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Google Machine Learning Crash Course

This class was created by Brainscape user Karsten Stiegler. Visit their profile to learn more about the creator.

By: Karsten Stiegler
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Decks in this class (9)

Terminology Week 1
Describe linear regression,
Describe a linear function,
Describe the notion of loss
11  cards
Terminology Week 2
What are hyperparameters,
Describe gradient descent,
What is a gradient
12  cards
Questions and Examples
How do i calculate the loss funct...,
Example code for linear regression,
How to draw data from a distribut...
3  cards
Week 3 (Tensorflow)
Tensorflow, Python, Pandas
27  cards
Concepts week 4 - Generalisation
What is ockhams razor law,
What is a test set methodology,
Describe generalisation
4  cards
Representation
One hot encoding,
Multi hot encoding,
Feature engineering
21  cards
Feature Crosses
What is a feature cross,
How do you call a synthetic featu...,
Explain three possible examples o...
9  cards
Regularisation
Regularisation,
Why regularisation,
A simple analogy for overfitting
6  cards
Classification
Accuracy,
What can go wrong with accuracy,
True positives
12  cards

More about
Google Machine Learning Crash Course

  • Class purpose General learning

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