One reason why we'd still want to implement logistic regression from scratch could be that we don't have the impression that we fully understand how it works; we read a bunch of papers, and kind of understood the core concept though. Using a programming language for prototyping (e.g., P...
A machine learning algorithm, such as logistic regression can then be trained on this new dataset. In essence, this new meta-algorithm learns how to best combine the prediction from multiple submodels. Below is a function named to_stacked_row() that implements this procedure for creating new ...
It is closely related to linear regression and logistic regression that make predictions in a similar way (e.g. a weighted sum of inputs). The weights of the Perceptron algorithm must be estimated from your training data using stochastic gradient descent. Stochastic Gradient Descent Gradient Descen...
Implemented logistic regression using a neural network approach to classify images Completed Week 2 of Andrew NG's course Day 23 (1-10-18) Implemented gradient descent from scratch Implemented gradient descent form scratch Learnt more about activation functions sigmoid, tanh, ReLU and leaky ReLU Lear...