Mastering Deep Learning with PyTorch
From Fundamentals to Real-World Projects
This first edition delivers a complete end-to-end learning pathway for mastering modern deep learning using PyTorch.
Major Topics Covered• Deep Learning Fundamentals
• Artificial Neural Networks
• PyTorch Framework and Tensor Operations
• Automatic Differentiation (Autograd)
• Feedforward Neural Networks
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Long Short-Term Memory Networks (LSTMs)
• Attention Mechanisms
• Transformer Architectures
• Hugging Face Ecosystem
• Generative Adversarial Networks (GANs)
• Computer Vision Applications
• Natural Language Processing Applications
• Model Evaluation and Optimization
• Hyperparameter Tuning
• Explainable Artificial Intelligence (XAI)
• Ethical AI and Bias Mitigation
• Model Deployment and Production Pipelines
Practical Implementations Included• Image Classification Systems
• Object Detection Models
• Image Segmentation Applications
• Text Classification Systems
• Sentiment Analysis Models
• Language Translation Pipelines
• Transformer-Based NLP Applications
• GAN-Based Image Generation
Capstone ProjectsProject 1: Pneumonia Detection using CNN
Project 2: Sentiment Analysis using LSTM
Project 3: Image Colorization using GAN
Project 4: Real-Time Object Detection System
Project 5: Transformer-Based Intelligent Chatbot
Industry Tools and Technologies• PyTorch
• TorchVision
• Hugging Face Transformers
• TensorBoard
• Flask
• ONNX
• Docker Concepts
• AWS Deployment Basics
• Google Cloud Deployment Concepts
Intended Audience• Undergraduate Students
• Postgraduate Students
• Data Scientists
• Machine Learning Engineers
• AI Researchers
• Software Developers
• Academic Professionals
• Industry Practitioners
Learning OutcomesUpon completion of this book, readers will be able to:
• Design and train neural network architectures.
• Build computer vision applications using CNNs.
• Develop NLP solutions using RNNs, LSTMs, and Transformers.
• Implement generative AI systems using GANs.
• Evaluate and optimize deep learning models.
• Deploy PyTorch models into production environments.
• Understand ethical considerations in AI development.
• Create portfolio-ready deep learning projects.
This release establishes a strong foundation for academic learning, industrial applications, and advanced research in modern deep learning.
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About
About the Book
Mastering Deep Learning with PyTorch: From Fundamentals to Real-World Projects
Artificial Intelligence has transformed the way organizations solve problems, automate processes, and create intelligent systems. At the heart of this revolution lies Deep Learning—a powerful subset of machine learning that enables computers to learn complex patterns from massive amounts of data. From self-driving vehicles and medical diagnosis systems to chatbots, recommendation engines, and generative AI, deep learning is driving innovation across virtually every industry.
Mastering Deep Learning with PyTorch: From Fundamentals to Real-World Projects is a comprehensive and practical guide designed to help students, developers, researchers, and working professionals build a strong foundation in deep learning while mastering one of the world's most popular deep learning frameworks—PyTorch.
This book takes readers on a structured learning journey, beginning with the fundamental concepts of neural networks and progressing toward advanced architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), Transformers, and Generative Adversarial Networks (GANs).
Unlike purely theoretical books, this guide emphasizes practical implementation. Every major concept is accompanied by PyTorch-based examples, real-world applications, and project-driven learning experiences that help readers develop both conceptual understanding and technical confidence.
The book covers modern deep learning applications across computer vision, natural language processing, generative AI, time-series forecasting, model deployment, explainable AI, and ethical machine learning practices. Readers learn how to build, train, evaluate, optimize, and deploy production-ready deep learning systems.
A major highlight of the book is its collection of end-to-end capstone projects, including pneumonia detection systems, sentiment analysis engines, image colorization applications, object detection platforms, and transformer-based chatbots. These projects help bridge the gap between academic learning and industry requirements.
Whether you are a beginner entering the field of Artificial Intelligence, a university student pursuing machine learning studies, a software engineer transitioning into AI, or a researcher seeking practical deep learning expertise, this book provides a complete roadmap to mastering deep learning with PyTorch.
By combining theoretical foundations, mathematical intuition, practical coding experience, and deployment techniques, this book equips readers with the knowledge and skills required to build intelligent systems capable of solving real-world challenges.
Author
About the Author
Anshuman Kumar Mishra, M.Tech (Computer Science) Assistant Professor, Doranda College, Ranchi University
Prolific Author of 50+ Books on AI, Machine Learning & Computer Science | 20+ Years Experience
Anshuman Kumar Mishra is a dedicated educator, researcher, and highly prolific author with over 20 years of experience in Computer Science and Information Technology. Holding an M.Tech in Computer Science from BIT Mesra, he brings a rare combination of academic depth and practical teaching expertise.
Currently serving as Assistant Professor at Doranda College under Ranchi University, he has mentored thousands of students, helping them build strong foundations in programming, data science, and artificial intelligence. His student-centric teaching style emphasizes conceptual clarity, hands-on practice, and real-world application.
Anshuman is a prolific author with more than 50 books published across a wide spectrum of computer science and emerging technology domains. From foundational programming languages to advanced topics in Artificial Intelligence, Machine Learning, Reinforcement Learning, Decision Theory, and Computer Vision — his books are widely appreciated by students, educators, and professionals for their clear explanations, strong theoretical foundation, and practical approach.
His extensive body of work reflects his deep commitment to making complex subjects accessible and meaningful for learners at all levels. He is particularly recognized for creating well-structured learning paths that help readers progress from beginner to advanced levels with confidence.
Driven by the mission to democratize quality technical education, Anshuman continues to write and update books that bridge the gap between academic theory and industry practice.
When not teaching or writing, he actively follows and explores new developments in AI, Quantum Machine Learning, and Ethical Intelligence systems.
Contents
Table of Contents
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