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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:

  1. Clear and concise explanations: The book breaks down complex AI concepts into easily understandable steps, making it accessible to beginners.
  2. Step-by-step instructions: Each chapter guides you through building a specific AI application, from setting up your environment to deploying your final product.
  3. Real-world examples: You'll learn by applying AI techniques to solve practical problems, gaining valuable hands-on experience.
  4. 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.
  5. 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:

  1. Understand the fundamentals of Generative AI and Large Language Models (LLMs).
  2. Set up and use local LLMs inference efficiently for AI development.
  3. Enrich RAG (Retrieval Augmented Generation) LLM models with your own datasets, such as PDFs and documents.
  4. Interact between LLM models and SQL databases.
  5. Build and train your own AI models.
  6. Utilize AI agents to perform tasks without human intervention.
  7. Deploy your AI applications in real-world scenarios.
  8. 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

Shamim Bhuiyan
Shamim Bhuiyan

Shamim Bhuiyan, currently working as an Enterprise architect, where he is responsible for designing and building out high scalable, high load middleware solutions. He has been in the IT field for over 16 years and specialized in Java and Data science. Also, he is a former SOA solution designer, speaker, and Big data evangelist. Actively participates in the development and designing high-performance software for IT, telecommunication and banking industry. In spare times, he usually writes blogs #frommyworkshop and shares ideas with others. 

Timur Isachenko
Timur Isachenko

Timur Isachenko is an accomplished Technical Lead and Solution Architect, known for his expertise in backend development and microservices architecture. With over 14 years in the IT industry, he has a rich history of developing and implementing high-load systems across the finance, healthcare, and online banking sectors. Timur holds a Specialist Degree in Computer Science and Applied Mathematics from Kuban State University.

He has led numerous projects, including the development of educational platforms, billing systems for betting and gambling platforms, and integration services for major enterprises. Timur is also a co-author of “High Performance In-Memory Data Grid with Ignite” and has been a speaker at various conferences.

In his personal life, Timur is a dedicated father to three daughters and an avid runner who once ran every day for an entire year.

Bundles that include this book

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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
      • 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
    • 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

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