Covers: theory of Stochastic Gradient Descent

CPSC 340 UBC ML course : lecture on Stochastic Gradient Descent (SGD)

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- Objectives
- Learn the basic of stochastic gradient descent as an optimizer
- Potential Use Cases
- Stochastic Gradient Descent (SGD) is a simple yet very efficient approach to fitting linear classifiers and regressors under convex loss functions
- Who is this for ?
- INTERMEDIATE

Click on each of the following **annotated items** to see details.

VIDEO 1. Stochastic gradient descent

CPSC 340 UBC ML course : lecture on Stochastic Gradient Descent (SGD)50 minutes

VIDEO 2. Stochastic Gradient Descent

Easy explanation of SGD from Andrew Ng12 minutes

ARTICLE 3. Toward data science - SGD

easy explanation of SGD15 minutes

LIBRARY 4. SGD in Scikit-Learn

SGD library in scikit-learn package in python10 minutes

VIDEO 5. Gradient Descent with Andrew Ng

easy explanation of gradient descent from Andrew Ng10 minutes

ARTICLE 6. Stochastic model

explanation of the stochastic model 10 minutes

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