Neo4j graph construction from unstructured data using LLMs
- data-import
- genai
- graph
- graph-rag
67,262 visible repositories in the index
49–72 of 182 · Most stars
Neo4j graph construction from unstructured data using LLMs
Technical Analysis Library using Pandas and Numpy
Translatedpandas tutorial in Chinese
Translated"A Guide to Building the Large Model White Box": a Tiny-Universe made entirely by hand
A comprehensive machine learning repository containing 30+ notebooks on different concepts, algorithms and techniques.
TranslatedSome interesting Python crawlers and data analysis projects
Machine Learning Foundations: Linear Algebra, Calculus, Statistics & Computer Science
The 3rd edition of course.fast.ai
A probabilistic programming language in TensorFlow. Deep generative models, variational inference.
A Collection of Cheatsheets, Books, Questions, and Portfolio For DS/ML Interview Prep
TranslatedKnowledge, code, and ideas for data mining, computer vision, natural language processing, and recommender system competitions
Data augmentation for NLP
TranslatedChinese translation notes for Python for Data Analysis, 2nd Edition (2017)
A 15TB Collection of Physics Simulation Datasets
Technical resources for AI developers to build applications, agents, and systems using Oracle AI Database and OCI services
TranslatedWith only a Python foundation, build your own embodied intelligence robot from scratch; step by step from scratch, build VLA/OpenVLA/SmolVLA/Pi0, and gain a deep understanding of embodied intelligence
TranslatedNeeding only a Python foundation, build large language models from scratch; build GLM4\Llama3\RWKV6 step by step from scratch, to gain a deep understanding of how large models work
Probabilistic reasoning and statistical analysis in TensorFlow
Python notebooks with ML and deep learning examples with Azure Machine Learning Python SDK | Microsoft
A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
Learn Agentic AI using Dapr Agentic Cloud Ascent (DACA) Design Pattern and Agent-Native Cloud Technologies: OpenAI Agents SDK, Memory, MCP, A2A, Knowledge Graphs, Dapr, Rancher Desktop, and Kubernetes.
This project reproduces the book Dive Into Deep Learning (https://d2l.ai/), adapting the code from MXNet into PyTorch.
Text and supporting code for Think Stats, 2nd Edition
标注自己的数据集,训练、评估、测试、部署自己的人工智能算法