| Part | Chapters | Core Themes | |------|----------|-------------| | | 1‑4 | Mathematical preliminaries, perceptron learning rule, gradient descent, loss functions | | Part II – Core Architectures | 5‑11 | MLPs, back‑propagation, regularization, CNNs, RNNs/LSTMs, attention | | Part III – Advanced Topics & Applications | 12‑15 | Transfer learning, GANs, reinforcement learning, model interpretability, AI ethics | | Appendices | A‑F | Python basics, linear‑algebra cheat‑sheet, data‑preprocessing pipelines, bibliography, solutions |
The book covers the basic concepts of neural networks, including: Neural Networks A Classroom Approach By Satish Kumar.pdf
The book has received high praise from many readers, who highlight its strengths as a learning tool: | Part | Chapters | Core Themes |
The book's philosophy is to create a "balanced blend" of neuroscience, mathematics, and computer programming, and its structure reflects this commitment. The second edition is a comprehensive volume, spanning approximately 735 to 736 pages across 15 chapters, which are logically grouped into four major parts. This organization allows for a systematic study of the field. perceptron learning rule