q Network (DQN). It explains how an AI agent can learn to play games by just observing the screen without any prior information about the game. The result of the experiment turned out to be pretty impressive in terms of accuracy. It opened the era of what is called deep reinforcement learning, a mix of deep learning and reinforcement learning.

The Q-Learning algorithm has a function called Q...