The 'machine-learning-research' repository is a comprehensive collection of resources related to mathematics, machine learning, deep learning, artificial intelligence, data science, and various scientific fields. It includes materials such as courses, tutorials, books, podcasts, communities, online courses, papers, and dissertations. The repository covers topics ranging from fundamental math skills to advanced machine learning concepts, with a focus on applications in healthcare, genetics, computational biology, precision health, and AI in science. It serves as a valuable resource for individuals interested in learning and researching in the fields of machine learning and related disciplines.

The 'Awesome Artificial Intelligence Research' repository is a curated list of up-to-date research papers in the field of Artificial Intelligence (AI). It aims to help researchers stay informed about cutting-edge research trends and topics in AI by providing a comprehensive collection of research paper lists. The repository covers various subfields of AI, including Machine Learning, Data Mining, Computer Vision, Natural Language Processing, Audio & Speech, and other applications. It also includes tools for research such as public datasets and new paper recommendations.

This repository is a collection of free resources and a roadmap designed to help individuals learn Machine Learning and Artificial Intelligence concepts by providing key concepts, building blocks, roles, a learning roadmap, courses, certifications, books, tools & frameworks, research blogs, applied blogs, practice problems, communities, YouTube channels, newsletters, and must-read papers. It covers a wide range of topics from supervised learning to MLOps, offering guidance on learning paths, practical experience, and job interview preparation.

AI6127 is a course focusing on deep neural networks for natural language processing (NLP). It covers core NLP tasks and machine learning models, emphasizing deep learning methods using libraries like Pytorch. The course aims to teach students state-of-the-art techniques for practical NLP problems, including writing, debugging, and training deep neural models. It also explores advancements in NLP such as Transformers and ChatGPT.

This repository is a collection of research notes and resources focusing on training large language models (LLMs) and Reinforcement Learning from Human Feedback (RLHF). It includes methodologies, techniques, and state-of-the-art approaches for optimizing preferences and model alignment in LLM training. The purpose is to serve as a reference for researchers and engineers interested in reinforcement learning, large language models, model alignment, and alternative RL-based methods.

This repository contains code for the O'Reilly Live Online Training for 'Transformer Architectures for Generative AI'. The course provides a deep understanding of transformer architectures and their impact on natural language processing (NLP) and vision tasks. Participants learn to harness transformers to tackle problems in text, image, and multimodal AI through theory and practical exercises.

InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train interpretable glassbox models and explain blackbox systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions. Interpretability is essential for: - Model debugging - Why did my model make this mistake? - Feature Engineering - How can I improve my model? - Detecting fairness issues - Does my model discriminate? - Human-AI cooperation - How can I understand and trust the model's decisions? - Regulatory compliance - Does my model satisfy legal requirements? - High-risk applications - Healthcare, finance, judicial, ...

MMMU is a benchmark designed to evaluate multimodal models on college-level subject knowledge tasks, covering 30 subjects and 183 subfields with 11.5K questions. It focuses on advanced perception and reasoning with domain-specific knowledge, challenging models to perform tasks akin to those faced by experts. The evaluation of various models highlights substantial challenges, with room for improvement to stimulate the community towards expert artificial general intelligence (AGI).

llm_benchmarks is a collection of benchmarks and datasets for evaluating Large Language Models (LLMs). It includes various tasks and datasets to assess LLMs' knowledge, reasoning, language understanding, and conversational abilities. The repository aims to provide comprehensive evaluation resources for LLMs across different domains and applications, such as education, healthcare, content moderation, coding, and conversational AI. Researchers and developers can leverage these benchmarks to test and improve the performance of LLMs in various real-world scenarios.

This repository serves as a comprehensive guide for technical teams interested in developing conversational AI solutions using Retrieval-Augmented Generation (RAG) techniques. It covers theoretical knowledge and practical code implementations, making it suitable for individuals with a basic technical background. The content includes information on large language models (LLMs), transformers, prompt engineering, embeddings, vector stores, and various other key concepts related to conversational AI. The repository also provides hands-on examples for two different use cases, along with implementation details and performance analysis.

