MegaDetector

MegaDetector is an AI model that identifies animals, people, and vehicles in camera trap images (which also makes it useful for eliminating blank images). This model is trained on several million images from a variety of ecosystems. MegaDetector is just one of many tools that aims to make conservation biologists more efficient with AI. If you want to learn about other ways to use AI to accelerate camera trap workflows, check out our of the field, affectionately titled "Everything I know about machine learning and camera traps".

ChatGPT is a large language model that can generate human-like text, translate languages, write different kinds of creative content, and answer your questions in a conversational way. It is trained on a massive amount of text data, and it is able to understand and respond to a wide range of natural language prompts. Here are 5 jobs suitable for this tool, in lowercase letters: 1. content writer 2. chatbot assistant 3. language translator 4. creative writer 5. researcher

AI Summer is a repository focused on providing workshops and resources for developing foundational skills in generative AI models and transformer models. The repository offers practical applications for inferencing and training, with a specific emphasis on understanding and utilizing advanced AI chat models like BingGPT. Participants are encouraged to engage in interactive programming environments, decide on projects to work on, and actively participate in discussions and breakout rooms. The workshops cover topics such as generative AI models, retrieval-augmented generation, building AI solutions, and fine-tuning models. The goal is to equip individuals with the necessary skills to work with AI technologies effectively and securely, both locally and in the cloud.

MediaAI is a repository containing lectures and materials for Aalto University's AI for Media, Art & Design course. The course is a hands-on, project-based crash course focusing on deep learning and AI techniques for artists and designers. It covers common AI algorithms & tools, their applications in art, media, and design, and provides hands-on practice in designing, implementing, and using these tools. The course includes lectures, exercises, and a final project based on students' interests. Students can complete the course without programming by creatively utilizing existing tools like ChatGPT and DALL-E. The course emphasizes collaboration, peer-to-peer tutoring, and project-based learning. It covers topics such as text generation, image generation, optimization, and game AI.

Start Machine Learning in 2024 is a comprehensive guide for beginners to advance in machine learning and artificial intelligence without any prior background. The guide covers various resources such as free online courses, articles, books, and practical tips to become an expert in the field. It emphasizes self-paced learning and provides recommendations for learning paths, including videos, podcasts, and online communities. The guide also includes information on building language models and applications, practicing through Kaggle competitions, and staying updated with the latest news and developments in AI. The goal is to empower individuals with the knowledge and resources to excel in machine learning and AI.

This repository is a comprehensive guide for individuals looking to start and improve their skills in Large Language Models (LLMs) without an advanced background in the field. It provides free resources, online courses, books, articles, and practical tips to become an expert in machine learning. The guide covers topics such as terminology, transformers, prompting, retrieval augmented generation (RAG), and more. It also includes recommendations for podcasts, YouTube videos, and communities to stay updated with the latest news in AI and LLMs.

Claudine is an AI agent designed to reason and act autonomously, leveraging the Anthropic API, Unix command line tools, HTTP, local hard drive data, and internet data. It can administer computers, analyze files, implement features in source code, create new tools, and gather contextual information from the internet. Users can easily add specialized tools. Claudine serves as a blueprint for implementing complex autonomous systems, with potential for customization based on organization-specific needs. The tool is based on the anthropic-kotlin-sdk and aims to evolve into a versatile command line tool similar to 'git', enabling branching sessions for different tasks.

Pwnagotchi is an AI tool leveraging bettercap to learn from WiFi environments and maximize crackable WPA key material. It uses LSTM with MLP feature extractor for A2C agent, learning over epochs to improve performance in various WiFi environments. Units can cooperate using a custom parasite protocol. Visit https://www.pwnagotchi.ai for documentation and community links.

AI Expert Roadmap is a comprehensive guide to becoming an Artificial Intelligence Expert in 2022. It provides detailed charts and paths for individuals interested in data science, machine learning, and AI. The roadmap covers fundamental concepts, data science, machine learning, deep learning, data engineering, and big data engineering. Created by AMAI GmbH, this resource aims to help individuals navigate the AI landscape and make informed decisions about their learning path. The interactive version with links is available at i.am.ai/roadmap. Stay updated by starring and watching the GitHub repo for new content.

