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

Understand the key concepts of deep learning with PyTorch, a platform developed by Facebook’s Artificial Intelligence Research Group with a hands-on approach to understanding both theory and practice. This book will prepare you for applying deep learning to real world problems with a sound theoretical foundation and practical know-how with respect to PyTorch.
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, Practical Deep Learning with PyTorch explains the best practices in taking these models to production. 
What You'll Learn

• Build deep learning models with PyTorch (Feed-Forward Neural Networks, Convolution Neural Networks, Recurrent Neural Networks, Long Short Term Memory Networks, Auto encoders etc.)  
  
• Implement custom layers and loss functions with PyTorch.
  
• Develop an understanding of first order optimization approaches (stochastic gradient with modern variations like momentum, ADAM, Adagrad, Adadelta).  
• Create an appropriate experimental infrastructure around PyTorch for a research/industry project (data preprocessing, error analysis). 
  

Who This Book Is For
Practicing data scientists who want to use PyTorch to build and deploy deep learning models.