Mastering Transformers: The Journey from BERT to Large Language Models and Stable Diffusion

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dc.contributor.authorYıldırım, Savaş
dc.contributor.authorAsgari-Chenaghlu, Meysam
dc.date.accessioned2026-04-04T18:48:45Z
dc.date.available2026-04-04T18:48:45Z
dc.date.issued2024
dc.description.abstractTransformer-based language models such as BERT, T5, GPT, DALL-E, and ChatGPT have dominated NLP studies and become a new paradigm. Thanks to their accurate and fast fine-tuning capabilities, transformer-based language models have been able to outperform traditional machine learning-based approaches for many challenging natural language understanding (NLU) problems. Aside from NLP, a fast-growing area in multimodal learning and generative AI has recently been established, showing promising results. Mastering Transformers will help you understand and implement multimodal solutions, including text-to-image. Computer vision solutions that are based on transformers are also explained in the book. You’ll get started by understanding various transformer models before learning how to train different autoregressive language models such as GPT and XLNet. The book will also get you up to speed with boosting model performance, as well as tracking model training using the TensorBoard toolkit. In the later chapters, you’ll focus on using vision transformers to solve computer vision problems. Finally, you’ll discover how to harness the power of transformers to model time series data and for predicting. By the end of this transformers book, you’ll have an understanding of transformer models and how to use them to solve challenges in NLP and CV. © 2024 Packt Publishing.
dc.identifier.endpage439
dc.identifier.isbn978-183763150-6
dc.identifier.isbn978-183763378-4
dc.identifier.scopus2-s2.0-105024291059
dc.identifier.scopusqualityN/A
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/11411/10340
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherDe Gruyter
dc.relation.ispartofMastering Transformers: The Journey from BERT to Large Language Models and Stable Diffusion
dc.relation.publicationcategoryKitap - Uluslararası
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20260402
dc.subjectComputational Linguistics
dc.subjectComputer Vision
dc.subjectLearning Systems
dc.subjectMachine Learning
dc.subjectNatural Language Processing Systems
dc.subjectPersonnel Training
dc.subjectPower Transformers
dc.subjectAuto-Regressive
dc.subjectFine Tuning
dc.subjectLanguage Model
dc.subjectLearning-Based Approach
dc.subjectMachine-Learning
dc.subjectMulti-Modal
dc.subjectMulti-Modal Learning
dc.subjectNatural Language Understanding
dc.subjectTransformer Modeling
dc.subjectTuning Capability
dc.subjectDiffusion
dc.titleMastering Transformers: The Journey from BERT to Large Language Models and Stable Diffusion
dc.typeBook

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