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Deep Learning for Beginners - A beginner's guide to getting up and running with deep learning from scratch using Python (Paperback)
Loot Price: R1,227
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Deep Learning for Beginners - A beginner's guide to getting up and running with deep learning from scratch using Python (Paperback)
Expected to ship within 10 - 15 working days
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Implement supervised, unsupervised, and generative deep learning
(DL) models using Keras and Dopamine with TensorFlow Key Features
Understand the fundamental machine learning concepts useful in deep
learning Learn the underlying mathematical concepts as you
implement deep learning models from scratch Explore
easy-to-understand examples and use cases that will help you build
a solid foundation in DL Book DescriptionWith information on the
web exponentially increasing, it has become more difficult than
ever to navigate through everything to find reliable content that
will help you get started with deep learning. This book is designed
to help you if you're a beginner looking to work on deep learning
and build deep learning models from scratch, and you already have
the basic mathematical and programming knowledge required to get
started. The book begins with a basic overview of machine learning,
guiding you through setting up popular Python frameworks. You will
also understand how to prepare data by cleaning and preprocessing
it for deep learning, and gradually go on to explore neural
networks. A dedicated section will give you insights into the
working of neural networks by helping you get hands-on with
training single and multiple layers of neurons. Later, you will
cover popular neural network architectures such as CNNs, RNNs, AEs,
VAEs, and GANs with the help of simple examples, and learn how to
build models from scratch. At the end of each chapter, you will
find a question and answer section to help you test what you've
learned through the course of the book. By the end of this book,
you'll be well-versed with deep learning concepts and have the
knowledge you need to use specific algorithms with various tools
for different tasks. What you will learn Implement recurrent neural
networks (RNNs) and long short-term memory (LSTM) for image
classification and natural language processing tasks Explore the
role of convolutional neural networks (CNNs) in computer vision and
signal processing Discover the ethical implications of deep
learning modeling Understand the mathematical terminology
associated with deep learning Code a generative adversarial network
(GAN) and a variational autoencoder (VAE) to generate images from a
learned latent space Implement visualization techniques to compare
AEs and VAEs Who this book is forThis book is for aspiring data
scientists and deep learning engineers who want to get started with
the fundamentals of deep learning and neural networks. Although no
prior knowledge of deep learning or machine learning is required,
familiarity with linear algebra and Python programming is necessary
to get started.
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