Book Image

Deep Learning with PyTorch

By : Vishnu Subramanian
Book Image

Deep Learning with PyTorch

By: Vishnu Subramanian

Overview of this book

Deep learning powers the most intelligent systems in the world, such as Google Voice, Siri, and Alexa. Advancements in powerful hardware, such as GPUs, software frameworks such as PyTorch, Keras, TensorFlow, and CNTK along with the availability of big data have made it easier to implement solutions to 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 libraries—PyTorch. PyTorch is grabbing the attention of deep learning researchers and data science professionals due to its accessibility, efficiency and being more native to Python way of development. You'll start off by installing PyTorch, then quickly move on to learn various fundamental blocks that power modern deep learning. You will also learn how to use CNN, RNN, LSTM and other networks to solve real-world problems. This book explains the concepts of various state-of-the-art deep learning architectures, such as ResNet, DenseNet, Inception, and Seq2Seq, without diving deep into the math behind them. You will also learn about GPU computing during the course of the book. You will see how to train a model with PyTorch and dive into complex neural networks such as generative networks for producing text and images. By the end of the book, you'll be able to implement deep learning applications in PyTorch with ease.
Table of Contents (11 chapters)

Introduction to neural networks

In the last few years, CNNs have become popular in the areas of image recognition, object detection, segmentation, and many other tasks in the field of computer vision. They are also becoming popular in the field of natural language processing (NLP), though they are not commonly used yet. The fundamental difference between fully connected layers and convolution layers is the way the weights are connected to each other in the intermediate layers. Let's take a look at an image where we depict how fully connected, or linear, layers work:

One of the biggest challenges of using a linear layer or fully connected layers for computer vision is that they lose all spatial information, and the complexity in terms of the number of weights used by fully connected layers is too big. For example, when we represent a 224 pixel image as a flat array, we would...