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© Matthew McAteer 2022
Machine Learning - Page 5
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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!
How 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
ReadAn introduction to probabilistic programming, now available in TensorFlow Probability
Announcement for the work I did with the TFP Team at Google
ReadGetting started with Attention for Classification
A quick guide on how to start using Attention in your NLP models
ReadAn introduction to probabilistic programming, now available in TensorFlow Probability
Guest Post by Michael Shwe
Announcement for the work I did with the TFP Team at Google
Read