Deep Learning: From Biological Neurons to Deep Neural Networks

Why This Course Matters

Deep neural networks are often treated as opaque black boxes. True engineering intuition comes from understanding the progressive evolution of architectures: how we moved from simple rule-based expert systems to McCulloch-Pitts neurons, Rosenblatt’s Perceptrons, smooth Sigmoid units, continuous Gradient Descent, deep Feedforward Networks, Backpropagation, modern Optimizers (Momentum, RMSProp, Adam), CNNs, and recurrent LSTMs.

What You Will Build & Master

1. The 6 Jars Paradigm: Data, Tasks, Models, Loss Functions, Learning Algorithms, and Evaluation.
2. Biological & Binary Neurons: McCulloch-Pitts formulation, threshold search, boolean functions, and linear separability geometry.
3. Rosenblatt’s Perceptron: Real-valued hyperplanes, perceptron loss, convergence theorem proofs, and limitations.
4. Competitive ML: Kaggle workflows, data binarisation, train-test splits, and benchmark contest submissions.
5. The Calculus of Learning: First-order Taylor series approximation, analytical derivation of gradient descent, partial derivatives, and loss surface contours.
6. Deep Feedforward Networks & Backprop: Rigorous chain rule derivation, computation graphs, and pure NumPy implementations.
7. Advanced Optimizers: Overcoming ravines and saddle points with Momentum, NAG, AdaGrad, RMSProp, and Adam.
8. Computer Vision & CNNs: Spatial convolution arithmetic, kernels, max pooling, LeNet, and ResNet architectures in PyTorch.
9. Sequence Modeling & LSTMs: Temporal unrolling, BPTT, and gated memory architectures for sequence prediction.