Overview
- Offers a sound theoretical/mathematical foundation and practical programming techniques using PyTorch
- Covers deep learning with multiple GPUs and optimizing deep learning models
- Reviews best practices of taking deep learning models to production with PyTorch
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Table of contents (8 chapters)
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Front Matter
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Back Matter
Keywords
About this book
You'll start with a perspective on how and why deep learning with PyTorch has emerged as an path-breaking framework with a set of tools and techniques to solve real-world problems. Next, the book will ground you with the mathematical fundamentals of linear algebra, vector calculus, probability and optimization. Having established this foundation, you'll move on to key components and functionality of PyTorch including layers, loss functions and optimization algorithms.
You'll also gain an understanding of Graphical Processing Unit (GPU) based computation, which is essential for training deep learning models. All the key architectures in deep learning are covered, including feedforward networks, convolution neural networks, recurrent neural networks, long short-term memory networks, autoencoders and generative adversarial networks. Backed by a number of tricks of the trade for training and optimizing deep learning models, this edition of Deep Learning with Python explains the best practices in taking these models to production with PyTorch.
What You'll Learn
- Review machine learning fundamentals such as overfitting, underfitting, and regularization.
- Understand deep learning fundamentals such as feed-forward networks, convolution neural networks, recurrent neural networks, automatic differentiation, and stochastic gradient descent.
- Apply in-depth linear algebra with PyTorch
- Explore PyTorch fundamentals andits building blocks
- Work with tuning and optimizing models
Beginners with a working knowledge of Python who want to understand Deep Learning in a practical, hands-on manner.
Authors and Affiliations
About the authors
Jojo Moolayil is an artificial intelligence, deep learning, machine learning, and decision science professional with over five years of industrial experience and is a published author of the book Smarter Decisions – The Intersection of IoT and Decision Science. He has worked with several industry leaders on high-impact and critical data science and machine learning projects across multiple verticals. He is currently associated with Amazon Web Services as a research scientist. He was born and raised in Pune, India and graduated from the University of Pune with a major in Information Technology Engineering. He started his career with Mu Sigma Inc., the world’s largest pure-play analytics provider and worked with the leaders of many Fortune 50 clients. He later worked with Flutura – an IoT analytics startup and GE. He currently resides in Vancouver, BC. Apart from writing books on decision science and IoT, Jojo has also been a technical reviewer for various books on machine learning, deep learning and business analytics with Apress and Packt publications. He is an active data science tutor and maintains a blog at http://blog.jojomoolayil.com.
Bibliographic Information
Book Title: Deep Learning with Python
Book Subtitle: Learn Best Practices of Deep Learning Models with PyTorch
Authors: Nikhil Ketkar, Jojo Moolayil
DOI: https://doi.org/10.1007/978-1-4842-5364-9
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Nikhil Ketkar, Jojo Moolayil 2021
Softcover ISBN: 978-1-4842-5363-2Published: 10 April 2021
eBook ISBN: 978-1-4842-5364-9Published: 09 April 2021
Edition Number: 2
Number of Pages: XVII, 306
Number of Illustrations: 82 b/w illustrations
Topics: Python, Machine Learning, Open Source