Getting started with Generative AI
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).
- 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.
- Aditional 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: Juputer 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 suuport.
- Using Google Colab or Kaggle service.
- Conclusion
- Chapter 2: Deep dive into the theories of Generative AI
- Artifical 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
- How LLM works internally?
- Chapter 3: RAG, enrich LLM models with private datasets
- What is RAG?
- RAG vs Fine tuning LLM models
- Key Concepts of RAG
- Embeddings
- Vector database
- Semantic Search
- How Semantic Search is differnt 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 a LLM
- Prerequisites
- Part 1. Analyze busines requirenments, choosing a base model and environment setup
- Part 2. Exploring the training dataset
- Part 3. Dataset pre-prosessing 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 with LLaVA-v1.6
- Possibilities and Functionalities of LLaVA-v1.6
- LLaVa architecture and how it works?
- 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 with LLaVA-v1.6
- Chapter 7: Developing and utilizing AI agents
- Preface
The Leanpub 60 Day 100% Happiness Guarantee
Within 60 days of purchase you can get a 100% refund on any Leanpub purchase, in two clicks.
Now, this is technically risky for us, since you'll have the book or course files either way. But we're so confident in our products and services, and in our authors and readers, that we're happy to offer a full money back guarantee for everything we sell.
You can only find out how good something is by trying it, and because of our 100% money back guarantee there's literally no risk to do so!
So, there's no reason not to click the Add to Cart button, is there?
See full terms...
Earn $8 on a $10 Purchase, and $16 on a $20 Purchase
We pay 80% royalties on purchases of $7.99 or more, and 80% royalties minus a 50 cent flat fee on purchases between $0.99 and $7.98. You earn $8 on a $10 sale, and $16 on a $20 sale. So, if we sell 5000 non-refunded copies of your book for $20, you'll earn $80,000.
(Yes, some authors have already earned much more than that on Leanpub.)
In fact, authors have earnedover $13 millionwriting, publishing and selling on Leanpub.
Learn more about writing on Leanpub
Free Updates. DRM Free.
If you buy a Leanpub book, you get free updates for as long as the author updates the book! Many authors use Leanpub to publish their books in-progress, while they are writing them. All readers get free updates, regardless of when they bought the book or how much they paid (including free).
Most Leanpub books are available in PDF (for computers) and EPUB (for phones, tablets and Kindle). The formats that a book includes are shown at the top right corner of this page.
Finally, Leanpub books don't have any DRM copy-protection nonsense, so you can easily read them on any supported device.
Learn more about Leanpub's ebook formats and where to read them
