Understand basic to advanced deep learning algorithms by implementing them from scratch, along with their practical applications
Digital
AvailableLanguage | English |
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ISBN-13 | 9781789344158 |
No of pages | 512 |
Book Publisher | Packt Publishing |
Published Date | 25 Jul 2019 |
Sudharsan Ravichandiran is a data scientist, researcher, Artificial Intelligence enthusiast, and YouTuber (search for "Sudharsan reinforcement learning"). He completed his Bachelor's in Information Technology at Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning, including Natural Language Processing and computer vision. He is an open source contributor and loves answering questions on Stack Overflow. He also authored a best-seller, Hands-On Reinforcement Learning with Python, published by Packt Publishing.
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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.