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30 days of Machine Learning
A journal for learning machine learning.
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Day 6 — Naive Bayes Classifier
Day 6 — Naive Bayes Classifier
Today we’ll learn a basic classifier based on probability, naive bayes classifier. We start from Bayes theorem. And we’ll see two examples…
Tzu-Chi Lin
Dec 23, 2018
Day 5 -Entropy, Relative Entropy, and Cross Entropy
Day 5 -Entropy, Relative Entropy, and Cross Entropy
Today we’ll focus on the theory of entropy. Understand the intuition of entropy, and how it relates to logistic regression. We’ll cover…
Tzu-Chi Lin
Dec 21, 2018
Day 4 — Logistic Regression
Day 4 — Logistic Regression
Today we’ll focus on a simple classification model, logistic regression. From its intuition, theory, and of course, implement it by our…
Tzu-Chi Lin
Dec 7, 2018
Day 3 — K-Nearest Neighbors and Bias–Variance Tradeoff
Day 3 — K-Nearest Neighbors and Bias–Variance Tradeoff
Today we’ll learn our first classification model, KNN, and discuss the concept of bias-variance tradeoff and cross-validation. Also, we…
Tzu-Chi Lin
Dec 4, 2018
Day 2 — Supervised Learning and Linear Regression
Day 2 — Supervised Learning and Linear Regression
Today we’ll walk through supervised learning, and spend most of the time on linear regression, especially gradient descent algorithm…
Tzu-Chi Lin
Nov 19, 2018
Day 1 — Machine Learning in a nutshell
Day 1 — Machine Learning in a nutshell
What is machine learning? How can a machine ‘learn’?
Tzu-Chi Lin
Nov 15, 2018
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