FluxAIGridComparisons is a repository containing a collection of different image grids generated using Flux. These grids showcase various attributes such as hairstyles, clothing, nationalities, and ages. The repository serves as a visual comparison tool for exploring different characteristics within images.

Spring AI Examples is a repository containing various examples of integrating artificial intelligence capabilities into Spring applications. The examples cover a wide range of AI technologies such as machine learning, natural language processing, computer vision, and more. These examples serve as a practical guide for developers looking to incorporate AI functionalities into their Spring projects.

This repository contains a collection of AI projects implemented in Python, primarily in Jupyter notebooks. The projects cover various aspects of artificial intelligence, including machine learning, deep learning, natural language processing, computer vision, and more. Each project is designed to showcase different AI techniques and algorithms, providing a hands-on learning experience for users interested in exploring the field of artificial intelligence.

This repository contains material related to the new book _Synthetic Data and Generative AI_ by the author, including code for NoGAN, DeepResampling, and NoGAN_Hellinger. NoGAN is a tabular data synthesizer that outperforms GenAI methods in terms of speed and results, utilizing state-of-the-art quality metrics. DeepResampling is a fast NoGAN based on resampling and Bayesian Models with hyperparameter auto-tuning. NoGAN_Hellinger combines NoGAN and DeepResampling with the Hellinger model evaluation metric.

This repository is a curated list of resources for learning and working with Large Language Models (LLMs). It includes a collection of articles, tutorials, tools, datasets, and research papers related to LLMs such as GPT-3, BERT, and Transformer models. Whether you are a researcher, developer, or enthusiast interested in natural language processing and artificial intelligence, this repository provides valuable resources to help you understand, implement, and experiment with LLMs.

Large Language Models (LLM) are language models built by deep neural networks containing hundreds of billions of weights, trained on a large amount of unlabeled text using self-supervised learning methods. Since 2018, companies and research institutions including Google, OpenAI, Meta, Baidu, and Huawei have released various models such as BERT, GPT, etc., which have performed well in almost all natural language processing tasks. Starting in 2021, large models have shown explosive growth, especially after the release of ChatGPT in November 2022, attracting worldwide attention. Users can interact with systems using natural language to achieve various tasks from understanding to generation, including question answering, classification, summarization, translation, and chat. Large language models demonstrate powerful knowledge of the world and understanding of language. This repository introduces the basic theory of large language models including language models, distributed model training, and reinforcement learning, and uses the Deepspeed-Chat framework as an example to introduce the implementation of large language models and ChatGPT-like systems.

InvokeAI is a leading creative engine built to empower professionals and enthusiasts alike. Generate and create stunning visual media using the latest AI-driven technologies. InvokeAI offers an industry leading Web Interface, interactive Command Line Interface, and also serves as the foundation for multiple commercial products.

GEN-AI is a versatile Python library for implementing various artificial intelligence algorithms and models. It provides a wide range of tools and functionalities to support machine learning, deep learning, natural language processing, computer vision, and reinforcement learning tasks. With GEN-AI, users can easily build, train, and deploy AI models for diverse applications such as image recognition, text classification, sentiment analysis, object detection, and game playing. The library is designed to be user-friendly, efficient, and scalable, making it suitable for both beginners and experienced AI practitioners.

Deeppowers is a powerful Python library for deep learning applications. It provides a wide range of tools and utilities to simplify the process of building and training deep neural networks. With Deeppowers, users can easily create complex neural network architectures, perform efficient training and optimization, and deploy models for various tasks. The library is designed to be user-friendly and flexible, making it suitable for both beginners and experienced deep learning practitioners.

The ai-workshop-code repository contains code examples and tutorials for various artificial intelligence concepts and algorithms. It serves as a practical resource for individuals looking to learn and implement AI techniques in their projects. The repository covers a wide range of topics, including machine learning, deep learning, natural language processing, computer vision, and reinforcement learning. By exploring the code and following the tutorials, users can gain hands-on experience with AI technologies and enhance their understanding of how these algorithms work in practice.

Awesome-RAG is a repository that lists recent developments in Retrieval-Augmented Generation (RAG) for large language models (LLM). It includes accepted papers, evaluation datasets, latest news, and papers from various conferences like NIPS, EMNLP, ACL, ICML, and ICLR. The repository is continuously updated and aims to build a general framework for RAG. Researchers are encouraged to submit pull requests to update information in their papers. The repository covers a wide range of topics related to RAG, including knowledge-enhanced generation, contrastive reasoning, self-alignment, mobile agents, and more.

This repository serves as an archive for computer science learning notes, codes, and materials. It covers a wide range of topics including basic knowledge, AI, backend & big data, tools, and other related areas. The content is organized into sections and subsections for easy navigation and reference. Users can find learning resources, programming practices, and tutorials on various subjects such as languages, data structures & algorithms, AI, frameworks, databases, development tools, and more. The repository aims to support self-learning and skill development in the field of computer science.

