Democratizing Artificial Intelligence Research, Education, and Technologies
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DAIR.AI has a significant public presence on GitHub, focusing on democratizing artificial intelligence research and education. The organization utilizes various programming languages, including Jupyter Notebook, Python, and TypeScript, and hosts several widely used repositories, such as the Prompt-Engineering-Guide and ml-visuals, which provide valuable resources and insights into machine learning.
Top languages
Public repositories
Prompt-Engineering-Guide
🐙 Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.
ml-visuals
🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.
ML-YouTube-Courses
📺 Discover the latest machine learning / AI courses on YouTube.
AI-Papers-of-the-Week
🔥Highlighting the top ML papers every week.
ML-Papers-Explained
Explanation to key concepts in ML
ML-Course-Notes
🎓 Sharing machine learning course / lecture notes.
Mathematics-for-ML
🧮 A collection of resources to learn mathematics for machine learning
ML-Notebooks
:fire: Machine Learning Notebooks
Transformers-Recipe
🧠 A study guide to learn about Transformers
nlp_paper_summaries
✍️ A carefully curated list of NLP paper summaries
GNNs-Recipe
🟠 A study guide to learn about Graph Neural Networks (GNNs)
MLOPs-Primer
A collection of resources to learn about MLOPs.
AI-Product-Index
A curated index to track AI-powered products.
pytorch_notebooks
🔥 A collection of PyTorch notebooks for learning and practicing deep learning
d2l-study-group
🧠 Material for the Deep Learning Study Group
nlp_fundamentals
📘 Contains a series of hands-on notebooks for learning the fundamentals of NLP
nlp_newsletter
📰Natural language processing (NLP) newsletter
dair-academy-plugins
DAIR.AI Academy Plugins
awesome-ML-projects-guide
A guide to building awesome machine learning projects.
dair-ai.github.io
Home of DAIR.AI
emotion_dataset
:smile: Dataset for Emotion Recognition Research
m2-deep-research
Deep research agents using MiniMax M2.1 interleaved thinking
awesome-research-proposals-guide
A guide to improve your research proposals.
ml-nlp-paper-discussions
📄 A repo containing notes and discussions for our weekly NLP/ML paper discussions.
keep-learning-ml
A club to keep learning about ML
dair-workshops
No description provided for this repository.
nlp_overview
📘Overview of Modern Deep Learning Techniques Applied to Natural Language Processing
notebooks
🔬 Sharing your data science notebooks with the community has never been this easy.
deep-ai-research
Claude agent for Deep AI Research
covid_19_search_application
Text Similarity Search Application using Modern NLP and Elasticsearch
maven-pe-for-llms-4
Prompt Engineering for Large Language Models - Notebooks, Demos, Exercises, and Projects
odsc_2020_nlp
Repository for ODSC talk related to Deep Learning NLP
deep-research-n8n
Deploy a mult-agent deep research agent with Replit and n8n
research_emotion_analysis
:smile: Multilingual emotion analysis research
llm-evaluator
Example for Logging LLM Evaluator Prompt Responses
data_science_writing_primer
Writing Primer for Data Scientists
nlp_highlights
✨ A report of the most important NLP highlights (A Yearly Report - 2018, 2019)
pe-for-llms
No description provided for this repository.
datasets
AI Datasets
maven-pe-for-llms
No description provided for this repository.
paper_implementations
A project for implementing ML and NLP papers
cursor-course
No description provided for this repository.
deep_learning_notations
➕Contains useful deep learning notations for writing blogs, presentations, and papers.
maven-pe-for-llms-6
Materials for the Prompt Engineering for LLMs (Cohort 6)
cursor-final-project
Final project for the Cursor course
maven-pe-for-llms-8
Materials for the Prompt Engineering for LLMs (Cohort 8)
nlp_with_tensorflow
NLP tutorials I have written using TensorFlow
maven-pe-for-llms-12
Course material for Prompt Engineering for LLMs
maven-pe-for-llms-11
Materials for the Prompt Engineering for LLMs Course (Cohort 11)
maven-pe-for-llms-13
Materials for the Prompt Engineering for LLMs (Cohort 13)
maven-pe-for-llms-9
Materials for Prompt Engineering for LLMs (Cohort 9)
maven-pe-for-llms-7
Code, Demos, and Exercises for Prompt Engineering for LLMs Course
deep-research-agent
No description provided for this repository.
cc-cohort-3
No description provided for this repository.
comet-llm
comet llm support
deep_affective_layer
:smile: Building a deep learning based affective computing platform
paper_presentations
All paper presentation material will be added here
mri-analysis-pytorch
🧠MRI analysis using PyTorch and MedicalTorch
deep-research-initial-project
No description provided for this repository.
.github
No description provided for this repository.
maven-pe-for-llms-10
Materials for Cohort 10
maven-pe-for-llms-2
No description provided for this repository.
dair-cc-plugins
DAIR.AI Claude Code Plugins
text-embeddings
No description provided for this repository.
appworks_meetup_2018
Contains all the material used for the "Applied Deep Learning for NLP Using PyTorch" meetup at AppWorks
wiki-sft
No description provided for this repository.
dr-agent
No description provided for this repository.
claude-code-course-c1
No description provided for this repository.
helsinki_ml
Machine Learning in the Elastic Stack
Frequently asked questions
What does dair-ai build on GitHub?
DAIR.AI builds a variety of resources on GitHub, focusing on artificial intelligence and machine learning. Notable repositories include the Prompt-Engineering-Guide and ML-Visuals, which offer guides, educational materials, and tools for researchers and practitioners.
Which programming languages does dair-ai use?
DAIR.AI predominantly uses Jupyter Notebook, Python, and TypeScript for its projects. These languages facilitate the development of interactive content, data analysis, and web applications in the field of artificial intelligence.
Are dair-ai's repositories public?
Yes, all of dair-ai's repositories are public. This transparency allows anyone to access, use, and contribute to their extensive range of projects related to artificial intelligence and machine learning.
Is this exposure intended?
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