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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Hands-On Deep Learning Algorithms with Python - Master deep learning algorithms with extensive math by implementing them using TensorFlow (Paperback)
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Hands-On Deep Learning Algorithms with Python - Master deep learning algorithms with extensive math by implementing them using TensorFlow (Paperback)
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Understand basic to advanced deep learning algorithms, the
mathematical principles behind them, and their practical
applications. Key Features Get up-to-speed with building your own
neural networks from scratch Gain insights into the mathematical
principles behind deep learning algorithms Implement popular deep
learning algorithms such as CNNs, RNNs, and more using TensorFlow
Book DescriptionDeep learning is one of the most popular domains in
the AI space, allowing 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 behind it, 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 RNNs and LSTM and how to
generate song lyrics with RNN. Next, you will master the math for
convolutional and capsule networks, widely used for image
recognition tasks. Then you learn how machines understand the
semantics of words and documents using CBOW, skip-gram, and PV-DM.
Afterward, you will explore various 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. What you
will learn Implement basic-to-advanced deep learning algorithms
Master the mathematics behind deep learning algorithms Become
familiar with gradient descent and its variants, such as AMSGrad,
AdaDelta, Adam, and Nadam Implement recurrent networks, such as
RNN, LSTM, GRU, and seq2seq models Understand how machines
interpret images using CNN and capsule networks Implement different
types of generative adversarial network, such as CGAN, CycleGAN,
and StackGAN Explore various types of autoencoder, such as Sparse
autoencoders, DAE, CAE, and VAE Who this book is forIf you are a
machine learning engineer, data scientist, AI developer, or simply
want to focus on 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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