== General ==

[[http://blog.mailgun.com/machine-learning-for-everyday-tasks/|Machine Learning for Everyday Tasks]]. Data only has two dimensions?

[[http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf|Rules of Machine Learning: Best Practices for ML Engineering]]

== TensorFlow ==

[[https://github.com/jostmey/NakedTensor|NakedTensor: Bare bone examples of machine learning in TensorFlow]]

[[https://github.com/astorfi/TensorFlow-World|astorfi/TensorFlow-World]]: Simple and easy to use TensorFlow samples & tutorials

== Applications ==

https://github.com/luanfujun/deep-photo-styletransfer: Given a "style" photo and a "target" photo, make the target photo look visually similar to the style photo

https://github.com/junyanz/interactive-deep-colorization: Colorize black & white photos.

== Linear Algebra ==

=== Books ===

Axler, Sheldon. ''Linear Algebra Done Right''.

== Support Vector Machines (SVMs) ==

[[https://generalabstractnonsense.com/2017/03/A-quick-look-at-Support-Vector-Machines/|A quick look at Support Vector Machines | General Abstract Nonsense]]

[[https://www.udacity.com/course/intro-to-machine-learning--ud120|Udacity's Intro to Machine Learning concepts]]. Covers SVMs.

[[https://www.youtube.com/watch?v=_PwhiWxHK8o|MIT OpenCourseWare 6.034 Artificial Intelligence lecture on Support Vector Machines]]

[[https://monkeylearn.com/blog/introduction-to-support-vector-machines-svm/|An introduction to Support Vector Machines (SVM) | MonkeyLearn Blog]]

== Courses ==

 * [[https://medium.com/@andrewng/deeplearning-ai-announcing-new-deep-learning-courses-on-coursera-43af0a368116|deeplearning.ai: Announcing new Deep Learning courses on Coursera]]
 * [[https://research.fb.com/the-facebook-field-guide-to-machine-learning-video-series/|Introducing the Facebook Field Guide to Machine Learning video series]]