dingyu

1.1 Supervised Learning In supervised learning, we are given a data set and already know what our correct output should look like, having the idea that there is ralationship between the input and the...

4.1 Model Representation I Let’s examine how we will represent a hypothesis function using neural networks. At a very simple level, neurons are basically computational units that take inputs (dendrit...

5.1 Cost Function Let’s first define a few variables that we will need to use: L = total number of layers in the network slsls_l = number of units (not counting bias unit) in layer lll K = number o...

2.1 Multiple Features Linear regression with multiple variables is also known as “multivariate linear regression”. We now introduce notation for equations where we can have any number of input varia...

3.1 Classification To attemp classification, one method is to use linear regression and map all redictions greater than 0.5 as 1 and all less than 0.5 as a 0. However, this method doesn’t work well b...

6.1 Evaluating a Hypothesis Once we have done some trouble shooting for errors in our predictions by: - Getting more training examples - Trying smaller sets of features - Trying additional feature...