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:

Similar tags include Tensorflow, GANs, and Private Machine Learning.

Enjoy!

An introduction to probabilistic programming, now available in TensorFlow Probability

Machine Learning

Announcement for the work I did with the TFP Team at Google

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Getting started with Attention for Classification

Tutorials

A quick guide on how to start using Attention in your NLP models

Read

Practical Causal Inference

Machine Learning

Putting Judea Pearl's ideas into actual code

Read

Getting Returns from RL Algorithms

Machine Learning

Using algos from robotics and videogames to tackle the stock market

Read

Influence Functions from scratch

Machine Learning

Finding which data instances stand out the most

Read

Journey to ML Part 2 Skills of a (Marketable) Machine Learning Engineer

Machine Learning

Skills for making yourself actually stand out

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An introduction to probabilistic programming, now available in TensorFlow Probability

Machine Learning

Guest Post by Michael Shwe

Announcement for the work I did with the TFP Team at Google

Read