Neo4j graph construction from unstructured data using LLMs
- data-import
- genai
- graph
- graph-rag
Repository Index
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Code for the book Deep Learning with PyTorch by Eli Stevens, Luca Antiga, and Thomas Viehmann.
Bonus materials, exercises, and example projects for our Python tutorials
Repo for the Deep Reinforcement Learning Nanodegree program
- deep-reinforcement-learning
- reinforcement-learning
- reinforcement-learning-algorithms
- neural-networks
links to conference publications in graph-based deep learning
- graph-neural-networks
- graph-convolutional-networks
- graph
- deep-learning
Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS.
- dependency-graph
A comprehensive machine learning repository containing 30+ notebooks on different concepts, algorithms and techniques.
Learn python3 in one picture.
A Bulletproof Way to Generate Structured JSON from Language Models
Machine Learning Foundations: Linear Algebra, Calculus, Statistics & Computer Science
🚀 An open-source, hands-on curriculum bridging the gap from basic RL concepts to LLM alignment, RLVR, and advanced Agentic systems.
- agent
- agentic
- agentic-ai
- agentic-rl
The 3rd edition of course.fast.ai
- data-science
- fastai
- deep-learning
- pytorch
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
- machine-learning
- economics
- causal-inference
- causality
A probabilistic programming language in TensorFlow. Deep generative models, variational inference.
- bayesian-methods
- deep-learning
- machine-learning
- data-science
Jupyter notebooks for the Natural Language Processing with Transformers book
A Collection of Cheatsheets, Books, Questions, and Portfolio For DS/ML Interview Prep
- data-science
- machine-learning
- deep-learning
- data-portfolio
Learn Deep Reinforcement Learning in 60 days! Lectures & Code in Python. Reinforcement Learning + Deep Learning
- reinforcement-learning
- machine-learning
- artificial-intelligence
- deep-reinforcement-learning
35 production-grade agentic AI architectures (Reflexion, LATS, GraphRAG, MemGPT, Voyager, BrowserAgent, ...) — a Python library and runnable textbook with multi-provider LLM support and a 17-task benchmark leaderboard.
- agentic-ai
- ai-agents
- langchain
- langgraph
Data augmentation for NLP
- nlp
- augmentation
- machine-learning
- artificial-intelligence
An Open Source text-to-speech system built by inverting Whisper.
Tutorials on getting started with PyTorch and TorchText for sentiment analysis.
A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4 and more
- tensorflow
- pytorch
- keras
- gobang
[ICCV 2019] Monocular depth estimation from a single image
- depth-estimation
- neural-network
- deep-learning
- self-supervision
Technical resources for AI developers to build applications, agents, and systems using Oracle AI Database and OCI services
- artificial-intelligence
- generative-ai
- kubernetes
- kustomize