Generative AI Foundations in Python : Discover Key Techniques and Navigate Modern Challenges in LLMs / Carlos Rodriguez and Samira Shaikh.

Format:

Book

Author/Creator:

Rodriguez, Carlos, 1945- author.

Shaikh, Samira, author.

Language:

English

Subjects (All):

Artificial intelligence.

Natural language processing (Computer science).

Python (Computer program language).

Machine learning.

Physical Description:

1 online resource (190 pages)

Edition:

First edition.

Place of Publication:

Birmingham, England : Packt Publishing, [2024]

Summary:

Begin your generative AI journey with Python as you explore large language models, understand responsible generative AI practices, and apply your knowledge to real-world applications through guided tutorials Key Features Gain expertise in prompt engineering, LLM fine-tuning, and domain adaptation Use transformers-based LLMs and diffusion models to implement AI applications Discover strategies to optimize model performance, address ethical considerations, and build trust in AI systems Purchase of the print or Kindle book includes a free PDF eBook Book Description The intricacies and breadth of generative AI (GenAI) and large language models can sometimes eclipse their practical application. It is pivotal to understand the foundational concepts needed to implement generative AI. This guide explains the core concepts behind -of-the-art generative models by combining theory and hands-on application. Generative AI Foundations in Python begins by laying a foundational understanding, presenting the fundamentals of generative LLMs and their historical evolution, while also setting the stage for deeper exploration. You'll also understand how to apply generative LLMs in real-world applications. The book cuts through the complexity and offers actionable guidance on deploying and fine-tuning pre-trained language models with Python. Later, you'll delve into topics such as task-specific fine-tuning, domain adaptation, prompt engineering, quantitative evaluation, and responsible AI, focusing on how to effectively and responsibly use generative LLMs. By the end of this book, you'll be well-versed in applying generative AI capabilities to real-world problems, confidently navigating its enormous potential ethically and responsibly. What you will learn Discover the fundamentals of GenAI and its foundations in NLP Dissect foundational generative architectures including GANs, transformers, and diffusion models Find out how to fine-tune LLMs for specific NLP tasks Understand transfer learning and fine-tuning to facilitate domain adaptation, including fields such as finance Explore prompt engineering, including in-context learning, templatization, and rationalization through chain-of-thought and RAG Implement responsible practices with generative LLMs to minimize bias, toxicity, and other harmful outputs Who this book is for This book is for developers, data scientists, and machine learning engineers embarking on projects driven by generative AI. A general understanding of machine learning and deep learning, as well as some proficiency with Python, is expected.

Contents:

Intro

Title Page

Copyright and Credits

Dedications

Foreword

Contributors

Table of Contents

Preface

Part 1: Foundations of Generative AI and the Evolution of Large Language Models

Chapter 1: Understanding Generative AI: An Introduction

Generative AI

Distinguishing generative AI from other AI models

Briefly surveying generative approaches

Clarifying misconceptions between discriminative and generative paradigms

Choosing the right paradigm

Looking back at the evolution of generative AI

Overview of traditional methods in NLP

Arrival and evolution of transformer-based models

Development and impact of GPT-4

Looking ahead at risks and implications

Introducing use cases of generative AI

The future of generative AI applications

Summary

References

Chapter 2: Surveying GenAI Types and Modes: An Overview of GANs, Diffusers, and Transformers

Understanding General Artificial Intelligence (GAI) Types - distinguishing features of GANs, diffusers, and transformers

Deconstructing GAI methods - exploring GANs, diffusers, and transformers

A closer look at GANs

A closer look at diffusion models

A closer look at generative transformers

Applying GAI models - image generation using GANs, diffusers, and transformers

Working with Jupyter Notebook and Google Colab

Stable diffusion transformer

Scoring with the CLIP model

Chapter 3: Tracing the Foundations of Natural Language Processing and the Impact of the Transformer

Early approaches in NLP

Advent of neural language models

Distributed representations

Transfer Learning

Advent of NNs in NLP

The emergence of the Transformer in advanced language models

Components of the transformer architecture

Sequence-to-sequence learning.

Evolving language models - the AR Transformer and its role in GenAI

Implementing the original Transformer

Data loading and preparation

Tokenization

Data tensorization

Dataset creation

Embeddings layer

Positional encoding

Multi-head self-attention

FFN

Encoder layer

Encoder

Decoder layer

Decoder

Complete transformer

Training function

Translation function

Main execution

Chapter 4: Applying Pretrained Generative Models: From Prototype to Production

Prototyping environments

Transitioning to production

Mapping features to production setup

Setting up a production-ready environment

Local development setup

Visual Studio Code

Project initialization

Docker setup

Requirements file

Application code

Creating a code repository

CI/CD setup

Model selection - choosing the right pretrained generative model

Meeting project objectives

Model size and computational complexity

Benchmarking

Updating the prototyping environment

GPU configuration

Loading pretrained models with LangChain

Setting up testing data

Quantitative metrics evaluation

Alignment with CLIP

Interpreting outcomes

Responsible AI considerations

Addressing and mitigating biases

Transparency and explainability

Final deployment

Testing and monitoring

Maintenance and reliability

Part 2: Practical Applications of Generative AI

Chapter 5: Fine-Tuning Generative Models for Specific Tasks

Foundation and relevance - an introduction to fine-tuning

PEFT

LoRA

AdaLoRA

In-context learning

Fine-tuning versus in-context learning

Practice project: Fine-tuning for Q&amp

A using PEFT

Background regarding question-answering fine-tuning

Implementation in Python

Evaluation of results

References.

Chapter 6: Understanding Domain Adaptation for Large Language Models

Demystifying domain adaptation - understanding its history and importance

Practice project: Transfer learning for the finance domain

Training methodologies for financial domain adaptation

Evaluation and outcome analysis - the ROUGE metric

Chapter 7: Mastering the Fundamentals of Prompt Engineering

The shift to prompt-based approaches

Basic prompting - guiding principles, types, and structures

Guiding principles for model interaction

Prompt elements and structure

Elevating prompts - iteration and influencing model behaviors

LLMs respond to emotional cues

Effect of personas

Situational prompting or role-play

Advanced prompting in action - few-shot learning and prompt chaining

Practice project: Implementing RAG with LlamaIndex using Python

Chapter 8: Addressing Ethical Considerations and Charting a Path Toward Trustworthy Generative AI

Ethical norms and values in the context of generative AI

Investigating and minimizing bias in generative LLMs and generative image models

Constrained generation and eliciting trustworthy outcomes

Constrained generation with fine-tuning

Constrained generation through prompt engineering

Understanding jailbreaking and harmful behaviors

Practice project: Minimizing harmful behaviors with filtering

Index

About Packt

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Notes:

Description based upon print version of record.

GPU configuration

Includes bibliographical references and index.

Description based on publisher supplied metadata and other sources.

Description based on print version record.

Other Format:

Print version: Rodriguez, Carlos Generative AI Foundations in Python

ISBN:

9781835464915

OCLC:

1443939992