SkalskiP/ILearnDeepLearning.py resource

Health v2 · maintenance only

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1410 stars · 461 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of Jupyter Notebook projects teaching neural networks and deep learning fundamentals, built with plain NumPy and Keras. Each project complements a Medium article, with visualizations of gradient descent, activation functions, and classification boundaries.

Use cases

• learn how neural networks work by implementing one in plain numpy
• visualize gradient descent and classification boundaries
• understand the math behind deep learning networks
• follow a deep learning tutorial alongside medium articles
• implement a convolutional neural network from scratch
• study optimizers and preventing overfitting in neural networks

When to choose

• you want to deeply understand neural network internals rather than just use a framework
• you prefer learning through hands-on code paired with written explanations
• you want visual, animated explanations of gradient descent and decision boundaries

When to avoid

• you need a production-ready deep learning framework
• you want actively maintained, up-to-date code with recent commits
• you need large-scale or GPU-accelerated training pipelines

Facets

learning-resource · maturity maintenance

machine-learning deep-learning data-visualization computer-vision deep-learning machine-learning computer-vision tutorials data-visualization python jupyter-notebooks numpy educational neural-networks medium-articles gradient-descent

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