Simple AI is a lightweight Python library for implementing basic artificial intelligence algorithms. It provides easy-to-use functions and classes for tasks such as machine learning, natural language processing, and computer vision. With Simple AI, users can quickly prototype and deploy AI solutions without the complexity of larger frameworks.

This repository provides baselines for various competitions, a few top solutions for some competitions, and independent deep learning projects. Baselines serve as entry guides for competitions, suitable for beginners to make their first submission. Top solutions are more complex and refined versions of baselines, with limited quantity but enhanced quality. The repository is maintained by a single author, yunsuxiaozi, offering code improvements and annotations for better understanding. Users can support the repository by learning from it and providing feedback.

The ai-collection repository is a collection of various artificial intelligence projects and tools aimed at helping developers and researchers in the field of AI. It includes implementations of popular AI algorithms, datasets for training machine learning models, and resources for learning AI concepts. The repository serves as a valuable resource for anyone interested in exploring the applications of artificial intelligence in different domains.

A drill of scientific methods, processes, algorithms, and systems to build stories & models. An in-depth learning resource for humans. This repository is designed for individuals aiming to excel in the field of Data and AI, providing video sessions and text content for learning. It caters to those in leadership positions, professionals, and students, emphasizing the need for dedicated effort to achieve excellence in the tech field. The content covers various topics with a focus on practical application.

FluxAIGridComparisons is a repository containing a collection of different image grids generated using Flux. These grids showcase various attributes such as hairstyles, clothing, nationalities, and ages. The repository serves as a visual comparison tool for exploring different characteristics within images.

Awesome AI is a repository that collects and shares resources in the fields of large language models (LLM), AI-assisted programming, AI drawing, and more. It explores the application and development of generative artificial intelligence. The repository provides information on various AI tools, models, and platforms, along with tutorials and web products related to AI technologies.

sdkit (stable diffusion kit) is an easy-to-use library for utilizing Stable Diffusion in AI Art projects. It includes features like ControlNets, LoRAs, Textual Inversion Embeddings, GFPGAN, CodeFormer for face restoration, RealESRGAN for upscaling, k-samplers, support for custom VAEs, NSFW filter, model-downloader, parallel GPU support, and more. It offers a model database, auto-scanning for malicious models, and various optimizations. The API consists of modules for loading models, generating images, filters, model merging, and utilities, all managed through the sdkit.Context object.

LearnPrompt is a permanent, free, open-source AIGC course platform that currently supports various tools like ChatGPT, Agent, Midjourney, Runway, Stable Diffusion, AI digital humans, AI voice & music, and large model fine-tuning. The platform offers features such as multilingual support, comment sections, daily selections, and submissions. Users can explore different modules, including sound cloning, RAG, GPT-SoVits, and OpenAI Sora world model. The platform aims to continuously update and provide tutorials, examples, and knowledge systems related to AI technologies.

FluxAIGridComparisons is a repository containing a collection of different image grids generated using Flux. These grids showcase various attributes such as hairstyles, clothing, nationalities, and ages. The repository serves as a visual comparison tool for exploring different characteristics within images.

FuseAI is a repository that focuses on knowledge fusion of large language models. It includes FuseChat, a state-of-the-art 7B LLM on MT-Bench, and FuseLLM, which surpasses Llama-2-7B by fusing three open-source foundation LLMs. The repository provides tech reports, releases, and datasets for FuseChat and FuseLLM, showcasing their performance and advancements in the field of chat models and large language models.

Prajna is an open-source programming language specifically developed for building more modular, automated, and intelligent artificial intelligence infrastructure. It aims to cater to various stages of AI research, training, and deployment by providing easy access to CPU, GPU, and various TPUs for AI computing. Prajna features just-in-time compilation, GPU/heterogeneous programming support, tensor computing, syntax improvements, and user-friendly interactions through main functions, Repl, and Jupyter, making it suitable for algorithm development and deployment in various scenarios.

LangChain-Udemy-Course is a comprehensive course directory focusing on LangChain, a framework for generative AI applications. The course covers various aspects such as OpenAI API usage, prompt templates, Chains exploration, callback functions, memory techniques, RAG implementation, autonomous agents, hybrid search, LangSmith utilization, microservice architecture, and LangChain Expression Language. Learners gain theoretical knowledge and practical insights to understand and apply LangChain effectively in generative AI scenarios.

A curated list of open-ended learning AI resources focusing on algorithms that invent new and complex tasks endlessly, inspired by human advancements. The repository includes papers, safety considerations, surveys, perspectives, and blog posts related to open-ended AI research.