Generative AI Essentials: Four must-read guides

Unlock the transformative world of Generative AI with our exclusive bundle, "Generative AI Essentials: Four Must-Read Guides." Whether you're an AI enthusiast, a seasoned professional, or just beginning your journey, this collection is designed to equip you with comprehensive knowledge and practical insights into the cutting-edge developments of Generative AI and Large Language Models (LLMs).

What's Inside?

1. Generative AI with Large Language Models

Dive deep into the lifecycle of generative AI projects with detailed course notes from the acclaimed "Generative AI with LLM" course on Coursera by AWS and deeplearning.ai. This guide covers:

• LLM Pre-Training: Understand what LLMs are, how Transformers revolutionised text generation, and the challenges of pre-training.
• LLM Fine-Tuning: Explore instruction fine-tuning, avoid catastrophic forgetting, and learn evaluation techniques.
• RLHF & Application: Align models with human values using Reinforcement Learning from Human Feedback, deploy LLMs for inference, and grasp responsible AI practices.

Ideal for: Anyone seeking a foundational understanding of LLMs and their applications.

2. Generative AI Terminology: An Evolving Taxonomy

Navigate the complex landscape of Generative AI with this non-technical, evolving taxonomy that categorizes over 100 key terms into 12 intuitive groups:

• Model Categories: Foundation models, LLMs, VLMs, etc.
• Common Terms: Prompts, tokens, hallucinations, etc.
• Lifecycle Stages: Pre-training, fine-tuning, RLHF, etc.
• And More: Evaluation metrics, architectures, security, deployment strategies, and a list of LLMOps providers.

Ideal for: Product managers, leaders, ML engineers, and enthusiasts looking to solidify their grasp of Generative AI terminology.

3. Retrieval Augmented Generation: A Simple Introduction

Learn how to enhance LLMs like GPT-4 by connecting them to external knowledge bases using Retrieval Augmented Generation (RAG). This guide answers critical questions:

• What is RAG and how does it help?
• Popular RAG use cases and architectures.
• Understanding embeddings, vector stores, and retrieval strategies.
• Evaluating RAG outputs and comparing RAG vs. fine-tuning.
• Insights into multimodal RAG and the evolving LLMOps stack.

Includes practical examples using LangChain, LlamaIndex, HuggingFace, and OpenAI.

Ideal for: Those interested in building advanced AI applications powered by grounding the LLMs in data

4. A Taxonomy of Retrieval Augmented Generation

Delve into a comprehensive taxonomy of over 200 key RAG terms, organized into 8 essential categories:

• RAG Basics and Core Components
• Evaluation Metrics and Pipeline Design
• Operations Stack and Emerging Patterns
• Technology Providers and Applied RAG

This guide demystifies the RAG ecosystem, making it accessible without unnecessary complexity.

Ideal for: ML engineers, data scientists, product managers, and AI enthusiasts aiming to deepen their understanding of RAG systems.

Who Will Benefit?

• Product Managers & Leaders: Harness Generative AI and RAG for innovative solutions.
• ML Engineers & Data Scientists: Expand expertise in LLMs and RAG technologies.
• AI Enthusiasts & Academics: Stay updated with the latest advancements and applications.
• Business Professionals: Leverage AI insights for strategic decision-making and competitive advantage.

From foundational concepts to advanced applications, gain a 360-degree understanding of Generative AI. Embark on a transformative journey into the world of Generative AI. Equip yourself with the knowledge and tools to innovate and lead in this exciting field.

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