AI-ML-Explainers：交互式可视化AI/ML学习资源库

AI-ML-Explainers is an open-source educational project by ancilcleetus hosted on GitHub. Its core idea is to teach AI/ML concepts through interactive visualizations instead of static content, enabling 'learn by doing' for abstract topics like neural networks. It covers multiple domains from basics to advanced, including deep learning, computer vision, and more.

• Original author/maintainer: ancilcleetus
• Source platform: GitHub
• Project link: https://github.com/ancilcleetus/AI-ML-Explainers
• Update time: 2026-06-08 This is an open-source project aimed at becoming a visual-first learning center for AI/ML.

The project plans 6 core domains:

Domain	Status	Progress
Deep Learning	Released	7/49 (14%)
Machine Learning	Upcoming	—
Computer Vision	Released	4/90 (4%)
NLP	Upcoming	—
Generative AI	Upcoming	—
Deployment	Upcoming	—
It's a long-term project with high-quality content despite partial completion.

Deep Learning (7 released):

1. Neuron & Forward Propagation
2. Tensors: Universal Language of DL
3. Activation Functions
4. Embeddings
5. Loss Functions Overview
6. Softmax & Probability Output
7. Cross-Entropy Loss

Computer Vision (4 released):

1. Image Basics (pixels, resolution)
2. Color Spaces (RGB, BGR, HSV)
3. Image as NumPy Array
4. Loading/Displaying/Saving Images Each explainer uses interactive tools to help understand concepts.

Unique Features:

• Visual-first learning (interactive instead of static)
• Step-by-step structure following cognitive rules
• Practice-oriented (code examples for PyTorch/TensorFlow)
• Open-source community contributions

How to Use:

1. Click the explainer link
2. Open in browser (no installation needed)
3. Interact to learn concepts in real time.

Target Users:

• AI beginners
• Developers with programming skills but fuzzy AI concepts
• AI educators
• Job seekers preparing for interviews

Learning Suggestions:

1. Follow the roadmap order (don't skip basics)
2. Interact hands-on with each explainer
3. Combine with code practice
4. Track project updates for new content.

AI-ML-Explainers represents a new direction in AI education—shifting from passive reading to active exploration. It's valuable for both new learners and practitioners to solidify basics. As more content is added, it's expected to become an important resource in AI education.