Learning PyTorch 2.0: Experiment deep learning from basics to complex models using every potential capability of Pythonic PyTorch

This book is a comprehensive guide to understanding and utilizing PyTorch 2.0 for deep learning applications. It starts with an introduction to PyTorch, its various advantages over other deep learning frameworks, and its blend with CUDA for GPU acceleration. We delve into the heart of PyTorch - tensors, learning their different types, properties, and operations. Through step-by-step examples, the reader learns to perform basic arithmetic operations on tensors, manipulate them, and understand errors related to tensor shapes.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.Further, the book delves into understan