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


Convolutional neural networks (CNNs) are networks of neurons that have learnable weights and biases. Every neuron accepts inputs, calculates a dot product, and follows it with a nonlinearity. CNNs are composed of several convolutional layers and are then followed by one or more fully connected layers, as in a standard multilayer neural network, starting from the raw image pixels on one end to class scores at the other. CNNs preserve the spatial relationship between pixels by learning feature representations. The feature is learned and applied across the whole image, allowing for the objects in the images to be shifted or translated in the scene and still be detectable by the network.

In a nutshell, CNNs are, fundamentally, several layers of convolutions with nonlinear activation functions, such as ReLU or tanh, applied to the results.

Applications for CNNs include relation extraction and relation classification tasks, image moderation, and natural language processing. This list...