Machine Learning

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In a nutshell, Machine Learning is a way to give computers the powers of generalization. And that's what I try to do: put [seemingly complicated] things simply. My posts on Machine Learning (ML) focus mainly on recent cutting edge research, and how to make it accessible to everyone. I felt like many academic guides weren't accessible enough, so I strove to make my guides as clear and practically-focused as possible.

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

Similar tags include Tensorflow, GANs, and Private Machine Learning.

Enjoy!

Nitpicking Machine Learning Technical Debt

Paper Critiques

Revisiting a resurging NeurIPS 2015 paper (and 25 best practices more relevant than that for 2020)

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Building a better Patent Classifier

Tutorials

Building a classifier to tell you whether your LIDAR patent will be approved

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Gaussian KDE from Scratch

Machine Learning

Understanding KDE inside and out

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Deploying and Scaling ML

Machine Learning

Practical considerations of scaling and implementing ML in the real world

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Deep Learning Concepts every practicioner should know

Machine Learning

A deep dive into the important 'deep' learning concepts

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Machine Learning Fundamentals

Machine Learning

Fundamentals of all types of machine learning, deep or otherwise

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The Math Behind ML (the important stuff)

Machine Learning

Important mathematical prerequisites for getting into Machine Learning, Deep Learning, or any of the other space

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