Lecture library
Slides are the complete handouts; Present opens the classroom build, and each Cheatsheet is a two-page review guide.
Resource key: ▤ handout · ▶ classroom presentation · ★ cheatsheet · ⌨ notebook · ◉ recording
Foundations
| No. | Lecture | Resources |
|---|---|---|
| 1 | Where Deep Learning Losses Come From I — distributions, likelihood, and loss | ▤ Slides · ▶ Present · ★ Cheatsheet |
| 2 | Where Deep Learning Losses Come From II — MLE, regression, and classification | ▤ Slides · ▶ Present · ★ Cheatsheet |
| 3 | Likelihood to Loss in Practice — MAP, regularization, and robust observation models | ▤ Slides · ★ Cheatsheet · ⌨ Likelihood notebook · ⌨ Robust regression |
| 4 | From Linear Models to Neural Networks — neurons, activations, multilayer networks, XOR, and approximation | ▤ Slides · ▶ Present · ★ Cheatsheet · ⌨ Linear GD · ⌨ XOR |
Gradients & optimization
| No. | Lecture | Resources |
|---|---|---|
| 5 | Optimization I — Gradient Estimates & Step Sizes — batches, stochastic gradients, and optimizer geometry | ▤ Slides · ▶ Present · ★ Cheatsheet · ◉ Recording |
| 6 | Backpropagation & Autograd from Scratch — computation graphs, reverse mode, and a scalar engine | ▤ Slides · ▶ Present · ★ Cheatsheet · ⌨ Colab · ◉ Recording |
| 7 | Optimization II — Momentum, RMSProp & Adam — moving averages and adaptive coordinate scaling | ▤ Slides · ▶ Present · ★ Cheatsheet · ◉ Momentum · ◉ RMSProp & Adam |
| 8 | Vector, Matrix & Affine Autograd — gradients, Jacobians, dense layers, and batched VJPs | ▤ Calculus · ▤ Autograd · ★ Cheatsheet · ⌨ Colab |
| 9 | Valid Outputs by Design & Learning-Rate Schedules — constrained parameters, warmup, decay, and cosine | ▤ Valid outputs · ▤ LR schedules · ★ Cheatsheet |
Generalization
| No. | Lecture | Resources |
|---|---|---|
| 10 | Generalization & Regularization — learning curves, weight decay, early stopping, dropout, augmentation, Mixup, and label smoothing | ▤ Slides · ▶ Present · ★ Cheatsheet · ⌨ Learning curves · ⌨ Mechanisms |
Attention and language
Read the interactive lecture at your own pace, or choose Present for the classroom slides. Each cheat sheet fills two printable A4 pages.
| Part | Lecture | Resources |
|---|---|---|
| 1 | From characters to next-token prediction - tokenization, learned embeddings, a hidden-layer MLP, training, and generation | ▤ Interactive lecture · ▶ Present · ★ A4 cheat sheet |
| 2 | Self-attention, from first principles - queries, keys, values, scaling, causal masking, and residual updates | ▤ Interactive lecture · ▶ Present · ★ A4 cheat sheet |
| 3 | From Attention to Applications - encoder, decoder-only and encoder-decoder models; token labels, CLS and English-to-Hindi cross-attention | ▤ Interactive lecture · ▶ Present · ▤ PDF |
| CLIP | CLIP: From Fixed Labels to Language-Defined Vision — image–text applications, two encoders, contrastive loss and linear probing | ▤ HTML lecture · ▶ Present · ▤ PDF · 164 pages |
| VLMs | From CLIP to Vision-Language Models — visual prefixes, cross-attention, compression, training and image-conditioned generation | ▤ HTML lecture · ▶ Present · ▤ PDF · 155 pages |
Object detection and beyond boxes
Two connected lectures: first predict a set of objects, then choose richer outputs when boxes are not enough.
| Part | Lecture | Resources |
|---|---|---|
| I | Object Detection: From One Label to a Set of Objects — candidates, assignment, worked losses, NMS, evaluation and learned queries | HTML lecture · Present · PDF · 133 pages · Detector lab |
| II | Beyond Boxes: Dense, Structured, and Promptable Vision — semantic and instance segmentation, pose, depth, promptable masks and grounding | HTML lecture · Present · PDF · 60 pages · Beyond Boxes lab |
Convolutional neural networks
One complete interactive deck built on the ML course CNN lecture and its tutorials: original filtering examples, a step-by-step LeNet exercise, real MNIST training and layer inspection, plus the DL course’s gradients, receptive fields, computational accounting, modern blocks, and transfer.
Read the lecture · Present · Sources and teaching choices · PyTorch companion