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Mastering Transformers: The Journey from BERT to Large Language Models and Stable Diffusion. Savas Yildirim, Meysam Asgari-Chenaghlu

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ISBN: 9781837633784 | 525 pages | 14 Mb

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

• Savas Yildirim, Meysam Asgari-Chenaghlu
  

• Page: 525
  

• Format: pdf, ePub, fb2, mobi
  

• ISBN: 9781837633784
  

• Publisher: Packt Publishing
  

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

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Address NLP tasks as well as multi-modal tasks including both NLP and CV through the utilization of modern transformer architecture. Understand the Complexity of Deep Learning Architectures and Transformers Architecture Learn how to create effective solutions to industrial NLP and CV problems Learn about the challenges in the preparation process, such as problem and language-specific data sets transformation The Transformer-based language models such as BERT, T5, GPT, DALL-E, ChatGPT have dominated natural language processing studies and become a new paradigm. Understand and be able to implement multimodal solutions including text-to-image. Computer vision solutions that are based on Transformers are also explained in the book. Thanks to their accurate and fast fine-tuning capabilities, Transformer-based language models outperformed traditional machine learning-based approaches for many challenging natural language understanding (NLU) problems. Apart from NLP, recently a fast-growing area in multimodal learning and generative AI has been established which shows promising results. Dalle and Stable diffusions are examples of it. Developers working with The Transformers architecture will be able to put their knowledge to work with this practical guide to NLP. The book provides a hands-on approach to implementation and associated methodologies in the field of NLP that will have you up-and-running, and productive in no time. Also, developers that want to learn more about multimodal models and generative AI in the field of computer vision can use this book as a source. How NLP technologies have evolved over the past years How to solve simple/complex NLP problems with Python programming language How to solve classification/regression problems with traditional NLP approaches Training a language model and further exploring how to fine-tune the models to the downstream tasks How to use Transformers for generative AI and computer vision tasks How to build Transformers-based NLP applications with the Python Transformers library How to build language generation such as Machine Translation, Conversational AI in any language How to speed up transformer model inference to reduce latency The book is for deep learning researchers, hands-on practitioners, ML/NLP researchers, educators and their students who have a good command of programming subjects, have knowledge in the field of machine learning and artificial intelligence, and want to develop applications in the field of cutting-edge natural language processing as well as multimodal tasks. The readers will have to know at least python or any programming language, know machine learning literature, have some basic understanding of computer science, as this book is going to cover the practical aspects of natural language processing and multimodal deep learning. From bag-of-words to the Transformers A hands-on Introduction to the Subject Autoencoding Language Models Autoregressive Language Models Fine-tuning Language Model for Text Classification Fine-tuning Language Model for Token Classification Text Representation Boosting your model performance Parameter Efficient Fine-tuning Zero-shot and Few-shot learning in NLP Explainable AI (XAI) for NLP Working with Efficient Transformers Cross-Lingual Language Modeling Serving Transformer Models Model Tracking and Monitoring Vision Transformers Tabular Transformers Multi-model Transformers Graph Transformers

[R] Timeline of recent Large Language Models
OA's dataset from yesterday will definitely be added in the next update, I just have to find a way how to deal with gradual epoch updates. (Just 
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