# AI News, Deep Learning: Regularization Notes

- On Sunday, September 30, 2018
- By Read More

## Deep Learning: Regularization Notes

In previous article (long ago, now I am back!!) I talked about overfitting and the problems faced due to overfitting.

Many regularization approaches are based on limiting the capacity of models, such as neural networks, linear regression, or logistic regression, by adding a parameter norm penalty Ω(θ) to the objective function J.

X, y) + αΩ(θ) — {1} where α ∈[0, ∞) is a hyperparameter that weights the relative contribution of the norm penalty term, Ω, relative to the standard objective function J.

We note that for neural networks, we typically choose to use a parameter norm penalty Ω that penalizes only the weights of the aﬃne transformation at each layer and leaves the biases unregularized.

We therefore use the vector w to indicate all of the weights that should be aﬀected by a norm penalty, while the vector θ denotes all of the parameters, including both w and the unregularized parameters.

- On Monday, March 25, 2019

**Lecture 3 | Loss Functions and Optimization**

Lecture 3 continues our discussion of linear classifiers. We introduce the idea of a loss function to quantify our unhappiness with a model's predictions, and ...

**Lecture 18 - Epilogue**

Epilogue - The map of machine learning. Brief views of Bayesian learning and aggregation methods. Lecture 18 of 18 of Caltech's Machine Learning Course ...

**Tutorial 3.3: Lorenzo Rosasco - Machine Learning Part 3**

MIT RES.9-003 Brains, Minds and Machines Summer Course, Summer 2015 View the complete course: Instructor: Lorenzo ..

**Mod-09 Lec-33 Kernel Functions for nonlinear SVMs; Mercer and positive definite Kernels**

Pattern Recognition by Prof. P.S. Sastry, Department of Electronics & Communication Engineering, IISc Bangalore. For more details on NPTEL visit ...

**Open Borders? Immigration, Citizenship, and Nationalism in the 21st Century | Janus Forum Series**

The Political Theory Project is proud to host David Miller, the Official Fellow and Professor in Social and Political Theory at Nuffield College in Oxford, and ...

**CUNY 2017 Live Stream: Thursday March 30, Morning Session**

**Support vector machine**

In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that ...

**Least squares**

The method of least squares is a standard approach to the approximate solution of overdetermined systems, i.e., sets of equations in which there are more ...

**Least squares**

The method of least squares is a standard approach to the approximate solution of overdetermined systems, i.e., sets of equations in which there are more ...