book

Generative AI Foundations in Python

Name: Generative AI Foundations in Python
Author: Carlos Rodriguez
ISBN: 9781835460825

by Carlos Rodriguez

July 2024

Beginner to intermediate

190 pages

5h 30m

English

Packt Publishing

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Generative AI Foundations in Python
ForewordContributorsAbout the authorAbout the reviewers
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesConventions usedGet in touchShare Your ThoughtsDownload a free PDF copy of this book
Part 1: Foundations of Generative AI and the Evolution of Large Language Models
Chapter 1: Understanding Generative AI: An Introduction
Generative AIDistinguishing generative AI from other AI modelsBriefly surveying generative approachesClarifying misconceptions between discriminative and generative paradigmsChoosing the right paradigmLooking back at the evolution of generative AIOverview of traditional methods in NLPArrival and evolution of transformer-based modelsDevelopment and impact of GPT-4Looking ahead at risks and implicationsIntroducing use cases of generative AIThe future of generative AI applicationsSummaryReferences
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 transformersDeconstructing GAI methods – exploring GANs, diffusers, and transformersA closer look at GANsA closer look at diffusion modelsA closer look at generative transformersApplying GAI models – image generation using GANs, diffusers, and transformersWorking with Jupyter Notebook and Google ColabStable diffusion transformerScoring with the CLIP modelSummaryReferences
Chapter 3: Tracing the Foundations of Natural Language Processing and the Impact of the Transformer
Early approaches in NLPAdvent of neural language modelsDistributed representationsTransfer LearningAdvent of NNs in NLPThe emergence of the Transformer in advanced language modelsComponents of the transformer architectureSequence-to-sequence learningEvolving language models – the AR Transformer and its role in GenAIImplementing the original TransformerData loading and preparationTokenizationData tensorizationDataset creationEmbeddings layerPositional encodingMulti-head self-attentionFFNEncoder layerEncoderDecoder layerDecoderComplete transformerTraining functionTranslation functionMain executionSummaryReferences
Chapter 4: Applying Pretrained Generative Models: From Prototype to Production
Prototyping environmentsTransitioning to productionMapping features to production setupSetting up a production-ready environmentLocal development setupVisual Studio CodeProject initializationDocker setupRequirements fileApplication codeCreating a code repositoryCI/CD setupModel selection – choosing the right pretrained generative modelMeeting project objectivesModel size and computational complexityBenchmarkingUpdating the prototyping environmentGPU configurationLoading pretrained models with LangChainSetting up testing dataQuantitative metrics evaluationAlignment with CLIPInterpreting outcomesResponsible AI considerationsAddressing and mitigating biasesTransparency and explainabilityFinal deploymentTesting and monitoringMaintenance and reliabilitySummary
Part 2: Practical Applications of Generative AI
Chapter 5: Fine-Tuning Generative Models for Specific Tasks
Foundation and relevance – an introduction to fine-tuningPEFTLoRAAdaLoRAIn-context learningFine-tuning versus in-context learningPractice project: Fine-tuning for Q&A using PEFTBackground regarding question-answering fine-tuningImplementation in PythonEvaluation of resultsSummaryReferences
Chapter 6: Understanding Domain Adaptation for Large Language Models
Demystifying domain adaptation – understanding its history and importancePractice project: Transfer learning for the finance domainTraining methodologies for financial domain adaptationEvaluation and outcome analysis – the ROUGE metricSummaryReferences

Chapter 7: Mastering the Fundamentals of Prompt Engineering
The shift to prompt-based approachesBasic prompting – guiding principles, types, and structuresGuiding principles for model interactionPrompt elements and structureElevating prompts – iteration and influencing model behaviorsLLMs respond to emotional cuesEffect of personasSituational prompting or role-playAdvanced prompting in action – few-shot learning and prompt chainingPractice project: Implementing RAG with LlamaIndex using PythonSummaryReferences
Chapter 8: Addressing Ethical Considerations and Charting a Path Toward Trustworthy Generative AI
Ethical norms and values in the context of generative AIInvestigating and minimizing bias in generative LLMs and generative image modelsConstrained generation and eliciting trustworthy outcomesConstrained generation with fine-tuningConstrained generation through prompt engineeringUnderstanding jailbreaking and harmful behaviorsPractice project: Minimizing harmful behaviors with filteringSummaryReferences
Index
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Content preview from Generative AI Foundations in Python

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

Adaptive Low-Rank Adaptation (AdaLoRA) 107, 108

AI model interactions 138

effect of personas 139

LLMs 138

role play 139, 140

situational prompting 139, 140

AI models

versus generative AI 4

algorithmic/model bias 151

application code 79, 80

artificial intelligence (AI) 3

AR Transformer

role, in GenAI 57-59

autoregressive (AR) 39

autoregressive modeling 26

autoregressive transformers 5

back translation 122

base64 153

benchmarking 87

bias

investigating and minimizing, in generative image models 151, 152

investigating and minimizing, in generative LLMs 151, 152

Bidirectional Encoder ...

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Generative AI Foundations in Python

by Carlos Rodriguez

Index

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