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)

Fundamentals of Machine Learning

In the previous chapters, we saw practical examples of how to build deep learning models to solve classification and regression problems, such as image classification and average user view predictions. Similarly, we developed an intuition on how to frame a deep learning problem. In this chapter, we will take a look at how we can attack different kinds of problems and different tweaks that we will potentially end up using to improve our model's performance on our problems.

In this chapter, we will explore:

  • Other forms of problems beyond classification and regression
  • Problems with evaluation, understanding overfitting, underfitting, and techniques to solve them
  • Preparing data for deep learning

Remember, most of the topics that we discuss in this chapter are common to machine learning and deep learning, except for some of the techniques—...