Ai Ml
What is Ai Ml
AI-ML is a repository that encompasses various concepts in artificial intelligence and machine learning, including large language models (LLMs) and their applications.
Use cases
Use cases include predictive analytics, language translation applications, automated customer support, content generation, and image recognition tasks.
How to use
Users can explore different AI/ML concepts and models through the repository, implementing them in their projects for tasks such as data extraction, predictions, and language processing.
Key features
Key features include Machine Learning for data extraction and predictions, Natural Language Processing for language translation, Computer Vision capabilities, Generative AI for text generation, and Large Language Models for human-like language tasks.
Where to use
AI-ML can be utilized in various fields such as technology, healthcare, finance, education, and any domain requiring data analysis, language processing, or automation.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Overview
What is Ai Ml
AI-ML is a repository that encompasses various concepts in artificial intelligence and machine learning, including large language models (LLMs) and their applications.
Use cases
Use cases include predictive analytics, language translation applications, automated customer support, content generation, and image recognition tasks.
How to use
Users can explore different AI/ML concepts and models through the repository, implementing them in their projects for tasks such as data extraction, predictions, and language processing.
Key features
Key features include Machine Learning for data extraction and predictions, Natural Language Processing for language translation, Computer Vision capabilities, Generative AI for text generation, and Large Language Models for human-like language tasks.
Where to use
AI-ML can be utilized in various fields such as technology, healthcare, finance, education, and any domain requiring data analysis, language processing, or automation.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Content
AI/ML Terminologies
| Description | Examples | |
|---|---|---|
| Generative AI (GenAI) | A broad category of AI models that can create new content such as text, images, music, code, etc. - All LLMs are GenAI, but not all GenAI are LLMs (some generate images, music, etc.). |
ChatGPT (text), DALL·E (images), Sora (video), GitHub Copilot (code) |
| Large Language Model (LLM) | A type of Generative AI specifically trained on vast amounts of text to understand and generate human language. - Text-based tasks like summarization, translation, question answering, and code generation. |
GPT-4, Claude, PaLM, LLaMA |
| AI Agents | Software entities that can autonomously perceive, decide, and act in an environment to achieve a goal. | A shopping assistant that books a trip end-to-end; Auto-GPT. |
| Agentic AI | A subset of AI focused on autonomous agents with long-term planning, memory, tool use, and goal pursuit. | OpenAI’s experiments with tool-using GPT agents; Cognosys, HyperGPT. |
| Multi-Agent Systems (MAS) | Systems where multiple AI agents interact with each other, possibly collaborating or competing to achieve goals. | Multi-agent simulations, decentralized trading bots, or AI-driven game environments. |
| Machine Learning | Extract data, make predictions and perform actions. Deep Learning is its subfield. |
|
| Natural Language Processing | Language transaction like english to chinese. | |
| Quantization | Quantization enables machine learning models to use less memory and computing power for faster responses and reduce costs. However, it can make AI inference less precise. | |
| Pre-training | ||
| Fine-tuning | Fine tuning is a technique used to improve the performance of a pre-trained AI model on a specific task. |
Dev Tools Supporting MCP
The following are the main code editors that support the Model Context Protocol. Click the link to visit the official website for more information.
