Description:

This book is an insightful and comprehensive guide on Deep Learning that delves into the evolving world of artificial intelligence. With the exponential growth of data and increasing computational power, deep learning has emerged as a transformative force across industries such as healthcare, finance, transportation, media, and education. This book serves as a bridge between theory and real-world applications in diverse domains like medical image analysis, autonomous navigation, smart cities, chatbots, translation systems, and fraud detection.

Starting with a historical overview of artificial intelligence and the evolution of neural networks, the book guides readers through the core principles of deep learning. It explains key concepts such as artificial neural networks, convolutional and recurrent architectures, and optimization algorithms with clarity and depth. In addition to the technical content, the book emphasizes important themes such as explainability, fairness, ethics, and responsible AI practices, addressing challenges that modern AI developers face today. It also explores emerging areas like federated learning, neuromorphic computing, and quantum AI, offering readers a glimpse into the future of deep learning research.

Undergraduate students of Computer Science and Engineering will find the book handy for its balanced coverage of theoretical concepts, practical tools, cutting-edge innovations, and syllabus aligned topics.

KEY FEATURES

• Comprehensive Coverage: Begins with foundational concepts of AI and neural networks, advancing to complex deep learning models and architectures.

• In-Depth Explanations: Detailed discussions on Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Autoencoders, Transformers, and more.

• Practical Focus: Includes real-world case studies from domains like healthcare, autonomous systems, NLP, finance, and smart cities.

• Ethical AI: Emphasizes fairness, accountability, transparency, and explainability in deep learning systems.

• Cutting-Edge Topics: Covers federated learning, neuromorphic computing, deep reinforcement learning, and quantum deep learning.

• Visual Aids: Includes diagrams, flowcharts, and illustrations to simplify complex concepts.

TARGET AUDIENCE

• B.Tech (Computer Science and Engineering)

• B.Tech (Information Technology)

• B.Tech (Data Science)

• BCA & MCA