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AI Training and Knowledge Repository

Welcome to the AI Training and Knowledge repository! This repository is designed to help both beginners and advanced learners deepen their understanding of Artificial Intelligence (AI) concepts, techniques, and applications. The content is divided into two main sections:

  1. AI_Learning: For those new to AI, covering foundational topics in AI, Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP).
  2. AI_Knowledge: Advanced topics for more experienced practitioners, including fine-tuning large language models (LLMs), cutting-edge research on Graph-RAG, and LLM-based QA evaluation.

Table of Contents

Introduction

This repository is a comprehensive guide to mastering both the fundamentals and advanced topics in AI. It’s designed to serve as a self-paced learning resource for individuals in technical roles like backend and frontend developers who want to get started with AI, as well as for more experienced AI engineers who wish to explore the latest in AI research.

Folder Structure

AI_Learning

The AI_Learning folder contains beginner-friendly content aimed at providing a solid foundation in AI, ML, DL, and NLP.

Topics include:

  • Artificial Intelligence Overview: Introduction to AI, ML, DL, NLP, and their relationships.
  • Machine Learning:
    • Supervised, Unsupervised, and Semi-supervised Learning
    • Key ML Algorithms (Linear Regression, Decision Trees, SVMs, etc.)
    • Model evaluation techniques (accuracy, precision, recall, F1-score)
    • Feature Engineering and Data Preprocessing
  • Deep Learning:
    • Neural Networks, CNNs, RNNs, GANs
    • Activation Functions, Backpropagation, Loss Functions
  • Natural Language Processing:
    • Text Classification, Named Entity Recognition, Machine Translation
    • Key techniques: Bag of Words, Word Embeddings, TF-IDF
  • Large Language Models (LLMs):
    • Transformers, Text embeddings, and LLM tokenizers

Each section includes links to learning resources such as videos, tutorials, and code notebooks for hands-on experience.

AI_Knowledge

The AI_Knowledge folder is designed for AI professionals and researchers looking to explore advanced topics.

Advanced topics include:

  • LLM Fine-Tuning:
    • Methods like LoRA (Low-Rank Adaptation) and QLoRA
    • Efficient fine-tuning techniques for resource-constrained environments
  • Graph-RAG (Retrieval-Augmented Generation):
    • Combining graph-based indexing with retrieval-augmented generation to improve LLM responses for complex queries
  • LLM QA Evaluation:
    • Methods for evaluating the performance and factuality of LLMs in QA tasks
    • Techniques to ensure cross-lingual performance of LLMs across multiple languages

This section is supported by research papers, practical examples, and in-depth explanations of cutting-edge AI models and techniques.

How to Use This Repository

  1. For Beginners:

    • Start with the AI_Learning folder to get familiar with core AI concepts. Follow the tutorials, read the summaries, and experiment with the provided code samples.

  2. For Advanced Users:

    • Dive into the AI_Knowledge folder to explore complex topics like fine-tuning LLMs and advanced AI techniques like Graph-RAG.

  3. Hands-on Practice:

    • Utilize the provided Python notebooks for practical exercises and experimentation.

Getting Started

To get started, clone this repository to your local machine:

git clone https://github.com/your-repo/ai-training.git
cd ai-training
  • Navigate to the AI_Learning folder if you are a beginner.
  • Explore the AI_Knowledge folder for more advanced topics.

Make sure to install the necessary Python packages:

pip install -r requirements.txt

Contributing

We welcome contributions! Whether it’s improving documentation, adding new tutorials, or expanding on advanced topics, feel free to submit a pull request.

  1. Fork the repository
  2. Create a new branch (git checkout -b feature-branch)
  3. Commit your changes (git commit -m 'Add new feature')
  4. Push to the branch (git push origin feature-branch)
  5. Open a pull request