Deep learning with Python : learn best practices of deep learning models with PyTorch / Nikhil Ketkar, Jojo Moolayil.

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Deep learning with Python : learn best practices of deep learning models with PyTorch / Nikhil Ketkar, Jojo Moolayil.

Second edition.

9781484253649 (electronic bk.)
1484253647 (electronic bk.)
9781484253632

[Berkeley, CA] : Apress, [2021]

English

1 online resource (xvii, 306 pages) : illustrations

10.1007/978-1-4842-5364-9 doi

QA76.73.P98

005.13/3

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. You will: 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.

Includes bibliographical references and index.

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Online resource; title from PDF title page (SpringerLink, viewed April 15, 2021).

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