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 andits 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 hands-on manner.
