Resources for Machine Learning

AzureMachineLearning_00EAB562

In the last few months I’ve really immersed myself in the world of deep learning. I’ve always had a huge interest in GPUs through my work with gaming, but now that Machine Learning has really taken advantage of the increased horsepower offered by a GPU, I thought it would be a good time to learn how it all works.

I’ll continue to add to this over time, but here are many of the resources I’ve been using and found to be incredibly helpful. If you have any others, I’d love to hear about them.

Overview

With the amount of misinformation I see online around exactly what Machine Learning and AI entails, I thought I’d clarify a bit here.

Fundamentally, machine learning is using algorithms to extract information from raw data and represent it in some type of model. We use this model to infer things about other data we have not yet modeled.

Framing the question:

If we can answer these 3 questions, we can setup a machine learning workflow that will build our model and produce our desired answers.

  1. What is the input data we want to extract information (model) from?
  2. What kind of model is most appropriate for this data?
  3. What kind of answer would we like to elicit from new data based on this model?

I have other posts relevant to these areas as well:

Books / Blogs

Videos / Courses

Microsoft’s ML / AI tools

TopTenStrTechTrends2017_Infographic_Final

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