Democratizing Artificial Intelligence Research, Education, and Technologies
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Il profilo pubblico GitHub di DAIR.AI mostra una vasta gamma di progetti focalizzati sulla democratizzazione della ricerca e delle tecnologie nell'intelligenza artificiale. Le lingue principali utilizzate includono Jupyter Notebook, Python e TypeScript. Tra i repository più noti ci sono il Prompt-Engineering-Guide e ml-visuals, che offrono risorse preziose per la comunità AI.
🐙 Guides, papers, lessons, notebooks and resources for prompt engineering, context engineering, RAG, and AI Agents.
🎨 ML Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.
📺 Discover the latest machine learning / AI courses on YouTube.
🔥Highlighting the top ML papers every week.
Explanation to key concepts in ML
🎓 Sharing machine learning course / lecture notes.
🧮 A collection of resources to learn mathematics for machine learning
:fire: Machine Learning Notebooks
🧠 A study guide to learn about Transformers
✍️ A carefully curated list of NLP paper summaries
🟠 A study guide to learn about Graph Neural Networks (GNNs)
A collection of resources to learn about MLOPs.
A curated index to track AI-powered products.
🔥 A collection of PyTorch notebooks for learning and practicing deep learning
🧠 Material for the Deep Learning Study Group
📘 Contains a series of hands-on notebooks for learning the fundamentals of NLP
📰Natural language processing (NLP) newsletter
DAIR.AI Academy Plugins
A guide to building awesome machine learning projects.
Home of DAIR.AI
:smile: Dataset for Emotion Recognition Research
Deep research agents using MiniMax M2.1 interleaved thinking
A guide to improve your research proposals.
📄 A repo containing notes and discussions for our weekly NLP/ML paper discussions.
A club to keep learning about ML
Nessuna descrizione fornita per questo repository.
📘Overview of Modern Deep Learning Techniques Applied to Natural Language Processing
🔬 Sharing your data science notebooks with the community has never been this easy.
Claude agent for Deep AI Research
Text Similarity Search Application using Modern NLP and Elasticsearch
Prompt Engineering for Large Language Models - Notebooks, Demos, Exercises, and Projects
Repository for ODSC talk related to Deep Learning NLP
Deploy a mult-agent deep research agent with Replit and n8n
:smile: Multilingual emotion analysis research
Example for Logging LLM Evaluator Prompt Responses
Writing Primer for Data Scientists
✨ A report of the most important NLP highlights (A Yearly Report - 2018, 2019)
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AI Datasets
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A project for implementing ML and NLP papers
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➕Contains useful deep learning notations for writing blogs, presentations, and papers.
Materials for the Prompt Engineering for LLMs (Cohort 6)
Final project for the Cursor course
Materials for the Prompt Engineering for LLMs (Cohort 8)
NLP tutorials I have written using TensorFlow
Course material for Prompt Engineering for LLMs
Materials for the Prompt Engineering for LLMs Course (Cohort 11)
Materials for the Prompt Engineering for LLMs (Cohort 13)
Materials for Prompt Engineering for LLMs (Cohort 9)
Code, Demos, and Exercises for Prompt Engineering for LLMs Course
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comet llm support
:smile: Building a deep learning based affective computing platform
All paper presentation material will be added here
🧠MRI analysis using PyTorch and MedicalTorch
Nessuna descrizione fornita per questo repository.
Nessuna descrizione fornita per questo repository.
Materials for Cohort 10
Nessuna descrizione fornita per questo repository.
DAIR.AI Claude Code Plugins
Nessuna descrizione fornita per questo repository.
Contains all the material used for the "Applied Deep Learning for NLP Using PyTorch" meetup at AppWorks
Nessuna descrizione fornita per questo repository.
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Machine Learning in the Elastic Stack
DAIR.AI sviluppa una varietà di progetti open source che includono guide, risorse didattiche e strumenti per l'intelligenza artificiale. I repository come Prompt-Engineering-Guide e ml-visuals sono esempi significativi del loro lavoro.
DAIR.AI utilizza diversi linguaggi di programmazione, tra cui Jupyter Notebook, Python, TypeScript, HTML e CSS. Questi linguaggi supportano la creazione di risorse educative e strumenti per l'AI.
Sì, tutti i repository di DAIR.AI su GitHub sono pubblici. Questo consente a chiunque di accedere alle loro risorse e contribuire alla democratizzazione della ricerca nell'intelligenza artificiale.
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