Ebook: Deep learning with PyTorch: a practical approach to building neural network models using PyTorch

Build neural network models in text, vision and advanced analytics using PyTorchKey Features Explore PyTorch—the latest, cutting-edge library for all your deep learning needs; Train your neural networks for higher speed and flexibility and learn how to implement them in various scenarios; Cover various advanced neural network architecture such as ResNet, Inception, DenseNet and more with practical examples; Book DescriptionDeep Learning is powering the most intelligent systems in the world such as Google Voice, Siri, and Alexa. Advancements in powerful hardware such as GPU, software frameworks like PyTorch, Keras, Tensorflow, CNTK, etc and availability of big data have made it easier to implement solutions for various problems in the areas of Text, Vision, and advanced analytics.This book will get you up and running with one of the most cutting-edge deep learning library—PyToch. Written in Python, PyTorch is grabbing the attention of all the data science professionals due to its accessibility and efficiency. You will start off with installing PyTorch, then quickly move on to the various statistical operations with it. Next you'll learn about Neural networks with PyTorch and we'll explore CNN, RNN, LSTM, and autoencoders.This book provides the intuition behind the various state of the art Deep Learning architectures such as ResNet, DenseNet, Inception, and Seq2Seq without diving deep into the math of it. Then you will also learn about GPU computing during the course of the book. You will learn how you can train a model with PyTorch and also dive into complex neural networks such as generative networks to produce text and images.By the end of the book, you'll be able to implement PyTorch in deep learning applications with ease. You’ll also know everything it takes to get up and running with PyTorch.What you will learn Use PyTorch for tensor computations accelerated by GPU’s Build an image classifier by implementing CNN architecture using PyTorch Build systems that do text classification and language modeling using RNN, LSTM, and GRU Generate new faces using GAN’s Who This Book Is ForThis book is for data analysts, data scientists, and anyone who is familiar with machine learning and is looking for the best options to perform Deep Learning in their systems. Those who are looking for high speed and flexibility in their deep learning models will find this book very useful. Knowledge of Python programming is expectedAbout the AuthorVishnu Subramanianhas been responsible for architecting and implementing various Big data analytical projects. His interests include distributed computing, Machine Learning, Deep Learning.