Machine Learning Refined
This is a blog about machine learning / deep learning fundamentals built by the authors of the textbook Machine Learning Refined published by Cambridge University Press.
The posts - cut into short series - use careful writing and interactive coding widgets to provide an intuitive and playful way to learn about core concepts in AI- from some of the most basic to the most advanced.
Each and every post here is a Python Jupyter notebook - prettied up for the web - that you can download and run on your own machine by pulling our repo.
THE BASICS OF MATH FUNCTIONS
COMPUTATIONAL CALCULUS
A series of posts on the pivotal role Calculus plays in machine learning.
THE BASICS OF LINEAR ALGEBRA
In this series we review topics from linear algebra that are foundational to understanding machine learning.
MATHEMATICAL OPTIMIZATION
Topics here include methods by which we determine proper parameters for machine learning and deep learning models.
LINEAR SUPERVISED LEARNING
Topics include linear regression, logistic regression, support vector machines, and more.
REINFORCEMENT LEARNING
Topics include basic Q-learning algorithm and common enhancements, deep reinforcement learning, and more.
UNSUPERVISED LEARNING
Topics include K-means clustering, spectral clustering, PCA, recommender systems, non-negative matrix factorization, and more.