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

Developers,data scientists,and machine learning engineers who want to use PyTorch for Deep Learning.
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.

Format: Paperback / softback
Length: 306 pages
Publication date: 10 April 2021
Publisher: APress

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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 a 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 PyT.

Apply in-depth vector calculus with PyT.

Apply in-depth probability and optimization with PyT.

Apply in-depth PyT.

Apply in-depth PyT.

Apply in-depth PyT.

Apply in-depth PyT.

Apply in-depth PyT.

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Weight: 502g
Dimension: 233 x 154 x 25 (mm)
ISBN-13: 9781484253632
Edition number: 2nd ed.