A practical guide to embeddings and sequence models, covering representation learning, recurrent networks, gated units, and engineering checks.

Practical notes on embeddings, learned representations, and sequence models.

Practical notes on training deep neural networks with reliable optimisation, regularisation, and evaluation habits.

A practical guide to training deep networks by managing optimisation, regularisation, validation, and model evaluation.

A practical guide to core neural network architecture families and when to use them.

A practical overview of core neural network architecture families and when engineers should use them.

Keep preprocessing and regression together in a scikit-learn Pipeline, evaluate it, and save the fitted model.

A practical explanation of the core foundations behind deep learning systems.

A practical foundation article explaining representation learning, optimisation, capacity, overfitting, and generalisation in deep learning.

Add pytest and Ruff to a small Python project inside WSL with a reusable pyproject.toml configuration.