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Full Description
In the rapidly evolving field of artificial intelligence, this book serves as a crucial resource for understanding the mathematical foundations of AI. It explores the intricate world of tensors, the fundamental elements powering today's advanced deep learning models. Combining theoretical depth with practical insights, the text navigates the complex landscape of tensor calculus, guiding readers to master the principles and applications of tensors in AI. From the basics of tensor algebra and geometry to the sophisticated architectures of neural networks, including multi-layer perceptrons, convolutional, recurrent, and transformer models, this book provides a comprehensive examination of the mechanisms driving modern AI innovations. It delves into the specifics of autoencoders, generative models, and geometric interpretations, offering a fresh perspective on the complex, high-dimensional spaces traversed by deep learning technologies. Concluding with a forward-looking view, the book addresses the latest advancements and speculates on the future directions of AI research, preparing readers to contribute to or navigate the next wave of innovations in the field. Designed for academics, researchers, and industry professionals, it serves as both an essential textbook for graduate and postgraduate students and a valuable reference for experts in the field. With its rigorous approach to the mathematical frameworks of AI and a strong focus on practical applications, this book bridges the gap between theoretical research and real-world implementation, making it an indispensable guide in the realm of artificial intelligence.
Contents
Chapter 1: A Tensorial Perspective to Deep Learning.- Chapter 2: The Algebra and Geometry of Deep Learning.- Chapter 3: Building Blocks.- Chapter 4: Journey into Convolutions.- Chapter 5: Modeling Temporal Data.- Chapter 6: Transformer Architectures.- Chapter 7: Attention Mechanisms Beyond Transformers.- Chapter 8: Graph Neural Networks: Extending Deep Learning to Graphs.- Chapter 9: Self-Supervised and Unsupervised Learning in Deep Learning.- Chapter 10: Learning Representations via Autoencoders and Generative Models.- Chapter 11: Recent Advances and Future Perspectives.
