Deep Learning: Difference between revisions

Revision as of 15:38, 1 September 2020

Notes for CMSC 828W: Foundations of Deep Learning (Fall 2020) taught by Soheil Feizi

My notes are intended to be a concise reference for myself, not a comprehensive replacement for lecture.

A refresher of Machine Learning and Supervised Learning.

Minimize loss function over your data: \(\displaystyle \min_{W} \frac{1}{N} \sum_{i=1}^{N} l(f_{W}(x_i), y_i))\)

For regression, can use quadratic loss: \(\displaystyle l(f_W(x), y) = \frac{1}{2}\Vert f_W(x)-y \Vert^2\)

For classification, can use hinge-loss: \(\displaystyle l(f_W(x), y) = \max(0, 1-yf_W(x))\)

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 * [http://www.cs.umd.edu/class/fall2020/cmsc828W/ Course Website]
+My notes are intended to be a concise reference for myself, not a comprehensive replacement for lecture.
+==Basics==
+A refresher of [[Machine Learning]] and Supervised Learning.
+===Empirical risk minimization (ERM)===
+Minimize loss function over your data:
+<math>\min_{W} \frac{1}{N} \sum_{i=1}^{N} l(f_{W}(x_i), y_i))</math>
+===Loss functions===
+For regression, can use quadratic loss:
+<math>l(f_W(x), y) = \frac{1}{2}\Vert f_W(x)-y \Vert^2</math>
+For classification, can use hinge-loss:
+<math>l(f_W(x), y) = \max(0, 1-yf_W(x))</math>