Deep Learning with Python:Learn Best Practices of Deep Learning Models with PyTorch


Deep Learning with Python:Learn Best Practices of Deep Learning Models with PyTorch
by:Nikhil Ketkar and Jojo Moolayil
Publisher Finelybook 出版社:Apress; 2nd ed. edition (April 10, 2021)
Language 语言:English
pages 页数:324 pages
ISBN-10 书号:1484253639
ISBN-13 书号:9781484253632

Book Description
Master the practical aspects of implementing deep learning solutions with PyTorch, using a hands-on approach to understanding both theory and practice. This updated edition will prepare you for applying deep learning to real world problems with a sound theoretical foundation and practical know-how with PyTorch, a platform developed by:Facebook’s Artificial Intelligence Research Group.

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 and its building blocks
Work with tuning and optimizing models

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