Books
- Bouchard & Peters (2024). Building LLMs for Production: Enhancing LLM Abilities and Reliability with Prompting, Fine-Tuning, and RAG.
- Ananthaswamy (2024). Why Machines Learn: The Elegant Math Behind Modern AI.
- Ascoli (2022). Artificial Intelligence and Deep Learning with Python: Every Line of Code Explained.
- Kneusel (2022). Math for Deep Learning: What You Need to Know to Understand Neural Networks.
- Tunstall, von Werra & Wolf (2022). Natural Language Processing with Transformers.
- Glassner (2021). Deep Learning: A Visual Approach.
- Kneusel (2021). Practical Deep Learning: A Python-Based Introduction.
- Serrano (2021). Machine Learning.
- Howard & Gugger (2020). Deep Learning for Coders with fastai and PyTorch.
- Rao & McMahan (2019). Natural Language Processing with PyTorch.
- Bird, Klein & Loper (2009). Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit.
- Manning & Schutze (1999). Foundations of Statistical Natural Language Processing.