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:
Similar tags include Tensorflow, GANs, and Private Machine Learning.
Enjoy!
Building a classifier to tell you whether your LIDAR patent will be approved
ReadPractical considerations of scaling and implementing ML in the real world
ReadA deep dive into the important 'deep' learning concepts
ReadFundamentals of all types of machine learning, deep learning or otherwise
ReadImportant mathematical prerequisites for getting into Machine Learning, Deep Learning, or any of the other space
ReadA 4-post series that covers the subject material in a good machine learning engineer interview.
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