TinyTroupe is an experimental Python library that leverages Large Language Models (LLMs) to simulate artificial agents called TinyPersons with specific personalities, interests, and goals in simulated environments. The focus is on understanding human behavior through convincing interactions and customizable personas for various applications like advertisement evaluation, software testing, data generation, project management, and brainstorming. The tool aims to enhance human imagination and provide insights for better decision-making in business and productivity scenarios.

The Machine Learning for Public Policy Lab is a project-based course focused on solving real-world problems using machine learning in the context of public policy and social good. Students will gain hands-on experience building end-to-end machine learning systems, developing skills in problem formulation, working with messy data, communicating with non-technical stakeholders, model interpretability, and understanding algorithmic bias & disparities. The course covers topics such as project scoping, data acquisition, feature engineering, model evaluation, bias and fairness, and model interpretability. Students will work in small groups on policy projects, with graded components including project proposals, presentations, and final reports.

AimStar is a free and open-source external cheat for CS2, written in C++. It is available for Windows 8.1+ and features ESP, glow, radar, crosshairs, no flash, bhop, aimbot, triggerbot, language settings, hit sound, and bomb timer. The code is mostly contributed by users and may be messy. The project is for learning purposes only and should not be used for illegal activities.

The 'ShitSpotter' repository is dedicated to developing a poop-detection algorithm and dataset for creating a phone app that helps locate dog poop in outdoor environments. The project involves training a PyTorch network to detect poop in images and provides scripts for detecting poop in unseen images using a pretrained model. The dataset consists of mostly outdoor images taken with a phone, with a process involving before and after pictures of the poop. The project aims to enable various applications, such as AR glasses for poop detection and efficient cleaning of public areas by city governments. The code, dataset, and pretrained models are open source with permissive licensing and distributed via IPFS, BitTorrent, and centralized mechanisms.

The LLM Evaluation guidebook provides comprehensive guidance on evaluating language model performance, including different evaluation methods, designing evaluations, and practical tips. It caters to both beginners and advanced users, offering insights on model inference, tokenization, and troubleshooting. The guide covers automatic benchmarks, human evaluation, LLM-as-a-judge scenarios, troubleshooting practicalities, and general knowledge on LLM basics. It also includes planned articles on automated benchmarks, evaluation importance, task-building considerations, and model comparison challenges. The resource is enriched with recommended links and acknowledgments to contributors and inspirations.

A drill of scientific methods, processes, algorithms, and systems to build stories & models. An in-depth learning resource for humans. This is a drill for people who aim to be in the top 1% of Data and AI experts. The repository provides a routine for deep and shallow work sessions, covering topics from Python to AI/ML System Design and Personal Branding & Portfolio. It emphasizes the importance of continuous effort and action in the tech field.

This repository introduces and helps organizations get started with building AI Apps and incorporating Large Language Models (LLMs) into them. The workshop covers topics such as prompt engineering, AI orchestration, and deploying AI apps. Participants will learn how to use Azure OpenAI, Langchain/ Semantic Kernel, Qdrant, and Azure AI Search to build intelligent applications.

MegaDetector is an AI model that identifies animals, people, and vehicles in camera trap images (which also makes it useful for eliminating blank images). This model is trained on several million images from a variety of ecosystems. MegaDetector is just one of many tools that aims to make conservation biologists more efficient with AI. If you want to learn about other ways to use AI to accelerate camera trap workflows, check out our of the field, affectionately titled "Everything I know about machine learning and camera traps".

SpeciesNet is an ensemble of AI models designed for classifying wildlife in camera trap images. It consists of an object detector that finds objects of interest in wildlife camera images and an image classifier that classifies those objects to the species level. The ensemble combines these two models using heuristics and geographic information to assign each image to a single category. The models have been trained on a large dataset of camera trap images and are used for species recognition in the Wildlife Insights platform.

