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


Why is human language so special? Human, or natural, language is a method that developed to convey meaning and is not produced by a physical action of any kind. It is quite different from vision or any other machine learning task.

Natural language processing (NLP) is one of the types of Artificial Intelligence (AI) that allow machines to analyze and understand the human language. NLP was begun to develop software that generates and understands natural languages so that a user can have natural conversations with his/her computer. NLP combines AI with computational linguistics and computer science to process human languages and speech.

Examples of NLP include sentiment analysis, chatbots, document classification, word clustering, machine translation, and many more. This list is long, and the scenarios in which one can use NLP are even greater in number. This chapter aims to introduce you to recipes with an understanding of NLP techniques as applied to deep learning models so that...