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)

Machine learning glossary

In the last few chapters, we have used lot of terminology that could be completely new to you if you are just entering the machine learning or deep learning space. We will list a lot of commonly-used terms in machine learning, which are also used in the deep learning literature:

  • Sample or input or data point: These mean particular instances of training a set. In our image classification problem seen in the last chapter, each image can be referred to as a sample, input, or data point.
  • Prediction or output: The value our algorithm generates as an output. For example, in our previous example our algorithm predicted a particular image as 0, which is the label given to cat, so the number 0 is our prediction or output.
  • Target or label: The actual tagged label for an image.
  • Loss value or prediction error: Some measure of distance between the predicted value...