This book is a guide to understanding and utilizing PyTorch 2.0 for deep learning applications. Matthew Rosch starts with an introduction to PyTorch, its various advantages over other deep learning frameworks, and its blend with CUDA for GPU acceleration. A substantial portion of the book is dedicated to illustrating how to build simple PyTorch models. This includes uploading and preparing datasets, defining the architecture, training, and predicting. It provides hands-on exercises with a real-world dataset. The book then dives into exploring PyTorch's nn module and gives a detailed comparison of different types of networks like Feedforward, RNN, GRU, CNN, and their combination.

Author: Matthew Rosch
Publisher: GitforGits
Date: July 2023
Pages: 146
ISBN: 978-8196288372
Print: 8196288379
Kindle: B0CB757XKX
Audience: Python developers interested in machine learning
Level: Intermediate/Advanced
Category: Artificial Intelligence

Topics include:

• A comprehensive introduction to PyTorch and CUDA for deep learning.
• Detailed understanding and operations on PyTorch tensors.
• Step-by-step guide to building simple PyTorch models.
• Insight into PyTorch's nn module and comparison of various network types.
• Overview of the training process and exploration of PyTorch's optim module.
• Understanding advanced concepts in PyTorch like model serialization and optimization.
• Knowledge of distributed training in PyTorch.
• Practical guide to using PyTorch's Quantization API.
• Differences between TensorFlow 2.0 and PyTorch 2.0.
• Guidance on migrating TensorFlow models to PyTorch using ONNX. 

For recommendations of books on Machine Learning see AI Books To Inspire You in our Programmer's Bookshelf section. 

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