VIDEOStochastic gradient descent
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Covers: theory of Stochastic Gradient Descent
Estimated time needed to finish: 50 minutes
Why this is worth your time
CPSC 340 UBC ML course : lecture on Stochastic Gradient Descent (SGD)
How to use this item?

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Stochastic gradient descent

Collaborators
Total time needed: ~2 hours
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 Ng
12 minutes
ARTICLE 3. Toward data science - SGD
easy explanation of SGD
15 minutes
LIBRARY 4. SGD in Scikit-Learn
SGD library in scikit​-learn package in python
10 minutes
VIDEO 5. Gradient Descent with Andrew Ng
easy explanation of gradient descent from Andrew Ng
10 minutes
ARTICLE 6. Stochastic model
explanation of the ​stochastic model
10 minutes

Concepts Covered

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