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What is MLpedia?
MLpedia provides accessible information on a wide range of Artificial Intelligence and Machine Learning topics. It breaks down complex subjects such as Intelligent Agents, Compound AI Systems, Diffusion Models, Large Language Models (LLMs), and Reinforcement Learning from Human Feedback (RLHF) into understandable explanations.
The platform covers various technical aspects including Low-Rank Adaptation (LoRA), Retrieval-Augmented Generation (RAG), Vector Databases, Prompt Engineering techniques like Chain-of-Thought, and evaluation metrics like Perplexity. It aims to serve as an educational resource for anyone looking to deepen their understanding of fundamental and advanced concepts within the AI field, including model architectures and training methodologies like Machine Unlearning and Direct Preference Optimization.
The platform covers various technical aspects including Low-Rank Adaptation (LoRA), Retrieval-Augmented Generation (RAG), Vector Databases, Prompt Engineering techniques like Chain-of-Thought, and evaluation metrics like Perplexity. It aims to serve as an educational resource for anyone looking to deepen their understanding of fundamental and advanced concepts within the AI field, including model architectures and training methodologies like Machine Unlearning and Direct Preference Optimization.
Features
- Concept Explanations: Detailed descriptions of core AI and ML concepts.
- Featured Concepts Section: Highlights important and trending topics in AI.
- Technical Deep Dives: Explains methodologies like RLHF, DPO, LoRA, and RAG.
- Prompt Engineering Guidance: Information on techniques like Chain-of-Thought and Few-Shot Prompting.
- Foundational Model Information: Covers Foundation Models, LLMs, and emergent abilities.
- Database Concepts: Explains Vector Databases and their role.
- Introductory Articles: Provides overviews of ML, Generative AI, and specific network architectures.
Use Cases
- Learning fundamental AI and ML concepts.
- Understanding specific techniques like RAG or LoRA.
- Researching terminology for AI projects.
- Getting definitions for AI/ML jargon.
- Educating oneself on prompt engineering strategies.
- Exploring different types of neural networks.
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