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© Matthew McAteer 2022
Machine Learning - Page 4
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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:
- Lessons from becoming a Machine Learning Engineer in 12 months, without a CS or Math degree
- How to approach algorithm problems
- An introduction to probabilistic programming, now available in TensorFlow Probability
Similar tags include Tensorflow, GANs, and Private Machine Learning.
Enjoy!
Under-Investigated Fields List (Version 1.0)
Nominations for fields that are not being researched as much as they should be
ReadAvoiding Disaster in ML Projects
A few high-level patterns to look out for in any projects you start or join
ReadLessons from becoming a Machine Learning Engineer in 12 months, without a CS or Math degree
A Guide to starting from scratch, based on my own experiences (WARNING: LONG READ)
ReadTurning my Coworker's Chihuahua into a Bear
Demo of Few-Shot Unsupervised Image-to-Image Translation
ReadMachine Learning Meme Collection
An increasingly necessary component of being engaged on ML Twitter
Read