Udacity Machine learning Nanodegree Syllabus and Summary

udacity nanodegree - becoming a machine learning engineer. This is my personal notes summarizing what I learned from the section, consider it my personal study notes. The part I labeled syllabus is the actual outline of the course (e.g. I try to use section title as the syllabus section title)

Section supervised learning
Sub section artificial neutral networks
Sub section neural networks
     how does a neuron work (illustrated)
     How artificial neural network works (illustrated) perceptron
     A group of inputs, each with a weights, processed by the network and output 1 of and only if a preconfigured threshold is met
     Artificial neural networks can be tuned

• Think of inputs as signals with different strengths, weights as sensitivity to those strengths. Can be tuned and adjusted to computer variety of tasks. Hence a collection of perceptroncomputing units is powerful
• The weights sum of all the inputs is called the activation
• When activation is greater than the threshold theta the perceptron outputs 1
•