Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized the way we approach complex problems in various fields, from computer vision and natural language processing to robotics and healthcare. At the heart of these technologies lies a fundamental concept: Neural Networks. In his book, "Neural Networks: A Classroom Approach", Satish Kumar provides an insightful and accessible introduction to this fascinating topic. In this blog post, we'll explore the key concepts and takeaways from Kumar's book, and discuss why a classroom approach to neural networks is essential for students, researchers, and professionals alike.
| Week | Topics | Practical Activity (Code) | |------|--------|----------------------------| | 1 | Neuron model, activation functions | Implement a single neuron in Python | | 2 | Perceptron learning | Code AND/OR gate training | | 3 | MLP architecture & backprop (derivation) | Hand-compute one epoch of XOR | | 4 | Backprop coding | Write a 2-layer net from scratch | | 5 | Momentum, learning rate tuning | Visualize error surfaces | | 6 | Hopfield networks | Store/recall patterns (digits) | | 7 | Self-organizing maps | Cluster colors in an image | | 8 | RBF networks | Function approximation | | 9 | Review & exam-style problems | Build a small classifier (e.g., iris) | | 10 | Final project from book’s appendix | Document and present results | Neural Networks A Classroom Approach By Satish Kumar.pdf