Syllabus · Deep Learning Foundations

Starting point

This course follows Aabha Python for AI and Data, Statistics with Data Storytelling and Applied Machine Learning. You should be able to write/import functions, manipulate arrays, read tracebacks, inspect a CSV, calculate a mean, interpret residuals, explain train/validation/test and choose precision/recall versus accuracy. Applied ML supplies pipeline, group/time split and evaluation judgement. We revisit those boundaries while teaching neural computation; we do not replace them with a random split for every problem.

Readiness: explain why preprocessing is fitted on train only; trace a two-feature linear prediction; calculate mean squared error for two examples; describe what a held-out test can and cannot establish; write a function returning a numerical array. If two or more are unfamiliar, review the corresponding prerequisite lessons before proceeding. See ASSESSMENTS for a concrete diagnostic and explained answers.

What you will be able to do

• Annotate tensor axes and catch broadcasting/dtype mistakes
• Compute a forward pass, loss and small backpropagation calculation by hand
• Implement minibatch training and distinguish module mode from gradient recording
• Fit and compare a linear neural baseline, small MLP regressor/classifier and tiny CNN
• Diagnose overfit, unstable steps and disconnected or accumulated gradients
• Select checkpoints/candidates using validation, then report a fixed final test honestly
• Explain a frozen feature extractor and one attention weighted average without overstating scope

Ordered learning path

1. Tensors and shape contracts: batched affine maps; practice shape tracing
2. Forward computation and losses: activations, raw logits, target contracts
3. Gradients and backpropagation: local derivatives, chain rule, finite differences
4. Data and batches: fixed independent-sample splits, train-only scaling, paired rows
5. Training loops: zero gradients, backward, update, train/eval/no_grad
6. Baselines and small networks: nonlinear sensors and concentric rings
7. Generalization: compare comparable curves, decay, dropout, checkpoint selection
8. Diagnostics: shape traps, mechanics checks, instability, confusion counts
9. Tiny CNN: local kernels, channels, pooling, exact shapes and 170 parameters
10. Reproducibility: seeds, state, selection records, test reuse boundaries
11. Scoped extensions: fixed extractor mechanics and two-key attention arithmetic
12. Capstone: declare, implement, freeze, evaluate and defend one experiment

Suggested pacing: 6 weeks at 4–6 hours per week, or 12 instructor-led sessions plus independent practice. These are planning estimates, not learner-pilot measurements. Slow down at Lessons 03 and 05 if the arithmetic or update sequence is still opaque.

Learning evidence

Each lesson has three exercises and an observable exit check. There are 18 checkpoint questions with separate explanations. Five staged coding functions have a public checker. The capstone combines a protocol, working code, comparison, diagnostics and a restrained conclusion. Suggested pass standard is 70/100 plus all evaluation-integrity gates; this is an instructional proposal, not an externally accredited credential.

Regression examples are IID synthetic samples. Rings and bars use independent class-conditional samples with fixed balanced labels, then random row splits. No claims of real-world operational performance, fairness, clinical utility or general LLM capability are supported. The optional extensions introduce concepts, not a complete specialization.