Deep learning course: foundations before architectures

A durable course sequence

1. Foundations

   Python, arrays, linear algebra, probability and the train-validation-test discipline.

2. Neural networks

   Layers, activations, losses, backpropagation, optimization and regularization.

3. Training practice

   Data pipelines, initialization, learning rates, debugging and experiment tracking.

4. Architectures

   Convolutional models, sequence models and Transformers in the problems that motivate them.

5. Evaluation and deployment

   Baselines, slices, robustness, latency, monitoring and responsible use.

Current SchoolWhool availability

SchoolWhool currently teaches Python, Data Tools, Maths, Statistics and classical Machine Learning. Deep learning and agentic AI are not published tracks yet; pages about them are study guides, not claims that those courses already exist.

Use the published Machine Learning, Maths, Statistics and Data Tools tracks as preparation. Do not read a deep-learning roadmap as an available SchoolWhool course page.

Make training behavior visible

A first deep-learning project should be small enough to run repeatedly. Plot training and validation loss, inspect batches and predictions, and save the configuration with each result. Change one factor at a time—learning rate, model size, regularization or data—and explain the pattern before adding another architecture.

This discipline separates implementation bugs from modelling problems. If loss never falls, inspect data, shapes, objective and gradients. If training improves while validation degrades, investigate generalization and data mismatch. If both look good but the product fails, revisit the metric and representative evaluation examples.

Questions before specializing

• Can you explain the training loop?You should know what the forward pass, loss, gradient, optimizer step and evaluation mode each contribute.
• Can you diagnose a baseline?Compare the network with a simpler method so added capacity has a measured reason to exist.
• Can you trace a failure?Inspect real examples and connect errors to data, objective, architecture or deployment conditions before changing models.

A tutorial study order

1. Tensors and shapes

   Create, index, reshape and broadcast arrays while predicting the shape of every result.

2. Autodiff

   Trace a small computation and explain what each gradient measures.

3. A first network

   Build forward pass, loss, backward pass and optimizer step without hiding the loop.

4. Generalization

   Separate training and validation behavior; add regularization only in response to evidence.

5. Data pipelines

   Batch, shuffle, transform and inspect inputs before blaming the model.

6. One specialization

   Choose vision, language or tabular deep learning after the shared loop makes sense.

Debug in a stable order

• Inspect the dataPrint shapes, ranges, labels and a few examples before changing the architecture or optimizer.
• Overfit a tiny batchCheck whether the model can fit a deliberately small sample; failure often exposes a pipeline or objective problem.
• Add complexity one change at a timeKeep a known baseline and record each configuration so an improvement or regression has an identifiable cause.

Deep learning and agentic AI are different layers

• Deep learningTensors, neural architectures, losses, optimization, datasets and model evaluation.
• Language-model foundationsTokens, embeddings, attention, Transformers, inference and limitations.
• Agent systemsTool schemas, state, planning loops, permissions, failure recovery and observability.
• EvaluationTask success, error taxonomy, cost, latency, safety and human review at consequential steps.

Boundaries for a first agent project

• One narrow jobChoose a task with a clear input, output and definition of completion rather than a general personal assistant.
• Small tool surfaceGive the system only the operations the task needs and validate every argument before execution.
• Human review at consequencesRequire explicit approval before sending, publishing, purchasing, deleting or making another material external change.