## ▸ Key concepts on Deep Neural Networks :

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- What is the "cache" used for in our implementation of forward propagation and backward propagation?
- It is used to cache the intermediate values of the cost function during training.
**We use it to pass variables computed during forward propagation to the corresponding backward propagation step. It contains useful values for backward propagation to compute derivatives.**

**Correct**

Correct, the "cache" records values from the forward propagation units and sends it to the backward propagation units because it is needed to compute the chain rule derivatives.- We use it to pass variables computed during backward propagation to the corresponding forward propagation step. It contains useful values for forward propagation to compute activations.
- It is used to keep track of the hyperparameters that we are searching over, to speed up computation.
- Among the following, which ones are "hyperparameters"? (Check all that apply.)
**learning rate**

**Correct****number of layers L in the neural network**

**Correct**- weight matrices
- bias vectors
**number of iterations**

**Correct**- activation values
**size of the hidden layers**

**Correct**- Which of the following statements is true?
**The deeper layers of a neural network are typically computing more complex features of the input than the earlier layers.**

**Correct**- The earlier layers of a neural network are typically computing more complex features of the input than the deeper layers.
- Vectorization allows you to compute forward propagation in an L-layer neural network without an explicit for-loop (or any other explicit iterative loop) over the layers l=1, 2, ...,L. True/False?
- True
**False**

**Correct**

Forward propagation propagates the input through the layers, although for shallow networks we may just write all the lines in a deeper network, we cannot avoid a for loop iterating over the layers: .- Assume we store the values for in an array called layers, as follows: layer_dims = . So layer 1 has four hidden units, layer 2 has 3 hidden units and so on. Which of the following for-loops will allow you to initialize the parameters for the model?
**Correct**

- Consider the following neural network.

How many layers does this network have? **The number of layers L is 4. The number of hidden layers is 3.**

**Correct**

Yes. As seen in lecture, the number of layers is counted as the number of hidden layers + 1. The input and output layers are not counted as hidden layers.- The number of layers L is 3. The number of hidden layers is 3.
- The number of layers L is 4. The number of hidden layers is 4.
- The number of layers L is 5. The number of hidden layers is 4.
- During forward propagation, in the forward function for a layer l you need to know what is the activation function in a layer (Sigmoid, tanh, ReLU, etc.). During backpropagation, the corresponding backward function also needs to know what is the activation function for layer l, since the gradient depends on it. True/False?
**True**

**Correct**

Yes, as you've seen in the week 3 each activation has a different derivative. Thus, during backpropagation you need to know which activation was used in the forward propagation to be able to compute the correct derivative.- False
- There are certain functions with the following properties:

(i) To compute the function using a shallow network circuit, you will need a large network (where we measure size by the number of logic gates in the network), but

(ii) To compute it using a deep network circuit, you need only an exponentially smaller network. True/False? **True**

**Correct**- False
- Consider the following 2 hidden layer neural network:

Which of the following statements are True? (Check all that apply). **will have shape (4, 4)**

**Correct**

Yes. More generally, the shape of is .**will have shape (4, 1)**

**Correct**

Yes. More generally, the shape of is .- will have shape (3, 4)
- will have shape (3, 1)
**will have shape (3, 4)**

**Correct**

Yes. More generally, the shape of is .- will have shape (1, 1)
- will have shape (3, 1)
**will have shape (3, 1)**

**Correct**

Yes. More generally, the shape of is .- will have shape (3, 1)
**will have shape (1, 1)**

**Correct**

Yes. More generally, the shape of is .**will have shape (1, 3)**

**Correct**

Yes. More generally, the shape of is .- will have shape (3, 1)
- Whereas the previous question used a specific network, in the general case what is the dimension of , the weight matrix associated with layer ?
- has shape
- has shape
**has shape**

**Correct**

True- has shape

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