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Getting Started with PyTorch : A Hands-On Guide
Getting Started with A Hands-On GuideBy Medhat Ullah
Welcome to this comprehensive guide designed to help you kickstart your journey with PyTorch, a powerful open-source machine learning library. Whether you’re a beginner eager to explore deep learning or an experienced practitioner seeking to switch frameworks, this book provides the foundational knowledge and practical insights needed to get up and running quickly.
PyTorch is renowned for its flexibility, Pythonic design, and dynamic computation graph, making it a favorite among researchers, developers, and AI innovators worldwide. Through clear explanations and real-world code examples, you’ll learn how to work with tensors, perform automatic differentiation, and build your first neural network from scratch.
Each chapter takes you deeper — from understanding the basics of data loading and model architecture to training, optimization, and evaluation. Along the way, you’ll gain confidence in debugging, visualization, and fine-tuning your models for real applications in computer vision and natural language processing.
By the end of this book, you’ll not only understand how PyTorch works but also be equipped to create, train, and deploy deep learning models with ease.
What You’ll LearnPyTorch tensors, autograd, and GPU acceleration
Building, training, and evaluating neural networks step by step
Implementing CNNs and RNNs for real-world tasks
Handling datasets and data augmentation
Visualizing results and monitoring model performance
Deploying trained models for inference
Who This Book Is ForBeginners exploring AI and deep learning for the first time
Python developers curious about machine learning
Students and self-taught engineers seeking a practical introduction to PyTorch
Welcome to this comprehensive guide designed to help you kickstart your journey with PyTorch, a powerful open-source machine learning library. Whether you’re a beginner eager to explore deep learning or an experienced practitioner seeking to switch frameworks, this book provides the foundational knowledge and practical insights needed to get up and running quickly.
PyTorch is renowned for its flexibility, Pythonic design, and dynamic computation graph, making it a favorite among researchers, developers, and AI innovators worldwide. Through clear explanations and real-world code examples, you’ll learn how to work with tensors, perform automatic differentiation, and build your first neural network from scratch.
Each chapter takes you deeper — from understanding the basics of data loading and model architecture to training, optimization, and evaluation. Along the way, you’ll gain confidence in debugging, visualization, and fine-tuning your models for real applications in computer vision and natural language processing.
By the end of this book, you’ll not only understand how PyTorch works but also be equipped to create, train, and deploy deep learning models with ease.
What You’ll LearnPyTorch tensors, autograd, and GPU acceleration
Building, training, and evaluating neural networks step by step
Implementing CNNs and RNNs for real-world tasks
Handling datasets and data augmentation
Visualizing results and monitoring model performance
Deploying trained models for inference
Who This Book Is ForBeginners exploring AI and deep learning for the first time
Python developers curious about machine learning
Students and self-taught engineers seeking a practical introduction to PyTorch
53 pages, Kindle Edition
Published October 27, 2025
2 people want to read
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