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Unsupervised Feature Learning and Deep Learning Tutorial

ufldl.stanford.edu · 1,085 words · saved by 1 readers

As a refresher, we will start by learning how to implement linear regression. The main idea is to get familiar with objective functions, computing their gradients and optimizing the objectives over a set of parameters. These basic tools will form the basis for more sophisticated algorithms later. Readers that want additional details may refer to the Lecture Note on Supervised Learning for more. Our goal in linear regression is to predict a target value y 𝑦 starting from a vector of input values x∈ R n 𝑥 ∈ ℜ 𝑛 . For example, we might want to make predictions about the price of a house so that y 𝑦 represents the price of the house in dollars and the elements x j 𝑥 𝑗 of x 𝑥 represent “features” that describe the house (such as its size and the number of bedrooms). Suppose that we are given many examples of houses where the features for the i’th house are denoted x (i) 𝑥 ( 𝑖 ) and the price is y (i) 𝑦 ( 𝑖 ) . For short, we will denote the Our goal is to find a functi

Unsupervised Feature Learning and Deep Learning Tutorial UFLDL Tutorial Home !--> Linear Regression 28 Aug 2013 ---> Problem Formulation As a refresher, we will start by learning how to implement linear regression. The main idea is to get familiar with objective functions, computing their gradients and optimizing the objectives over a set of parameters. These basic tools will form the basis for more sophisticated algorithms later. Readers that want additional details may refer to the Lecture Note on Supervised Learning for more. Our goal in linear regression is to predict a target value y star

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