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)


In this chapter, we explored the complete life cycle of a neural network in Pytorch, starting from constituting different types of layers, adding activations, calculating cross-entropy loss, and finally optimizing network performance (that is, minimizing loss), by adjusting the weights of layers using the SGD optimizer.

We have studied how to apply the popular ResNET architecture to binary or multi-class classification problems.

While doing this, we have tried to solve the real-world image classification problem of classifying a cat image as a cat and a dog image as a dog. This knowledge can be applied to classify different categories/classes of entities, such as classifying species of fish, identifying different kinds of dogs, categorizing plant seedlings, grouping together cervical cancer into Type 1, Type 2, and Type 3, and much more.

In the next chapter, we will go...