Master the practical aspects of implementing deep learning solutions with PyTorch, using a handson 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 knowhow 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 pathbreaking framework with a set of tools and techniques to solve realworld 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 shortterm 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 feedforward networks, convolution neural networks, recurrent neural networks, automatic differentiation, and stochastic gradient descent.

Apply indepth linear algebra with PyTorch

Explore PyTorch fundamentals and its building blocks

Work with tuning and optimizing models
Who This Book Is For
Beginners with a working knowledge of Python who want to understand Deep Learning in a practical, handson manner.