# 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.