You're reading from Mastering Transformers The Journey from BERT to Large Language Models and Stable Diffusion

Product type Paperback

Published in Jun 2024

Publisher Packt

ISBN-13 9781837633784

Length 462 pages

Edition 2nd Edition

Languages

Python

Tools

BERT

Concepts

GPT/LLMs

Authors (2):

Savaş Yıldırım

Meysam Asgari- Chenaghlu

View More author details

Table of Contents (25) Chapters

Preface

1. Part 1: Recent Developments in the Field, Installations, and Hello World Applications

2. Chapter 1: From Bag-of-Words to the Transformers FREE CHAPTER

3. Chapter 2: A Hands-On Introduction to the Subject

4. Part 2: Transformer Models: From Autoencoders to Autoregressive Models

5. Chapter 3: Autoencoding Language Models

6. Chapter 4: From Generative Models to Large Language Models

7. Chapter 5: Fine-Tuning Language Models for Text Classification

8. Chapter 6: Fine-Tuning Language Models for Token Classification

9. Chapter 7: Text Representation

10. Chapter 8: Boosting Model Performance

11. Chapter 9: Parameter Efficient Fine-Tuning

12. Part 3: Advanced Topics

13. Chapter 10: Large Language Models

14. Chapter 11: Explainable AI (XAI) in NLP

15. Chapter 12: Working with Efficient Transformers

16. Chapter 13: Cross-Lingual and Multilingual Language Modeling

17. Chapter 14: Serving Transformer Models

18. Chapter 15: Model Tracking and Monitoring

19. Part 4: Transformers beyond NLP

20. Chapter 16: Vision Transformers

21. Chapter 17: Multimodal Generative Transformers

22. Chapter 18: Revisiting Transformers Architecture for Time Series

23. Index

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24. Other Books You May Enjoy

References

Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.
Bahdanau, D., Cho, K., and Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473.
Pennington, J., Socher, R., and Manning, C. D. (2014, October). GloVe: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532-1543).
Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
Bengio, Y., Simard, P, and Frasconi, P. (1994). Learning long-term dependencies with gradient descent is difficult. IEEE transactions on neural networks, 5(2), 157-166.
Cho, K., et al. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078.
Kim, Y. (2014). Convolutional neural networks for sentence classification. CoRR abs/1408.5882 (2014). arXiv preprint arXiv:1408.5882.
Vaswani, A., et al. (2017). Attention is all you need. arXiv preprint arXiv:1706.03762.
Devlin, J., Chang, M. W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of deep bidirectional Transformers for language understanding. arXiv preprint arXiv:1810.04805.
Alammar (2022). The Illustrated stable diffusion. https://jalammar.github.io/illustrated-stable-diffusion/
Zhong, Y., et al. (2022). Regionclip: Region-based language-image pretraining. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 16793-16803).
Dosovitskiy, A., et al. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929.
Radford, A., et al. (2021, July). Learning transferable visual models from natural language supervision. In International Conference on Machine Learning (pp. 8748-8763). PMLR.

You're reading from Mastering Transformers The Journey from BERT to Large Language Models and Stable Diffusion

Table of Contents (25) Chapters

References

Authors (2)

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