Machine Learning

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Machine Learning is, put simply, getting computers to generalize from examples. And that's what I try to do: put [seemingly complicated] things simply. My posts on Machine Learning (ML) consist primarily of beginner-focused introductions to common ML models or concepts. I felt like too many ML tutorials weren't accessible enough, so I strove to make my guides as clear and beginner-friendly as possible.

Unsure where to start? Here are some of my best / most popular posts:

Similar tags include Neural Networks, Computer Vision, and Random Forests.

Happy Reading!

Keras for Beginners: Building Your First Neural Network

Python

A beginner-friendly guide on using Keras to implement a simple Neural Network in Python.

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A Simple Explanation of Information Gain and Entropy

Machine Learning

What Information Gain and Information Entropy are and how they're used to train Decision Trees.

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CNNs, Part 2: Training a Convolutional Neural Network

Machine Learning

A simple walkthrough of deriving backpropagation for CNNs and implementing it from scratch in Python.

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CNNs, Part 1: An Introduction to Convolutional Neural Networks

Machine Learning

A simple guide to what CNNs are, how they work, and how to build one from scratch in Python.

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Random Forests for Complete Beginners

Machine Learning

The definitive guide to Random Forests and Decision Trees.

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A Simple Explanation of Gini Impurity

Machine Learning

What Gini Impurity is (with examples) and how it's used to train Decision Trees.

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Machine Learning for Beginners: An Introduction to Neural Networks

Machine Learning

A simple explanation of how they work and how to implement one from scratch in Python.

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Building a Better Profanity Detection Library with scikit-learn

Machine Learning

Why existing libraries are uninspiring and how I built a better one.

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