Buchumschlag
Generative AI Foundations in Python: Discover Key Techniques and Navigate Modern Challenges in LLMs
Gespeichert in:
Bibliographische Detailangaben
Beteilige Person: Rodriguez, Carlos (VerfasserIn)
Weitere beteiligte Personen: Shaikh, Samira (MitwirkendeR)
Format: Elektronisch E-Book
Sprache:Englisch
Veröffentlicht: Birmingham Packt Publishing, Limited 2024
Ausgabe:1st ed.
Links:https://ebookcentral.proquest.com/lib/hm-bib/detail.action?docID=31516396
https://ebookcentral.proquest.com/lib/kxp/detail.action?docID=31516396
Abstract: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 -- Summary -- References -- 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.
Beschreibung:Description based on publisher supplied metadata and other sources
Umfang:1 online resource (190 pages)
ISBN:9781835464915

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