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© Matthew McAteer 2021
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!
Lessons 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
ReadHow to approach algorithm problems
An example that's probably closer to what you're experiencing in coding interviews
ReadUsing TPUs in Google Colab (properly)
A simple way to use TPUs with minimal hardware optimization
Read