References

Books and Textbooks #

1. Russell, S., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
2. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. https://www.deeplearningbook.org/
3. Jurafsky, D., & Martin, J. H. (2023). Speech and Language Processing (3rd ed. draft). https://web.stanford.edu/~jurafsky/slp3/
4. Murphy, K. P. (2022). Probabilistic Machine Learning: An Introduction. MIT Press.
5. Géron, A. (2022). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (3rd ed.). O’Reilly Media.
6. Szeliski, R. (2022). Computer Vision: Algorithms and Applications (2nd ed.). Springer.

Research Papers #

1. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep Learning. Nature, 521(7553), 436-444.
2. Vaswani, A., et al. (2017). Attention Is All You Need. NeurIPS.
3. Brown, T., et al. (2020). Language Models are Few-Shot Learners. NeurIPS.
4. He, K., et al. (2016). Deep Residual Learning for Image Recognition. CVPR.
5. Bommasani, R., et al. (2021). On the Opportunities and Risks of Foundation Models. arXiv:2108.07258.
6. Ouyang, L., et al. (2022). Training Language Models to Follow Instructions with Human Feedback. NeurIPS.