Readings in Machine Learning Engineering
One of my goals this year, and every year, is to become a better machine learning engineer. Right now, I'm focusing on basics and best practices — reviewing the fundamentals, filling in knowledge gaps, and learning the recommended way to do things. Here is a collection of some of my learning resources.
Books
- Natural Language Processing in Action
- Natural Language Processing with Transformers
- Speech and Language Processing
- Designing Machine Learning Systems
- Machine Learning Design Patterns
- AI Engineering
- Deep Learning with PyTorch
- AI and ML for Coders in PyTorch
- Hands-On Machine Learning with Scikit-Learn and PyTorch
- Hands-On Large Language Models
- Hands-On Generative AI with Transformers and Diffusion Models
- The Design of Everyday Things
- Thinking in Systems
- A Philosophy of Software Design
- Ethics and Data Science
- Designing Data-Intensive Applications