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© Matthew McAteer 2022
Machine Learning - Page 3
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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!
Deploying and Scaling ML
Practical considerations of scaling and implementing ML in the real world
ReadDeep Learning Concepts every practicioner should know
A deep dive into the important 'deep' learning concepts
ReadMachine Learning Fundamentals
Fundamentals of all types of machine learning, deep learning or otherwise
ReadThe Math Behind ML (the important stuff)
Important mathematical prerequisites for getting into Machine Learning, Deep Learning, or any of the other space
ReadML Research Interview Handbook
A 4-post series that covers the subject material in a good machine learning engineer interview.
View SeriesUnder-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
Read