I write about AI, Biotech, and a bunch of other topics. Subscribe to get new posts by email!
© Matthew McAteer 2021
Machine Learning - Page 2
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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!
Nitpicking Machine Learning Technical Debt
Revisiting a resurging NeurIPS 2015 paper (and 25 best practices more relevant than that for 2020)
ReadBuilding a better Patent Classifier
Building a classifier to tell you whether your LIDAR patent will be approved
ReadDeploying and Scaling ML
Practical considerations of scaling and implementing ML in the real world
ReadStructural Similarity in Neural Style Transfer
Co-Authored by Vinay Prabhu
Bringing back an ancient computer vision technique to effortlessly improve style transfer results
Read