🔗 AI for Everyone: Master the Basics

Unlock the essentials of Artificial Intelligence (AI) with this free IBM course. Explore applications and key concepts like machine learning, deep learning, and neural networks.

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🔰 Neural networks will translate books, articles, diagrams and graphs, preserving their presentable appearance

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🔗 Links: https://github.com/Byaidu/PDFMathTranslate

🔅 LLM Foundations: Building Effective Applications for Enterprises

🌐 Author: Kumaran Ponnambalam
🔰 Level: Advanced

⏰ Duration: 1h 43m

🌀 Explore design considerations and best practices for building generative AI-powered applications at enterprise scale.

📗 Topics: Large Language Models, Artificial Intelligence, Enterprise Software

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📂 Full denoscription

As generative AI models have become increasingly popular, enterprises have started to build end-to-end applications to integrate their existing workflows with generative AI. In this course, instructor Kumaran Ponnambalam shows you how to get up and running with integration, performance management, trust, and monitoring to deliver effective and trustworthy generative AI applications at scale.Explore some of the unique characteristics and use cases for generative AI-powered applications in an enterprise setting, including available options, selection criteria, and key deployment considerations for generative AI models. Kumaran covers the basics of evaluating and fine-tuning models as well as patterns and best practices for core application design. By the end of this course, youll also be equipped with new skills to manage application performance, maintain safety and trust, and navigate some of the most important ethical and legal challenges of AI.

📱Artificial intelligence
📱LLM Foundations: Building Effective Applications for Enterprises

🔗 Master the Top 10 Machine Learning Topics

🔗 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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📱Artificial intelligence
📱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