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Get up to speed with building your own neural networks from scratch
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Gain insights into the mathematical principles behind deep learning algorithms
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Implement popular deep learning algorithms such as CNNs, RNNs, and more using TensorFlow
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.
If you are a machine learning engineer, data scientist, AI developer, or anyone looking to delve into neural networks and deep learning, this book is for you. Those who are completely new to deep learning, but have some experience in machine learning and Python programming will also find the book very helpful.
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Implement basic-to-advanced deep learning algorithms
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Master the mathematics behind deep learning algorithms
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Become familiar with gradient descent and its variants, such as AMSGrad, AdaDelta, Adam, and Nadam
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Implement recurrent networks, such as RNN, LSTM, GRU, and seq2seq models
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Understand how machines interpret images using CNN and capsule networks
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Implement different types of generative adversarial network, such as CGAN, CycleGAN, and StackGAN
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Explore various types of autoencoder, such as Sparse autoencoders, DAE, CAE, and VAE
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