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!
A beginner-friendly guide on using Keras to implement a simple Convolutional Neural Network (CNN) in Python.
ReadA 4-post series that provides a fundamentals-oriented approach towards understanding Neural Networks.
View SeriesA simple walkthrough of what RNNs are, how they work, and how to build one from scratch in Python.
ReadWhat Softmax is, how it's used, and how to implement it in Python.
ReadA beginner-friendly guide on using Keras to implement a simple Neural Network in Python.
ReadWhat Information Gain and Information Entropy are and how they're used to train Decision Trees.
ReadA simple walkthrough of deriving backpropagation for CNNs and implementing it from scratch in Python.
ReadA simple guide to what CNNs are, how they work, and how to build one from scratch in Python.
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