| 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 |
JavaScript seems to be disabled in your browser. Current country/territory: India (Switch country/territory) Computing. Computing. McGraw Hill Neural Networks- A Classroom Approach - McGraw Hill Neural Networks A Classroom Approach By Satish Kumar.pdf
: Visualizing gradient descent and local minima. | Week | Topics | Practical Activity (Code)
As Professor Kumar drew more diagrams and explained the concepts, the students began to grasp the basics. He introduced them to artificial neural networks (ANNs), which mimic the brain's structure and function. ANNs consist of layers of interconnected nodes or "neurons," which process and transmit information. McGraw Hill Neural Networks- A Classroom Approach -
The mathematical frameworks governing weight adjustments. 3. Multi-Layer Perceptrons (MLP) and Backpropagation