Curious thoughts
- "Who owns the code?": "This shift raises an important question: who is accountable when something goes wrong – Copilot, the reviewer, or someone else?Rajesh Jethwa, CTO of software engineering consultancy Digiterre, describes this issue as a “minefield”, because there are a number of entities involved in creating the code. First, there are the providers of the models themselves, such as OpenAI or Anthropic. It is currently unclear whether these providers own the code generated by their models. Second, there are the authors of the code used to train the model. There are still questions around whether they have any claim to ownership of the resulting code, given the provenance of the training data. Third, there are employees and the organizations they work for. Typically, when an employee creates code as part of their job, the organization owns that code. However, it remains uncertain whether the organization or the individual employee should bear responsibility for the code that is produced with the help of a coding assistance."
Contrarian/hype-debunking
General
Fuzzy Logic
Expert Systems
Large Language Models
- "On the Biology of a Large Language Model": "We investigate the internal mechanisms used by Claude 3.5 Haiku — Anthropic's lightweight production model — in a variety of contexts, using our circuit tracing methodology."
Natural Language Processing
Machine Learning
- A Brief Introduction to Machine Learning for Engineers - Osvaldo Simeone (PDF)
- A Brief Introduction to Neural Networks
- A Comprehensive Guide to Machine Learning - Soroush Nasiriany, Garrett Thomas, William Wang, Alex Yang (PDF)
- A Course in Machine Learning (PDF)
- A First Encounter with Machine Learning - Max Welling (PDF) (:card_file_box: archived)
- A Selective Overview of Deep Learning - Fan, Ma, and Zhong (PDF)
- Algorithms for Reinforcement Learning - Csaba Szepesvári (PDF)
- An Introduction to Statistical Learning - Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani (PDF)
- Approaching Almost Any Machine Learning Problem - Abhishek Thakur (PDF)
- Bayesian Reasoning and Machine Learning
- Deep Learning - Ian Goodfellow, Yoshua Bengio and Aaron Courville
- Deep Learning for Coders with Fastai and PyTorch - Jeremy Howard, Sylvain Gugger (Jupyter Notebooks)
- Deep Learning with PyTorch - Eli Stevens, Luca Antiga, Thomas Viehmann (PDF)
- Dive into Deep Learning
- Explorations in Parallel Distributed Processing: A Handbook of Models, Programs, and Exercises - James L. McClelland
- Foundations of Machine Learning, Second Edition - Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar
- Free and Open Machine Learning - Maikel Mardjan (HTML)
- Gaussian Processes for Machine Learning
- IBM Machine Learning for Dummies - Judith Hurwitz, Daniel Kirsch
- Information Theory, Inference, and Learning Algorithms
- Interpretable Machine Learning - Christoph Molnar
- Introduction to CNTK Succinctly - James McCaffrey
- Introduction to Machine Learning - Amnon Shashua
- Keras Succinctly - James McCaffrey
- Learn Tensorflow - Jupyter Notebooks
- Learning Deep Architectures for AI (PDF)
- Machine Learning
- Machine Learning for Data Streams - Albert Bifet, Ricard Gavaldà, Geoff Holmes, Bernhard Pfahringer
- Machine Learning from Scratch - Danny Friedman (HTML, PDF, Jupyter Book)
- Machine Learning, Neural and Statistical Classification
- Machine Learning with Python - Tutorials Point (HTML, PDF)
- Mathematics for Machine Learning - Garrett Thomas (PDF)
- Mathematics for Machine Learning - Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong
- Neural Networks and Deep Learning
- Practitioners guide to MLOps - Khalid Samala, Jarek Kazmierczak, Donna Schut (PDF)
- Probabilistic Models in the Study of Language (Draft, with R code)
- Python Machine Learning Projects - Lisa Tagliaferri, Brian Boucheron, Michelle Morales, Ellie Birkbeck, Alvin Wan (PDF, EPUB, Kindle)
- Reinforcement Learning: An Introduction - Richard S. Sutton, Andrew G. Barto (PDF)
- Speech and Language Processing (3rd Edition Draft) - Daniel Jurafsky, James H. Martin (PDF)
- The Elements of Statistical Learning - Trevor Hastie, Robert Tibshirani, and Jerome Friedman
- The LION Way: Machine Learning plus Intelligent Optimization - Roberto Battiti, Mauro Brunato (PDF)
- The Mechanics of Machine Learning - Terence Parr and Jeremy Howard
- The Python Game Book - Horst Jens (:card_file_box: archived)
- Top 10 Machine Learning Algorithms Every Engineer Should Know - Binny Mathews and Omair Aasim
- Understanding Machine Learning: From Theory to Algorithms - Shai Shalev-Shwartz, Shai Ben-David
Coding Assistants/Interaction
Retrieval-Augmented Generation (RAG)
Tags:
reading
ai
machine learning
fuzzy logic
logic
expert system
nlp
Last modified 30 July 2025