Feature Engineering vs Representation Learning: A Guide by Sven Mayer
Explore the differences between feature engineering and representation learning in practical machine learning, as explained by Sven Mayer. Understand the pros and cons of each approach and their implications on model development.
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Presentation Transcript
Practical Machine Learning Feature Engineering & Representation Learning Sven Mayer
Feature Engineering Feature Engineering is often used for non NN ML e.g. SVM Requires domain knowledge and thinking before training Sven Mayer 2
Feature Engineering Sum of pixels Min/Max value Ellipse fitting Radius 1 & 2 Theta Area of the Convex hull And others we can think of depending on our problem Sven Mayer 3
Representation Learning The data gets presented into the model without additional preprocessing No domain knowledge No thinking The hope is that the model is doing the thinking for you Sven Mayer 4
Pros and Cons Feature Engineering Reduced the input data models can be smaller more suitable for traditional ML models Representation Learning Raw data as input the models needs to do more work the model has to be larger Harder to train more suitable for NN models Sven Mayer 5
Conclusion Feature Engineering & Representation Learning Feature Engineering Representation Learning Sven Mayer 6
License This file is licensed under the Creative Commons Attribution-Share Alike 4.0 (CC BY-SA) license: https://creativecommons.org/licenses/by-sa/4.0 Attribution: Sven Mayer Sven Mayer
