Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Mastering PyTorch
  • Table Of Contents Toc
Mastering PyTorch

Mastering PyTorch - Second Edition

By : Ashish Ranjan Jha
4.7 (21)
close
close
Mastering PyTorch

Mastering PyTorch

4.7 (21)
By: Ashish Ranjan Jha

Overview of this book

PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch deep learning book will help you uncover expert techniques to get the most out of your data and build complex neural network models. You’ll build convolutional neural networks for image classification and recurrent neural networks and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation, using generative models, including diffusion models. You'll not only build and train your own deep reinforcement learning models in PyTorch but also learn to optimize model training using multiple CPUs, GPUs, and mixed-precision training. You’ll deploy PyTorch models to production, including mobile devices. Finally, you’ll discover the PyTorch ecosystem and its rich set of libraries. These libraries will add another set of tools to your deep learning toolbelt, teaching you how to use fastai to prototype models and PyTorch Lightning to train models. You’ll discover libraries for AutoML and explainable AI (XAI), create recommendation systems, and build language and vision transformers with Hugging Face. By the end of this book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.
Table of Contents (22 chapters)
close
close
20
Other Books You May Enjoy
21
Index

A refresher on deep learning

Neural networks are a sub-type of machine learning methods that are inspired by the structure and function of the biological brain, such as the biological neuron shown in Figure 1.2. In neural networks, each computational unit, analogically called a neuron, is connected to other neurons in a layered fashion. When the number of such layers is more than two, the neural network thus formed is called a Deep Neural Network (DNN). Such models are generally called deep learning models.

Figure 1.2: Artificial neuron inspired by biological neuron. (Biological neuron image by: https://pixabay.com/users/clker-free-vector-images-3736)

Deep learning models have been proven superior to other classical machine learning models because of their ability to learn highly complex relationships between input data and the output (ground truth). In recent times, deep learning has gained a lot of attention, and rightly so, primarily because of the following two reasons...

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Mastering PyTorch
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist download Download options font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon