Coursera Deep Learning Course 1 Week 4 notes: Deep neural networks
20171016Deep Neural Network
Deep Llayer neural network
The word ‘deep’ refers to the number of layer of a neural network (not count the input layer).
Some notations:
 = number of layer
 = number of units in layer l (
 = activations in layer l ().
Forward Propagation in a Deep Network
Vectorization for whole training set (stack lowercase matrices in column to obtain capital matrices):
We can’t avoid having a for loop iterating over all layers.
Getting your matrix dimensions right
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Why deep representing
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Building blocks of deep neural networks
Forward and Backward Propagation
Forward propagation for layer l
Input
Output , cache()

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Backward propagation for layer l
Input
Output

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Parameters vs Hyperameters
Parameters: .
Hyperparameters: control parameters.
 Learning rate .
 Number of iterations.
 Number of hidden layers L.
 Number of hidden units.
 Choice of activation functions.
 Many more…
Therefore, applying deep learning is a very empirical process.