Your search for machine learning tutorials returned 53 results











Issue 192 - Tools and Techniques

Evolutionary Algorithms

Evolutionary algorithms are inspired by natural selection and are super interesting for solving search and optimization problems. Together, two articles this week make for a great primer. The first article, from the MIT Tech Review, explores how evolutionary algorithms work and the second is a step-by-step tutorial that shows how to build one from scratch using Python:













Issue 112 - Tools and Techniques

Machine Learning Crash Course

Two-part tutorial from the Machine Learning at Berkeley organization. This is very visual and easy to follow:

  • Part 1 - Introduction, Regression/Classification, Cost Functions, and Gradient Descent
  • Part 2 - Perceptrons, Logistic Regression, and SVMs












Issue 68 - Tools and Techniques

Machine Learning is Fun!

This two-part tutorial is for anyone who is curious about machine learning but has no idea where to start. The goal is be accessible to anyone, which means that there are a lot of generalizations but this is a worthwhile introduction. The newly released Part 2 uses neural nets to create Super Mario Maker levels. Fun read!



Issue 61 - In the News

TensorFlow

Google made a huge splash this week by open-sourcing their machine learning system called "TensorFlow." This is a BIG deal. Check out the announcement on the Google Research Blog and then follow up with these:






Issue 31 - Tools and Techniques

Conferences

There were a couple of great conferences this past week that are worth noting. OpenVis is definitely one of the best data visualization conferences anywhere and all of the presentations from the 2015 OpenVis conference have just been put online. If you're interested in data visualization, these are MUST SEE presentations. Then there was PyCon in Montreal which was an entire week of Python talks and tutorials. This 3 hour tutorial by Jake VanderPlas called Machine Learning with scikit-learn is particularly well done.







Your search for machine learning tutorials returned 53 results











Issue 192 - Tools and Techniques

Evolutionary Algorithms

Evolutionary algorithms are inspired by natural selection and are super interesting for solving search and optimization problems. Together, two articles this week make for a great primer. The first article, from the MIT Tech Review, explores how evolutionary algorithms work and the second is a step-by-step tutorial that shows how to build one from scratch using Python:













Issue 112 - Tools and Techniques

Machine Learning Crash Course

Two-part tutorial from the Machine Learning at Berkeley organization. This is very visual and easy to follow:

  • Part 1 - Introduction, Regression/Classification, Cost Functions, and Gradient Descent
  • Part 2 - Perceptrons, Logistic Regression, and SVMs












Issue 68 - Tools and Techniques

Machine Learning is Fun!

This two-part tutorial is for anyone who is curious about machine learning but has no idea where to start. The goal is be accessible to anyone, which means that there are a lot of generalizations but this is a worthwhile introduction. The newly released Part 2 uses neural nets to create Super Mario Maker levels. Fun read!



Issue 61 - In the News

TensorFlow

Google made a huge splash this week by open-sourcing their machine learning system called "TensorFlow." This is a BIG deal. Check out the announcement on the Google Research Blog and then follow up with these:






Issue 31 - Tools and Techniques

Conferences

There were a couple of great conferences this past week that are worth noting. OpenVis is definitely one of the best data visualization conferences anywhere and all of the presentations from the 2015 OpenVis conference have just been put online. If you're interested in data visualization, these are MUST SEE presentations. Then there was PyCon in Montreal which was an entire week of Python talks and tutorials. This 3 hour tutorial by Jake VanderPlas called Machine Learning with scikit-learn is particularly well done.







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