March 2018
Intermediate to advanced
484 pages
10h 31m
English
The flexibility of neural networks is also one of their main drawbacks: there are many hyperparameters to tweak. Even in a simple MLP, you can change the number of layers, the number of neurons per layer, and the type of activation function to use in each layer. You can also change the weight initialization logic, the drop out keep probability, and so on.
Additionally, some common problems in FFNNs, such as the gradient vanishing problem, and selecting the most suitable activation function, learning rate, and optimizer, are of prime importance.
Hyperparameters are parameters that are not directly learned within estimators. It is possible and recommended that you search the hyperparameter ...
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