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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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Generating region proposals within an image, using selective search

To understand what a region proposal is, let's break the term into its constituents—region and proposal.

A region is a portion of the total image where the pixels in that portion have very similar values.

A region proposal is the smaller portion of the total image, where there is a higher chance of the portion belonging to a particular object.

A region proposal is useful, as we generate candidates from the image where the chances of an object being located in one of those regions is high. This comes in handy in the object localization tasks, where we need to have a bounding box around the object that is similar to what we have in the picture in the previous section.

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