🔗 5 Techniques to Fine-Tune Large Language Models (LLMs) 🚀

With the rise of large language models (LLMs), fine-tuning for specific tasks has become more important than ever. But how can we do it efficiently without compromising performance? 🤔 Here are 5 advanced techniques that can help:

1. LoRA (Low-Rank Adaptation)
- LoRA reduces the number of trainable parameters by adding low-rank adaptation matrices, making fine-tuning faster and more memory-efficient.

2. LoRA-FA (LoRA with Feature Augmentation)
- This method combines LoRA with external feature augmentation, injecting task-specific features to further boost performance with minimal overhead.

3. Vera (Virtual Embedding Regularization Adaptation)
- Vera helps regularize model embeddings during fine-tuning, preventing overfitting and improving generalization across different domains.

4. Delta LoRA
- An extension of LoRA, this approach focuses on updating only the most significant layers, reducing computational costs while retaining fine-tuning effectiveness.

5. Prefix Tuning
- Instead of modifying model weights, this technique learns task-specific prefix tokens that steer the model’s output, enabling efficient adaptation to new tasks.

💡 10 Key Data Structures We Use Every Day

- list: keep your Twitter feeds
- stack: support undo/redo of the word editor
- queue: keep printer jobs, or send user actions in-game
- hash table: cashing systems
- Array: math operations
- heap: task scheduling
- tree: keep the HTML document, or for AI decision
- suffix tree: for searching string in a document
- graph: for tracking friendship, or path finding
- r-tree: for finding the nearest neighbor
- vertex buffer: for sending data to GPU for rendering

🔅 Everyday AI Concepts

📝 Learn key artificial intelligence concepts and discover how AI can benefit your team, organization, products, and services.

🌐 Author: Doug Rose
🔰 Level: General
⏰ Duration: 49m

📋 Topics: Artificial Intelligence for Business

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📱Everyday AI Concepts

🔗 7 Advanced Retriever Architecture

💡 The AI Universe

This visual guide clearly illustrates the different layers and concepts within Artificial Intelligence, Machine Learning, Deep Learning, and Generative AI.

🔅 Natural Language Processing (NLP) on Amazon Bedrock

📝 This course addresses natural language AI tasks using Bedrock's LLMs, Amazon Q capabilities, and SageMaker NLP models.

🌐 Author: Noah Gift
🔰 Level: Intermediate
⏰ Duration: 56m

📋 Topics: Amazon Bedrock, Large Language Models, Natural Language Processing

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📱Artificial intelligence
📱Natural Language Processing (NLP) on Amazon Bedrock

💡 13 Practical Steps For Creating an AI Agent

💡 9 AI Skills to Master in 2026

It’s the infrastructure behind how smart businesses run today.

The gap between users and experts is closing fast.
But the gap between curiosity and capability is getting wider.

The difference comes down to skill, not just tools.

These are the nine that matter most in 2026.
Each one compounds the rest and turns AI from novelty into leverage.

1⃣ Prompt Engineering to ask better questions and get sharper answers
🔢 AI Workflow Automation to connect apps and remove repetitive work
🔢 AI Agents to build systems that act without human input
🔢 Retrieval-Augmented Generation (RAG) to give models access to your own data
🔢 Fine-Tuning and Custom GPTs to train models for your goals and tone
🔢 Multimodal AI to mix text, image, and audio in one workflow
🔢 AI Video Generation to turn ideas into content without editing tools
🔢 AI Tool Stacking to link platforms into a single automated system
🔢 LLM Evaluation and Management to measure accuracy, cost, and performance

🔅 Advanced LLMs with Retrieval Augmented Generation (RAG): Practical Projects for AI Applications

📝 Discover the core concepts of successful AI applications using LLMs to achieve high levels of performance and accuracy.

🌐 Author: Guy Ernest
🔰 Level: Advanced
⏰ Duration: 1h 47m

📋 Topics: Retrieval-Augmented Generation, Large Language Models, Artificial Intelligence

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📱Artificial intelligence
📱Advanced LLMs with Retrieval Augmented Generation (RAG): Practical Projects for AI Applications

💡 20 Concepts In LLMs