Introduction A good ML practitioner needs to: know how to choose an algorithm how to monitor and respond to feedback … More
IMHO, This is a great book. Period. Below are some notes. I will put more here when possible. Work principles … More
Neuroscience-Inspired Artificial Intelligence Better understanding biological brains could play a vital role in building intelligent machines More recently, the interaction … More
How to learn from very little data? Analogy! Analogizers can learn from as little as one example because they never … More
Ronald Fisher’s classic treatise The Genetical Theory of natural Selection formulated the first mathematical theory of evolution. The key input … More
Hebb’s rule is the cornerstone of connectionism. It says knowledge is stored in the connections between neurons. Donald Hebb stated … More
“The Master Algorithm” by Dr. Pedro Domingos is a nice book. I enjoyed reading it. Learners program themselves. Learning algorithms … More
Reading Note: Realtime Machine Learning the Missing Pieces Context: ML has predominantly focused on training and serving predictions based on … More
This is my reading note for the paper titled “Machine Learning: the hight-interest credit card of technical debt”.
Machine learning is powerful toolkit to build complex systems quickly, but these quick wins does not come for free.
Dilemma: speed of execution and quality of engineering.