Leverage the power of deep learning and Keras to develop smarter and more efficient data models

Key Features

Understand different neural networks and their implementation using Keras

Explore recipes for training and fine-tuning your neural network models

Put your deep learning knowledge to practice with real-world use-cases, tips, and tricks

Book Description

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

What you will learn

Install and configure Keras in TensorFlow

Master neural network programming using the Keras library

Understand the different Keras layers

Use Keras to implement simple feed-forward neural networks, CNNs and RNNs

Work with various datasets and models used for image and text classification

Develop text summarization and reinforcement learning models using Keras

Who this book is for

Keras Deep Learning Cookbook is for you if you are a data scientist or machine learning expert who wants to find practical solutions to common problems encountered while training deep learning models. A basic understanding of Python and some experience in machine learning and neural networks is required for this book.