Each chapter follows a :
Neural networks are computational models inspired by biological neurons that learn mappings from inputs to outputs by adjusting parameters (weights and biases). They form the core of modern machine learning for tasks like classification, regression, sequence modeling, and generative modeling. Neural Networks A Classroom Approach By Satish Kumar.pdf
" Neural Networks: A Classroom Approach " by Satish Kumar, published by McGraw Hill Education , provides a foundational, geometrically intuitive guide to artificial neural networks, bridging biological concepts with mathematical theory. The textbook covers essential topics including feedforward networks, supervised learning, SVMs, and recurrent neurodynamics, utilizing MATLAB examples for practical application. For more details, visit McGraw Hill Education. Neural Networks- A Classroom Approach - McGraw Hill Each chapter follows a : Neural networks are
Professor Kumar highlighted the three main components of a neural network: and recurrent neurodynamics
| 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 |