**Prompt flow** is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality.

DeepEval is a simple-to-use, open-source LLM evaluation framework specialized for unit testing LLM outputs. It incorporates various metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., and runs locally on your machine for evaluation. It provides a wide range of ready-to-use evaluation metrics, allows for creating custom metrics, integrates with any CI/CD environment, and enables benchmarking LLMs on popular benchmarks. DeepEval is designed for evaluating RAG and fine-tuning applications, helping users optimize hyperparameters, prevent prompt drifting, and transition from OpenAI to hosting their own Llama2 with confidence.

MegaDetector is an AI model that identifies animals, people, and vehicles in camera trap images (which also makes it useful for eliminating blank images). This model is trained on several million images from a variety of ecosystems. MegaDetector is just one of many tools that aims to make conservation biologists more efficient with AI. If you want to learn about other ways to use AI to accelerate camera trap workflows, check out our of the field, affectionately titled "Everything I know about machine learning and camera traps".

LeapfrogAI is a self-hosted AI platform designed to be deployed in air-gapped resource-constrained environments. It brings sophisticated AI solutions to these environments by hosting all the necessary components of an AI stack, including vector databases, model backends, API, and UI. LeapfrogAI's API closely matches that of OpenAI, allowing tools built for OpenAI/ChatGPT to function seamlessly with a LeapfrogAI backend. It provides several backends for various use cases, including llama-cpp-python, whisper, text-embeddings, and vllm. LeapfrogAI leverages Chainguard's apko to harden base python images, ensuring the latest supported Python versions are used by the other components of the stack. The LeapfrogAI SDK provides a standard set of protobuffs and python utilities for implementing backends and gRPC. LeapfrogAI offers UI options for common use-cases like chat, summarization, and transcription. It can be deployed and run locally via UDS and Kubernetes, built out using Zarf packages. LeapfrogAI is supported by a community of users and contributors, including Defense Unicorns, Beast Code, Chainguard, Exovera, Hypergiant, Pulze, SOSi, United States Navy, United States Air Force, and United States Space Force.

This Docker image for LLaVA (Large Language and Vision Assistant) provides a convenient way to run LLaVA locally or on RunPod. LLaVA is a powerful AI tool that combines natural language processing and computer vision capabilities. With this Docker image, you can easily access LLaVA's functionalities for various tasks, including image captioning, visual question answering, text summarization, and more. The image comes pre-installed with LLaVA v1.2.0, Torch 2.1.2, xformers 0.0.23.post1, and other necessary dependencies. You can customize the model used by setting the MODEL environment variable. The image also includes a Jupyter Lab environment for interactive development and exploration. Overall, this Docker image offers a comprehensive and user-friendly platform for leveraging LLaVA's capabilities.

The 'carrot' repository on GitHub provides a list of free and user-friendly ChatGPT mirror sites for easy access. The repository includes sponsored sites offering various GPT models and services. Users can find and share sites, report errors, and access stable and recommended sites for ChatGPT usage. The repository also includes a detailed list of ChatGPT sites, their features, and accessibility options, making it a valuable resource for ChatGPT users seeking free and unlimited GPT services.

TrustLLM is a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.

AI-YinMei is an AI virtual anchor Vtuber development tool (N card version). It supports fastgpt knowledge base chat dialogue, a complete set of solutions for LLM large language models: [fastgpt] + [one-api] + [Xinference], supports docking bilibili live broadcast barrage reply and entering live broadcast welcome speech, supports Microsoft edge-tts speech synthesis, supports Bert-VITS2 speech synthesis, supports GPT-SoVITS speech synthesis, supports expression control Vtuber Studio, supports painting stable-diffusion-webui output OBS live broadcast room, supports painting picture pornography public-NSFW-y-distinguish, supports search and image search service duckduckgo (requires magic Internet access), supports image search service Baidu image search (no magic Internet access), supports AI reply chat box [html plug-in], supports AI singing Auto-Convert-Music, supports playlist [html plug-in], supports dancing function, supports expression video playback, supports head touching action, supports gift smashing action, supports singing automatic start dancing function, chat and singing automatic cycle swing action, supports multi scene switching, background music switching, day and night automatic switching scene, supports open singing and painting, let AI automatically judge the content.