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:
- An Introduction to Neural Networks
- An Introduction to Convolutional Neural Networks
- An Introduction to Recurrent Neural Networks
- Random Forests for Complete Beginners
Similar tags include Neural Networks, Computer Vision, and Random Forests.
Happy Reading!
Keras for Beginners
A beginner-friendly series on using Keras to build, train, and evaluate Neural Networks in Python.
View SeriesKeras for Beginners: Implementing a Recurrent Neural Network
A beginner-friendly guide on using Keras to implement a simple Recurrent Neural Network (RNN) in Python.
ReadEasy Visual Question Answering
Co-Authored by Phillip Wang
A gentle introduction to Visual Question Answering (VQA) using neural networks.
ReadA Simple Explanation of the Bag-of-Words Model
A quick, easy introduction to the Bag-of-Words model and how to implement it in Python.
ReadKeras for Beginners: Implementing a Convolutional Neural Network
A beginner-friendly guide on using Keras to implement a simple Convolutional Neural Network (CNN) in Python.
ReadNeural Networks From Scratch
A 4-post series that provides a fundamentals-oriented approach towards understanding Neural Networks.
View SeriesAn Introduction to Recurrent Neural Networks for Beginners
A simple walkthrough of what RNNs are, how they work, and how to build one from scratch in Python.
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