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Neural Networks with Keras Cookbook

Neural Networks with Keras Cookbook

By : V Kishore Ayyadevara
3.3 (8)
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Neural Networks with Keras Cookbook

Neural Networks with Keras Cookbook

3.3 (8)
By: V Kishore Ayyadevara

Overview of this book

This book will take you from the basics of neural networks to advanced implementations of architectures using a recipe-based approach. We will learn about how neural networks work and the impact of various hyper parameters on a network's accuracy along with leveraging neural networks for structured and unstructured data. Later, we will learn how to classify and detect objects in images. We will also learn to use transfer learning for multiple applications, including a self-driving car using Convolutional Neural Networks. We will generate images while leveraging GANs and also by performing image encoding. Additionally, we will perform text analysis using word vector based techniques. Later, we will use Recurrent Neural Networks and LSTM to implement chatbot and Machine Translation systems. Finally, you will learn about transcribing images, audio, and generating captions and also use Deep Q-learning to build an agent that plays Space Invaders game. By the end of this book, you will have developed the skills to choose and customize multiple neural network architectures for various deep learning problems you might encounter.
Table of Contents (18 chapters)
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Introduction

Consider a scenario where we are transcribing the image of a handwritten text. In this case, we would be dealing with image data and also sequential data (as the content in the image needs to be transcribed sequentially).

In traditional analysis, we would have hand-crafted the solution—for example: we might have slid a window across the image (where the window is of the average size of a character) so that the window would detect each character, and then output characters that it detects, with high confidence.

However, in this scenario, the size of the window or the number of windows we shall slide is hand crafted by us—which becomes a feature-engineering (feature generation) problem.

A more end-to-end approach shall be extracting the features obtained by passing the image through a CNN and then passing these features as inputs to various time steps...

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