AI Resources (Free + Open Source + Paid)

Curated resources for learning and using AI.
Links are grouped by type, and entries marked as [PAID] are subscription/paid services.

Learning (Start Here)

• [FREE] DeepLearning.AI (learning hub) — https://www.deeplearning.ai/
• [FREE] CS50 AI — https://cs50.harvard.edu/ai/
• [FREE] fast.ai (courses/resources) — https://www.fast.ai/
• [FREE] Hugging Face Learn — https://huggingface.co/learn

Courses & Guides

• [FREE] Kaggle Learn — https://www.kaggle.com/learn
• [FREE] YouTube: 3Blue1Brown — https://www.youtube.com/@3blue1brown
• [FREE] YouTube: sentdex (ML basics) — https://www.youtube.com/@sentdex
• [FREE] Stanford CS231n (materials—varies by access) — https://cs231n.stanford.edu/
• [PAID] Coursera (many courses require subscription after trial) — https://www.coursera.org/
• [PAID] edX (many programs require paid upgrade) — https://www.edx.org/
• [PAID] Udacity (nanodegrees typically paid) — https://www.udacity.com/

Model Hubs & Tools

• [FREE] Hugging Face Models — https://huggingface.co/models
• [FREE] Hugging Face Datasets — https://huggingface.co/datasets
• [FREE] Papers with Code — https://paperswithcode.com/
• [FREE] Replicate (some free tiers exist; pricing often applies) — https://replicate.com
• [FREE] Ollama Library — https://ollama.com/library

Running Models Locally (Open Source)

• [OPEN SOURCE] Ollama — https://github.com/ollama/ollama
• [OPEN SOURCE] llama.cpp — https://github.com/ggerganov/llama.cpp
• [OPEN SOURCE] vLLM — https://github.com/vllm-project/vllm
• [OPEN SOURCE] Transformers — https://github.com/huggingface/transformers
• [OPEN SOURCE] diffusers — https://github.com/huggingface/diffusers

Frameworks (Open Source)

• [OPEN SOURCE] PyTorch — https://pytorch.org/
• [OPEN SOURCE] TensorFlow — https://www.tensorflow.org/
• [OPEN SOURCE] LangChain — https://www.langchain.com/
• [OPEN SOURCE] LlamaIndex — https://www.llamaindex.ai/

Papers & Reading

• [FREE] arXiv — https://arxiv.org/
• [FREE] Semantic Scholar — https://www.semanticscholar.org/
• [FREE] Google Scholar (search) — https://scholar.google.com/
• [FREE] The Batch (reading list/newsletter) — https://www.multithreaded.stitchfix.com/blog/thebatch/

Datasets & Benchmarks

• [FREE] Hugging Face Datasets — https://huggingface.co/datasets
• [FREE] Kaggle Datasets — https://www.kaggle.com/datasets
• [FREE] OpenML — https://www.openml.org/

Prompting / Best Practices (Free)

• [FREE] Prompting Guide — https://www.promptingguide.ai/
• [FREE] Learn Prompting — https://learnprompting.org/
• [FREE] Stanford / general prompt engineering resources (various) — https://www.promptingguide.ai/ (covers broadly)

Communities

• [FREE] Hugging Face Forums — https://discuss.huggingface.co/
• [FREE] Reddit r/MachineLearning — https://www.reddit.com/r/MachineLearning/
• [FREE] Reddit r/LocalLLaMA — https://www.reddit.com/r/LocalLLaMA/
• [FREE] Hacker News — https://news.ycombinator.com/
• [FREE] Discord (community hub) — https://discord.com/invite/huggingface (check invite validity)

Paid Subscription Services (Mark as [PAID])

Use these when you want convenience/API power. Many are optional or have free tiers, but the core product is commonly subscription-based.

• [PAID] OpenAI (ChatGPT / API ecosystem) — https://openai.com/
• [PAID] Anthropic (Claude) — https://www.anthropic.com/
• [PAID] Google AI (Gemini platform) — https://ai.google/
• [PAID] Microsoft Copilot — https://copilot.microsoft.com/
• [PAID] AWS Bedrock — https://aws.amazon.com/bedrock/
• [PAID] Google Vertex AI — https://cloud.google.com/vertex-ai
• [PAID] Azure AI (cognitive services) — https://azure.microsoft.com/en-us/products/ai-services

Contributing

If you add resources, include:

• Name
• Link
• Tag: [FREE], [OPEN SOURCE], or [PAID] / required subscription

License

MIT