August 2018
Intermediate to advanced
438 pages
12h 3m
English
Content preview from Hands-On Transfer Learning with Python
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Adaptive learning rate – separate for each connection
In the preceding methods the same learning rate is applied to all parameter updates. Having sparse data, we may instead want to update the parameters in different extent. Adaptive gradient descent algorithms, such as AdaGrad, AdaDelta, RMSprop, and Adam, provide an alternative to classical SGD by keeping per parameter learning rates.
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