AI Reading List: Technologies: Technical issues
Below is a portion of my informal list of readings related to Artificial Intelligence (AI). This started out as a very short list created for use in conjunction with an academic presentation and has now grown much larger. Please let me know if you have any corrections, additions, suggestions, etc. It is very idiosyncratic and not meant to be comprehensive. Please feel free to share with others.
Artificial Intelligence (AI) Reading List, by Philip Rubin
Technologies — Technical issues:
Ernest Davis. New-AI-Benchmark: AI Benchmark: To be opened 1/2/2026. GitHub, January 2, 2026.
Kavishka Abeywardana. Probability Theory for Machine Learning: A Beginner’s Tutorial. Medium, December 28, 2025.
Aqeel Anwar. Animation is All You Need — A Visual Guide to Understanding Transformers — Part 1. Medium, December 27, 2025.
Irene Markelic. Essential Math for Data Science: Matrix Diagonalization Clearly Explained. Medium, December 27, 2025.
Irene Markelic. Unlocking Matrix Secrets! Understanding Eigenvalues and Eigenvectors. Medium, December 18, 2025.
Kuriko Iwai. Kullback-Leibler (KL) Divergence for LLMs. Mastering the exploration-exploitation trade-off in fine-tuning with KL divergence regularization. Medium, December 10, 2025.
Irene Markelic. Matrix Multiplication Made Easy. Medium, November 16, 2025.
ArnonBonny. 011: Understanding Logistic Regression (Cost Function and Optimization). Medium, November 15, 2025.
Kuriko Awai. Transformer in Action —Optimizing Self-Attention with Attention Approximation. Discover self-attention mechanisms and attention approximation techniques with practical examples. Medium, November 10, 2025.
Maxwell’s Demon. Kalman Filters Demystified — The Algorithm Behind Moon Landings. Medium, November 5, 2025.
Paolo Molignini. If It Bends, It Learns. How a Single Curve Can Reveal All You Need to Know About Your Binary Classifier. Medium, October 6, 2025.
Maxwell’s Demon. A Simple (But Not Too Simple) Intro to Linear Estimators. Optimally combining prior knowledge with new data. Medium, September 16, 2025.
Pankaj Chandravanshi. AI Agents from First Principles. Because Agents’ meaning and abilities are not clearly understood. Medium, September 10, 2025.
Mayur Jain. A Deep Dive into Vector Database Algorithms. Specialized algorithms that enable efficient similarity search on billions of document embeddings. Medium, September 6, 2025.
Okan Yenigün. Recurrent Neural Networks Explained Simply. Memory in Neural Networks: Understanding RNNs. Medium, August 29, 2025.
Ryan Revilla. The First Learning Algorithms: Adaptive Filters. A brief history lesson on machine learning origins that proved to be a useful learning exercise. Medium, August 5, 2025.
Khushbu Shah. 4 Advanced Data Modelling Techniques Every Data Engineer Must Learn. Medium, July 31, 2025.
Rohit Patel. Understanding LLMs from Scratch Using Middle School Math. A self-contained, full explanation to inner workings of an LLM. Medium, Oct. 19, 2024.
Louis Chan. SHAP: Explain Any Machine Learning Model in Python. Your Comprehensive Guide to SHAP, TreeSHAP, and DeepSHAP. Medium, Jan. 11, 2023.