Description
Mastering Transformers. Transformer-based language models such as BERT, T5, GPT, DALL-E, and ChatGPT have dominated natural language processing studies and become a new paradigm. Thanks to their fast and accurate fine-tuning capabilities, these models have been able to outperform traditional machine learning-based approaches on many challenging natural language understanding problems.
In addition to natural language processing, the field of multimodal learning and generative artificial intelligence has grown rapidly and has yielded promising results. This book helps the reader understand and implement multimodal solutions, including text-to-image conversion. Transformer-based machine vision solutions are also described in detail. The book begins with an understanding of various transformer models and then covers training autoregressive language models such as GPT and XLNet. It also examines model performance improvement and training tracking using the TensorBoard tool. The final chapters focus on using vision transformers to solve machine vision problems and modeling time series data for prediction. Finally, the reader gains a comprehensive understanding of transformer models and how they can be applied to overcome challenges in natural language processing and machine vision.
Book Features
- Focus on solving simple to complex natural language processing problems with Python
- Discover how to solve classification and regression problems with traditional natural language processing approaches
- Training language models and exploring how to fine-tune them for various tasks
- Understanding how transformers are used in generative AI and machine vision
- Building Transformer-Based Natural Language Processing Programs with Python’s Transformers Library
- Focus on language generation such as machine translation and conversational AI in different languages
- Speeding up transformer model inference to reduce latency
Book specifications
- Publisher: Packt
- Lecturer/Author: SAVAŞ YILDIRIM
- Number of pages: 457
- Number of chapters: 18
- Format: PDF
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