توضیحات
This book examines the geometry behind deep learning models, providing insights into how neural networks process data, learn representations, and optimize performance. It covers topics such as high-dimensional spaces, manifold learning, gradient dynamics, and loss landscapes. Through theoretical explanations and practical examples, readers learn how geometric concepts influence model generalization, convergence, and robustness. The text also discusses advanced applications in computer vision, natural language processing, and reinforcement learning. By the end, readers gain a deeper understanding of the mathematical and geometric foundations of deep learning, enabling more informed model design and analysis.
نقد و بررسیها
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