Machine Learning
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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!
Journey to ML Part 2 Skills of a (Marketable) Machine Learning Engineer
Skills for making yourself actually stand out
ReadJourney to Machine Learning Part 1 Introductions Motivations and Roadmap
Beginnings of how to start a career in Machine Learning
ReadHeirarchal Temporal Memory (with Keras example)
Making ANNs that resemble actual neural networks in the brain
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