English | 13 Feb. 2025 | ASIN: B0DX8K5BNZ | 296 pages| PDF
Ready to dive into the revolutionary world of Deep Learning but feeling overwhelmed? This book is your friendly, hands-on guide to mastering PyTorch, the powerful and intuitive Python framework that’s transforming artificial intelligence.
“PyTorch for Deep Learning: A Practical Introduction for Beginners” provides a clear and accessible path for anyone with basic Python knowledge to build and train their own deep learning models. You’ll start with the fundamentals, learning about tensors, automatic differentiation, and neural network building blocks. The book then guides you through practical examples, including image and text classification, using convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Finally, you’ll discover advanced techniques like transfer learning to leverage pre-trained models for rapid development and cutting-edge results. Throughout the book, code examples are clear, well-documented, and designed to help you build real-world applications.
This book prioritizes hands-on learning and makes no assumptions about previous machine learning knowledge. Learn to build and train linear regression and logistic regression models, how data is handled by datasets and dataloaders, and how to monitor and evaluate model performance using metrics and visualizations. This book provides practical exercises that reinforce learning and show how to apply what you’ve learned to real-world problems. This book will help you to gain skills in one of the most indemand AI frameworks.
