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Hands-On Deep Learning Algorithms with Python

Hands-On Deep Learning Algorithms with Python

By : Sudharsan Ravichandiran
4.1 (13)
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Hands-On Deep Learning Algorithms with Python

Hands-On Deep Learning Algorithms with Python

4.1 (13)
By: Sudharsan Ravichandiran

Overview of this book

Deep learning is one of the most popular domains in the AI space that allows you to develop multi-layered models of varying complexities. This book introduces you to popular deep learning algorithms—from basic to advanced—and shows you how to implement them from scratch using TensorFlow. Throughout the book, you will gain insights into each algorithm, the mathematical principles involved, and how to implement it in the best possible manner. The book starts by explaining how you can build your own neural networks, followed by introducing you to TensorFlow, the powerful Python-based library for machine learning and deep learning. Moving on, you will get up to speed with gradient descent variants, such as NAG, AMSGrad, AdaDelta, Adam, and Nadam. The book will then provide you with insights into recurrent neural networks (RNNs) and LSTM and how to generate song lyrics with RNN. Next, you will master the math necessary to work with convolutional and capsule networks, widely used for image recognition tasks. You will also learn how machines understand the semantics of words and documents using CBOW, skip-gram, and PV-DM. Finally, you will explore GANs, including InfoGAN and LSGAN, and autoencoders, such as contractive autoencoders and VAE. By the end of this book, you will be equipped with all the skills you need to implement deep learning in your own projects.
Table of Contents (17 chapters)
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Section 1: Getting Started with Deep Learning
4
Section 2: Fundamental Deep Learning Algorithms
10
Section 3: Advanced Deep Learning Algorithms

Translating images using a CycleGAN

We have learned several types of GANs, and the applications of them are endless. We have seen how the generator learns the distribution of real data and generates new realistic samples. We will now see a really different and very innovative type of GAN called the CycleGAN.

Unlike other GANs, the CycleGAN maps the data from one domain to another domain, which implies that here we try to learn the mapping from the distribution of images from one domain to the distribution of images in another domain. To put it simply, we translate images from one domain to another.

What does this mean? Assume we want to convert a grayscale image to a colored image. The grayscale image is one domain and the colored image is another domain. A CycleGAN learns the mapping between these two domains and translates between them. This means that given a grayscale image...

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