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Key benefits

Understand how to use PyTorch to build advanced neural network models

Get the best from PyTorch by working with Hugging Face, fastai, PyTorch Lightning, PyTorch Geometric, Flask, and Docker

Unlock faster training with multiple GPUs and optimize model deployment using efficient inference frameworks

Description

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.

Who is this book for?

This deep learning with PyTorch book is for data scientists, machine learning engineers, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning models using PyTorch. This book is ideal for those looking to switch from TensorFlow to PyTorch. Working knowledge of deep learning with Python is required.

What you will learn

Implement text, vision, and music generation models using PyTorch

Build a deep Q-network (DQN) model in PyTorch

Deploy PyTorch models on mobile devices (Android and iOS)

Become well versed in rapid prototyping using PyTorch with fastai

Perform neural architecture search effectively using AutoML

Easily interpret machine learning models using Captum

Design ResNets, LSTMs, and graph neural networks (GNNs)

Create language and vision transformer models using Hugging Face

What do you get with Print?

Instant access to your digital copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

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Frequently bought together

€38.99

Machine Learning with PyTorch and Scikit-Learn

€41.99

€45.99

Total € 126.97

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Franziska Kirschner Sep 02, 2024

Absolutely love this book both as a reference and to learn new techniques.I'm a ML researcher converting from Tensorflow to Pytorch and wanted a reference guide as I made the transition. The hands-on code examples were super useful to get up and running, and much more clearly explained than just trying to Google what to do.The pieces on engineering included a bunch of optimisations I hadn't considered in in the past, so I ended up learning a lot more than I anticipated. This book is very well-rounded and considers both the practical application and the theory behind it.I would highly recommend to any ML researcher or engineer!

Amazon Verified review

Fabio Milano Sep 07, 2024

If you're looking for a hands-on, comprehensive guide to modern neural network architectures and the PyTorch ecosystem, this book is a gem! The author's decade of deep learning experience shines through with practical, step-by-step instructions that truly guide you in building state-of-the-art neural networks. The balance between depth and breadth is perfect.Whether you're a data scientist or machine learning engineer wanting to upskill in the latest deep learning tools and frameworks, or a software engineer curious about modern machine learning, this book has got you covered. The clear explanations and practical examples make complex concepts easy to grasp and apply. Highly recommended!

Arun May 31, 2024

The book is an easy-read. It is quite resourceful for anyone looking to deepen their understanding of neural network models and its practical implementation using pytorch.The concepts are well-explained with examples. I like the way the book covered different type of data: images, text, sounds, etc. There are things for everyone.Other attraction is that it adapts to emerging concepts like diffusion models and integration with huggingface leveraging off-the-shelf pretrained models. Further, it provides practical guidance on deploying models to production, including on mobile devices.Overall, Mastering PyTorch is a must-have for a deep learning enthusiast who looks into recent learning techniques and its applications. Highly recommended!

Kelsey S. Jul 27, 2024

I've been working with PyTorch for a while now, but I really feel like this book took my skills to the next level. The explanations are clear and concise, and the code examples really helped me to understand the concepts. I especially appreciated the coverage of advanced techniques like generative models and graph neural networks, but most of the content is also completely suitable for beginners. I appreciate that the author also focuses on the modern landscape, including how to transition from TensorFlow to PyTorch, coverage of LLMs, neural networks in mobile settings (eg quantized models), autoML, integration with Hugging Face and beyond. I've already used some of the techniques from the book in my own work, and I'm really excited to see what else I can accomplish with PyTorch. Overall, I highly recommend this book to anyone who wants to learn more about PyTorch and deep learning.

Saloni Shukla Jun 09, 2024

I enjoyed reading "Mastering PyTorch" because it offered a hands-on, practical approach to deep learning with PyTorch. The clear explanations, real-world examples, and up-to-date content kept me engaged and informed. The expert insights and comprehensive coverage made complex concepts accessible and relevant, significantly enhancing my learning experience.

Mastering PyTorch: Create and deploy deep learning models from CNNs to multimodal models, LLMs, and beyond , Second Edition

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