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Transfer learning — Machine Learning Zoomcamp Module 8

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mlz-module-08

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convolutional layers convert an image into a vector representation
dense layers use vector representations to make predictions
using a pretrained neural network
imagenet has 1000 different classes
a dense layer may be specific to a certain number of classes whereas the vector representation can be applied to another dataset
reusing the vector representation from convolutional layers means transferring knowledge and the idea behind transfer learning
train faster on smaller size images
the batch size
base model vs custom model
bottom layers vs top layers in keras
keras optimizers
using the adam optimizer
weights, learning rates
eta in xgboost
model loss
categorical cross entropy
changing accuracy during several training epochs
overfitting

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