Getting started with Generative AI (10 copies of a package for team. )
Getting started with Generative AI
Learn how to build your own AI application step-by-step. A hands-on guide to AI development with local LLM
About the Book
This book is a practical guide for anyone interested in diving into the world of Generative AI development, regardless of their prior programming experience.
Here's what you can expect:
- Clear and concise explanations: The book breaks down complex AI concepts into easily understandable steps, making it accessible to beginners.
- Step-by-step instructions: Each chapter guides you through building a specific AI application, from setting up your environment to deploying your final product.
- Real-world examples: You'll learn by applying AI techniques to solve practical problems, gaining valuable hands-on experience.
- Popular tools and libraries: The book focuses on widely used tools and libraries like Langchain, Vanna, TensorFlow, and PyTorch equipping you with in-demand skills.
- Project-based learning: You'll work on engaging projects that range from simple image recognition to more advanced natural language processing tasks.
By the end of this book, you'll be able to:
- Understand the fundamentals of Generative AI and Large Language Models (LLMs).
- Set up and use local LLMs inference efficiently for AI development.
- Enrich RAG (Retrieval Augmented Generation) LLM models with your own datasets, such as PDFs and documents.
- Interact between LLM models and SQL databases.
- Build and train your own AI models.
- Utilize AI agents to perform tasks without human intervention.
- Deploy your AI applications in real-world scenarios.
- Gain confidence in your ability to develop innovative AI solutions.
Whether you're a student, a professional looking to upskill, or simply someone curious about AI, this book provides a comprehensive and practical roadmap to becoming an AI developer.
About the Authors
Bundles that include this book
Table of Contents
- Preface
- What this book covers
- Code Samples
- Readership
- Conventions
- Reader feedback
- About the authors
- Acknowledgments
- Chapter 1: Getting started with Local LLM
- Tools and frameworks used in this book
- Installing and setting up the local LLM inference
- Useful commands and interfaces
- Additional setup
- Uninstall LLM inference
- Installing a graphical user interface (GUI) client to work with local LLM
- Configure a Python virtual environment for AI development
- Install Python 3
- Install Python package manager pip3
- Installing and configuring Miniconda
- Install IDE: Jupyter lab and notebook
- Install and configure SQLLite database
- Additional setups
- Develop your first application with local LLM
- Troubleshooting
- Hardware acceleration
- Using a Workstation with GPU
- Enabling AVX/AVX2 for CPU acceleration
- Using 3rd party ASIC platform or VPS with GPU support
- Using Google Colab or Kaggle service
- Conclusion
- Chapter 2: Deep dive into the theories of Generative AI
- Artificial Intelligence (AI)
- Machine Learning (ML)
- Deep Learning (DL)
- Natural Language Processing (NLP)
- Transformer
- Self-Attention mechanism
- Encoder-Decoder architecture
- Generative AI
- What is Generative AI and what is not?
- Categories of Generative AI
- Large Language Model
- How LLM works internally?
- Tokenization
- Vector
- Embedding
- Transformers
- Training LLM
- Pre-training
- Fine-tuning
- How LLM works internally?
- RAG
- AI Agents
- Prompt engineering
- Resources
- Conclusion
- Chapter 3: RAG, enrich LLM models with private datasets
- RAG vs fine-tuning LLMs
- Key concepts of RAG
- Embeddings
- Vector database
- Semantic Search
- How semantic search is different from full text search?
- Real world use cases of using RAG
- Implementing RAG in a private company
- Step-by-Step Example: Loading, retrieving, and processing custom documents with LLM
- Conclusion
- Chapter 4: Text-to-SQL, enhance your LLM responses by integrating data from the Database
- What is Text-to-SQL?
- Challenges of Text-to-SQL
- LLM for Text-to-SQL
- System design patterns of using Text-to-SQL with examples
- Design pattern 1. Generating and executing SQL queries
- Design pattern 2. Using Agent’s for error handling and ensure correctness
- Design pattern 3. Text-To-SQL with RAG
- Conclusion
- Chapter 5: Fine-tuning LLMs
- Steps for Fine-tuning a pre-trained model
- Fine-tuning technics
- Full Fine-Tuning
- Parameter-Efficient Fine-Tuning (PEFT)
- LoRA (Low-Rank Adaptation)
- Quantized LoRA (QLoRA)
- Knowledge Distillation (KD)
- Popular frameworks used for fine-tuning LLMs
- Step-by-step example of fine-tuning an LLM
- Prerequisites
- Part 1. Analyze business requirements, choosing a base model and environment setup
- Part 2. Exploring the training dataset
- Part 3. Dataset pre-processing and adapter configuration
- Part 4. Train the model
- Part 5. Evaluate the model
- Part 6. Save & deploy the final model
- Conclusion
- Chapter 6: Image processing & generating with LLM
- Image visioning
- Possibilities and Functionalities of LLaVA-v1.6
- LLaVa architecture
- Step-by-Step Example: Utilizing LLaVA-v1.6 for Image Visioning
- Incorporating LLaVA into your application for image processing
- Image processing
- Tips for Better Results
- References
- Conclusion
- Image visioning
- Chapter 7: Developing and utilizing AI agents
- The future of AI agents
- Difference between AI Agents and AI Tools
- Use cases of AI agents in Generative AI
- Use cases from a developer’s perspective
- Use cases from a product manager’s perspective
- Classification of AI Agents in Generative AI
- AI agents architecture
- Frameworks for developing AI Agents
- Developing a practical AI Agents: a step-by-step Guide
- Conclusion
- Final words
- Preface
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