Deep learning tutorials: learn one training loop all the way through
Tutorial hopping creates recognition without debugging skill. Pick one framework, rebuild a small training loop and change it until you can explain every tensor and measurement.
No ranking, interview or hiring outcome is guaranteed.
Tutorial order
Tensors and shapes
Create, index, reshape and broadcast arrays while predicting the shape of every result.
Autodiff
Trace a small computation and explain what each gradient measures.
A first network
Build forward pass, loss, backward pass and optimizer step without hiding the loop.
Generalization
Separate training and validation behavior; add regularization only in response to evidence.
Data pipelines
Batch, shuffle, transform and inspect inputs before blaming the model.
One specialization
Choose vision, language or tabular deep learning after the shared loop makes sense.
A tutorial is complete when
- You can reproduce itA fresh environment produces the recorded result.
- You can perturb itChanging batch size, learning rate or data produces an outcome you can investigate.
- You can explain failureThe write-up includes errors and limitations, not only the final metric.
SchoolWhool status
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.
Keep the claim in proportion
- A keyword match or AI fit assessment is guidance, not an employer ATS score or a prediction of being hired.
- Course and tool availability is described as it exists today; planned material is labelled rather than presented as published.
- Job applications happen on the employer's site, under your control.
Questions
Which deep learning framework should I learn first?
Choose one widely used framework and stay with it long enough to understand the training loop. The concepts transfer more easily than shallow familiarity with two APIs.
Are these SchoolWhool tutorials already published?
No. This is a study-order guide; the deep learning track is planned, not currently published.
Related reading
Deep learning course roadmapWhat is machine learning?Data Tools courseLearn it in order, with the practice attached
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