CS229a - Week 9
Density Estimation Problem Motivation Aircraft engine features: \(x_1\) = heat generated \(x_2\) = vibration intensity Dataset: \[\{x^{(1)}, x^{(2)}, ..., x^{(m)}\}\] New engine: \(x_{test}\) Fraud detection: \(x^{(i)}\): features of user i’s...
CS229a - Week 8
In unsupervised learning what we do is we give this sort of unlabeled training set to an algorithm and we just ask the algorithm find some structure in the data...
CS229a - Week 7
Compared to both logistic regression and neural networks, the Support Vector Machine, or SVM sometimes gives a cleaner, and sometimes more powerful way of learning complex non-linear functions. Large Margin...
CS229a - Week 6
Evaluating a Learning Algorithm Deciding what to try next Debugging a learning algorithm if you test your hypothesis on the new set of houses, suppose you find that this is...
CS229a - Week 5
Cost Function and Back-propagation Cost Function Suppose we have neural networks to classification problems. \[\{(x^{(1)}, y^{(1)}), (x^{(2)}, y^{(2)}), ..., (x^{(m)}, y^{(m)})\}\] \(L\) = total number of layers in network \(s_l\)...