Leverage the power of deep learning and Keras to solve complex computational problems

• Recipes on training and fine-tuning your neural network models efficiently using Keras;
• A highly practical guide to simplify your understanding of neural networks and their implementation;
• This book is a must-have on your shelf if you are planning to put your deep learning knowledge to practical use.

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.

This book shows you how to tackle different problems in training efficient deep learning models using the popular Keras library. Starting with installing and setting up of Keras, the book demonstrates how you can perform deep learning with Keras on top of Tensorflow, Apache MXNet and CNTK backend’s. From loading the data to fitting and evaluating your model for optimal performance, you will go through a step by step process to tackle every possible problem in training deep models. You will implement efficient convolutional neural networks, recurrent neural networks, adversarial networks and more, with the help of this handy guide. You will also see 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.

• Install and configure Keras on top of Tensorflow, Apache MXNet and CNTK;
• Develop a strong background in neural network programming using the Keras library;
• Understand the details of different Keras layers like Core, Embedding and so on;
• Use Keras to implement simple feed-forward neural networks and the more complex CNNs, RNNs;
• Work with various datasets, models used for image and text classification;
• Develop text summarization and Reinforcement Learning models using Keras.