A repository sharing literatures and resources about Large Language Models (LLMs) and beyond. It includes tutorials, notebooks, course assignments, development stages, modeling, inference, training, applications, study, and basics related to LLMs. The repository covers various topics such as language models, transformers, state space models, multi-modal language models, training recipes, applications in autonomous driving, code, math, embodied intelligence, and more. The content is organized by different categories and provides comprehensive information on LLMs and related topics.

The LLM course is divided into three parts: 1. 🧩 **LLM Fundamentals** covers essential knowledge about mathematics, Python, and neural networks. 2. 🧑‍🔬 **The LLM Scientist** focuses on building the best possible LLMs using the latest techniques. 3. 👷 **The LLM Engineer** focuses on creating LLM-based applications and deploying them. For an interactive version of this course, I created two **LLM assistants** that will answer questions and test your knowledge in a personalized way: * 🤗 **HuggingChat Assistant**: Free version using Mixtral-8x7B. * 🤖 **ChatGPT Assistant**: Requires a premium account. ## 📝 Notebooks A list of notebooks and articles related to large language models. ### Tools | Notebook | Description | Notebook | |----------|-------------|----------| | 🧐 LLM AutoEval | Automatically evaluate your LLMs using RunPod |  | | 🥱 LazyMergekit | Easily merge models using MergeKit in one click. |  | | 🦎 LazyAxolotl | Fine-tune models in the cloud using Axolotl in one click. |  | | ⚡ AutoQuant | Quantize LLMs in GGUF, GPTQ, EXL2, AWQ, and HQQ formats in one click. |  | | 🌳 Model Family Tree | Visualize the family tree of merged models. |  | | 🚀 ZeroSpace | Automatically create a Gradio chat interface using a free ZeroGPU. |  |

A curated list of Generative AI projects, tools, artworks, and models

This repository compiles a list of academic papers that evaluate, align, simulate, and provide surveys or perspectives on the use of Large Language Models (LLMs) in the field of Social Science. The papers cover various aspects of LLM research, including assessing their alignment with human values, evaluating their capabilities in tasks such as opinion formation and moral reasoning, and exploring their potential for simulating social interactions and addressing issues in diverse fields of Social Science. The repository aims to provide a comprehensive resource for researchers and practitioners interested in the intersection of LLMs and Social Science.

Tunix is a JAX-based library designed for post-training Large Language Models. It provides efficient support for supervised fine-tuning, reinforcement learning, and knowledge distillation. Tunix leverages JAX for accelerated computation and integrates seamlessly with the Flax NNX modeling framework. The library is modular, efficient, and designed for distributed training on accelerators like TPUs. Currently in early development, Tunix aims to expand its capabilities, usability, and performance.

The 'machine-learning-research' repository is a comprehensive collection of resources related to mathematics, machine learning, deep learning, artificial intelligence, data science, and various scientific fields. It includes materials such as courses, tutorials, books, podcasts, communities, online courses, papers, and dissertations. The repository covers topics ranging from fundamental math skills to advanced machine learning concepts, with a focus on applications in healthcare, genetics, computational biology, precision health, and AI in science. It serves as a valuable resource for individuals interested in learning and researching in the fields of machine learning and related disciplines.

MLDL.Study is a free interactive learning platform focused on simplifying Machine Learning (ML) and Deep Learning (DL) education for students and enthusiasts. It features curated roadmaps, videos, articles, and other learning materials. The platform aims to provide a comprehensive learning experience for Indian audiences, with easy-to-follow paths for ML and DL concepts, diverse resources including video tutorials and articles, and a growing community of over 6000 users. Contributors can add new resources following specific guidelines to maintain quality and relevance. Future plans include expanding content for global learners, introducing a Python programming roadmap, and creating roadmaps for fields like Generative AI and Reinforcement Learning.

OpenUnivCourses is a repository that provides free university courses in machine learning from top universities like MIT, Stanford, Berkeley, Carnegie Mellon, NYU, University of Michigan, University of Pennsylvania, University of Chicago, Purdue, Cornell, University of Oxford, and CalTech. The repository includes a wide range of courses covering topics such as deep learning, reinforcement learning, natural language processing, and more. Users can access lectures, notes, and videos from these prestigious institutions to enhance their knowledge and skills in the field of artificial intelligence and machine learning.

This repository contains a collection of resources in the form of eBooks related to Data Science, Machine Learning, and similar topics.

