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A graph-based RAG framework pairing a knowledge graph with vector retrieval and a dual-level (low/high) query mode. New documents merge into the graph via set operations instead of triggering a rebuild, cutting the cost of keeping the index current.
A free, lesson-based curriculum for building AI agents in Python from first principles: agentic frameworks, design patterns, tool use, RAG, planning, multi-agent systems, memory, and protocols like MCP and A2A. Hands-on, with Azure AI and Semantic Kernel.
Official tutorial hub teaching how to code effectively with AI agents inside Cursor, from AI foundations to working with agents and reviewing their output. Lessons cover rules, tools, context as working memory, and which tasks agents handle well.
Runnable starter projects for the Claude API you fork and adapt: a knowledge-base customer support agent, a financial analyst that charts results in chat, plus computer-use, browser-use, and autonomous-coding-agent reference implementations.
Argues that "interesting" complexity is low in both ordered and fully random states but peaks in between, and proposes "complextropy" — a resource-bounded Kolmogorov-complexity measure — to capture the rise-then-fall pattern entropy can't explain.
Stanford's course teaches deep learning by making you build vision models from scratch — k-NN and linear classifiers up through CNNs, detection, segmentation, and Transformers — with three PyTorch assignments and a self-chosen final project.
Karpathy's 2015 walkthrough of character-level RNNs trained to predict the next character, showing how a tiny model learns to generate convincing Shakespeare, C code, and LaTeX — and what its neurons actually track.
Walks through the LSTM gating mechanism step by step, showing how the cell state and forget/input/output gates let the network carry information across long sequences where plain RNNs lose it to vanishing gradients.
Curates step-by-step, hands-on tutorials for reimplementing technologies from scratch—covering everything from OSs and compilers to neural networks, LLMs, and vision systems—so learners learn by rebuilding real systems across languages.
Condenses Stanford's CS 229 into one-page visual cheatsheets spanning supervised, unsupervised, and deep learning, plus probability and linear-algebra refreshers. Available in 10+ languages, with all topics merged into one Super VIP PDF.
Notebook-first deep learning textbook that teaches concepts through runnable multi-framework code, math, and exercises. Includes lecture-ready notebooks, community contributions, and broad university adoption—designed for hands-on learners and instructors.