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GitHub Repository: y33-j3T/Coursera-Deep-Learning
 Path: blob/master/Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/week6/__pycache__/opt_utils.cpython-36.pyc
Views: 13376
3

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                    W1 -- weight matrix of shape (layer_dims[l], layer_dims[l-1])
                    b1 -- bias vector of shape (layer_dims[l], 1)
                    Wl -- weight matrix of shape (layer_dims[l-1], layer_dims[l])
                    bl -- bias vector of shape (1, layer_dims[l])
                    
    Tips:
    - For example: the layer_dims for the "Planar Data classification model" would have been [2,2,1]. 
    This means W1's shape was (2,2), b1 was (1,2), W2 was (2,1) and b2 was (1,1). Now you have to generalize it!
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    Implements the forward propagation (and computes the loss) presented in Figure 2.
    
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    Implement the backward propagation presented in figure 2.
    
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