PicHome is a powerful open-source cloud storage program that efficiently manages various types of files and excels in image and media file management. Its highlights include robust file sharing features and advanced AI-assisted management tools, providing users with a convenient and intelligent file management experience. The program offers diverse list modes, customizable file information display, enhanced quick file preview, advanced tagging, custom cover and preview images, multiple preview images, and multi-library management. Additionally, PicHome features strong file sharing capabilities, allowing users to share entire libraries, create personalized showcase web pages, and build complete data sharing websites. The AI-assisted management aspect includes AI file renaming, tagging, description writing, batch annotation, and file Q&A services, all aimed at improving file management efficiency. PicHome supports a wide range of file formats and can be applied in various scenarios such as e-commerce, gaming, design, development, enterprises, schools, labs, media, and entertainment institutions.

The 'machine-learning-research' repository is a comprehensive collection of resources related to mathematics, machine learning, deep learning, artificial intelligence, data science, and various scientific fields. It includes materials such as courses, tutorials, books, podcasts, communities, online courses, papers, and dissertations. The repository covers topics ranging from fundamental math skills to advanced machine learning concepts, with a focus on applications in healthcare, genetics, computational biology, precision health, and AI in science. It serves as a valuable resource for individuals interested in learning and researching in the fields of machine learning and related disciplines.

Awesome-TimeSeries-SpatioTemporal-LM-LLM is a curated list of Large (Language) Models and Foundation Models for Temporal Data, including Time Series, Spatio-temporal, and Event Data. The repository aims to summarize recent advances in Large Models and Foundation Models for Time Series and Spatio-Temporal Data with resources such as papers, code, and data. It covers various applications like General Time Series Analysis, Transportation, Finance, Healthcare, Event Analysis, Climate, Video Data, and more. The repository also includes related resources, surveys, and papers on Large Language Models, Foundation Models, and their applications in AIOps.

Moon is a monitoring and alerting platform suitable for multiple domains, supporting various application scenarios such as cloud-native, Internet of Things (IoT), and Artificial Intelligence (AI). It simplifies operational work of cloud-native monitoring, boasts strong IoT and AI support capabilities, and meets diverse monitoring needs across industries. Capable of real-time data monitoring, intelligent alerts, and fault response for various fields.

DownEdit is a fast and powerful program for downloading and editing videos from platforms like TikTok, Douyin, and Kuaishou. It allows users to effortlessly grab videos, make bulk edits, and utilize advanced AI features for generating videos, images, and sounds in bulk. The tool offers features like video, photo, and sound editing, downloading videos without watermarks, bulk AI generation, and AI editing for content enhancement.

AI TrendPublish is an AI-based trend discovery and content publishing system that supports multi-source data collection, intelligent summarization, and automatic publishing to WeChat official accounts. It features data collection from various sources, AI-powered content processing using DeepseekAI Together, key information extraction, intelligent title generation, automatic article publishing to WeChat official accounts with custom templates and scheduled tasks, notification system integration with Bark for task status updates and error alerts. The tool offers multiple templates for content customization and is built using Node.js + TypeScript with AI services from DeepseekAI Together, data sources including Twitter/X API and FireCrawl, and uses node-cron for scheduling tasks and EJS as the template engine.

Hunyuan-T1 is a cutting-edge large-scale hybrid Mamba reasoning model driven by reinforcement learning. It has been officially released as an upgrade to the Hunyuan Thinker-1-Preview model. The model showcases exceptional performance in deep reasoning tasks, leveraging the TurboS base and Mamba architecture to enhance inference capabilities and align with human preferences. With a focus on reinforcement learning training, the model excels in various reasoning tasks across different domains, showcasing superior abilities in mathematical, logical, scientific, and coding reasoning. Through innovative training strategies and alignment with human preferences, Hunyuan-T1 demonstrates remarkable performance in public benchmarks and internal evaluations, positioning itself as a leading model in the field of reasoning.

DownEdit is a fast and powerful program for downloading and editing videos from top platforms like TikTok, Douyin, and Kuaishou. Effortlessly grab videos from user profiles, make bulk edits throughout the entire directory with just one click. Advanced Chat & AI features let you download, edit, and generate videos, images, and sounds in bulk. Exciting new features are coming soon—stay tuned!