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Data Science Interviews
1.48MB. 0 audio & 27 images. Updated 2020-09-29.
Sample (from 117 notes)
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|Back||Bootstrap aggregatingTrain multiple models on subsamples and average predictions to reduce variance.Usually uses "strong," low-bias models.|
|Front||How does an artificial neuron (perceptron) work?|
|Back||Applies an activation function to the weighted sum of its inputs.\[ y = f \left( \textstyle \sum w_i x_i \right) \]Common activation functions are linear, step, sigmoid, tangent, rectified linear...|
|Front||How do decision trees work, high-level?|
|Back||Recursively split the data into groups based on most discriminating feature; each leaf gives a prediction.|
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Basic concepts to go.on
Clear and concise questions and answers.on
Nice deck. Covers main concepts!on
Using it a lot! Thankson
Exactly what I was looking for!on
Thank you! Good job!on
Thanks a lot!on
quite a comprehensive deck, with some subtle questions