Find the four Deep Learning guides in reading order and see what changed in Chilepluto's publishing schedule.

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

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

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 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.

A practical study path for engineers learning deep learning, based on the structure of Deep Learning by Goodfellow, Bengio, and Courville.

Choose a practical first local language model by checking memory, precision, context length, and runtime support.

A detailed operating model for cloud governance teams running secure, repeatable enterprise Azure platforms.