deeplearning.ai

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/Week 1 Quiz - Practical aspects of deep learning.md

@@ -0,0 +1,50 @@
+## Week 1 Quiz - Practical aspects of deep learning
+
+1. If you have 10,000,000 examples, how would you split the train/dev/test set?
+
+    - 98% train . 1% dev . 1% test
+
+2. The dev and test set should:
+
+    - Come from the same distribution
+
+3. If your Neural Network model seems to have high variance, what of the following would be promising things to try?
+
+    - Add regularization
+    - Get more training data
+
+    Note: Check [here](https://user-images.githubusercontent.com/14886380/29240263-f7c517ca-7f93-11e7-8549-58856e0ed12f.png).
+
+4. You are working on an automated check-out kiosk for a supermarket, and are building a classifier for apples, bananas and oranges. Suppose your classifier obtains a training set error of 0.5%, and a dev set error of 7%. Which of the following are promising things to try to improve your classifier? (Check all that apply.)
+
+    - Increase the regularization parameter lambda
+    - Get more training data
+
+    Note: Check [here](https://user-images.githubusercontent.com/14886380/29240263-f7c517ca-7f93-11e7-8549-58856e0ed12f.png).
+
+ 5. What is weight decay?
+
+    - A regularization technique (such as L2 regularization) that results in gradient descent shrinking the weights on every iteration.
+
+ 6. What happens when you increase the regularization hyperparameter lambda?
+
+    - Weights are pushed toward becoming smaller (closer to 0)
+
+7. With the inverted dropout technique, at test time:
+
+    - You do not apply dropout (do not randomly eliminate units) and do not keep the 1/keep_prob factor in the calculations used in training
+
+8. Increasing the parameter keep_prob from (say) 0.5 to 0.6 will likely cause the following: (Check the two that apply)
+
+    - Reducing the regularization effect
+    - Causing the neural network to end up with a lower training set error
+
+9. Which of these techniques are useful for reducing variance (reducing overfitting)? (Check all that apply.)
+
+    - Dropout
+    - L2 regularization
+    - Data augmentation
+
+10. Why do we normalize the inputs x?
+
+    - It makes the cost function faster to optimize

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/Week 2 Quiz - Optimization algorithms.md

@@ -0,0 +1,67 @@
+## Week 2 Quiz - Optimization algorithms
+
+1. Which notation would you use to denote the 3rd layer’s activations when the input is the 7th example from the 8th minibatch?
+
+    - a^\[3]\{8}\(7)
+
+    Note: **[i]{j}(k)** superscript means **i-th layer**, **j-th minibatch**, **k-th example**
+
+2. Which of these statements about mini-batch gradient descent do you agree with?
+
+    - [ ] You should implement mini-batch gradient descent without an explicit for-loop over different mini-batches, so that the algorithm processes all mini-batches at the same time (vectorization).
+    - [ ] Training one epoch (one pass through the training set) using mini-batch gradient descent is faster than training one epoch using batch gradient descent.
+    - [x] One iteration of mini-batch gradient descent (computing on a single mini-batch) is faster than one iteration of batch gradient descent.
+
+    Note: Vectorization is not for computing several mini-batches in the same time.
+
+3. Why is the best mini-batch size usually not 1 and not m, but instead something in-between?
+
+    - If the mini-batch size is 1, you lose the benefits of vectorization across examples in the mini-batch.
+    - If the mini-batch size is m, you end up with batch gradient descent, which has to process the whole training set before making progress.
+
+4. Suppose your learning algorithm’s cost ***J***, plotted as a function of the number of iterations, looks like this:
+
+    - If you’re using mini-batch gradient descent, this looks acceptable. But if you’re using batch gradient descent, something is wrong.
+
+    Note: There will be some oscillations when you're using mini-batch gradient descent since there could be some noisy data example in batches. However batch gradient descent always guarantees a lower ***J*** before reaching the optimal.
+
+5. Suppose the temperature in Casablanca over the first three days of January are the same:
+
+    Jan 1st: θ_1 = 10
+
+    Jan 2nd: θ_2 * 10
+
+    Say you use an exponentially weighted average with β = 0.5 to track the temperature: v_0 = 0, v_t = βv_t−1 + (1 − β)θ_t. If v_2 is the value computed after day 2 without bias correction, and v^corrected_2 is the value you compute with bias correction. What are these values?
+
+    - v_2 = 7.5, v^corrected_2 = 10
+
+6. Which of these is NOT a good learning rate decay scheme? Here, t is the epoch number.
+
+    - α = e^t * α_0
+
+    Note: This will explode the learning rate rather than decay it.
+
+7. You use an exponentially weighted average on the London temperature dataset. You use the following to track the temperature: v_t = βv_t−1 + (1 − β)θ_t. The red line below was computed using β = 0.9. What would happen to your red curve as you vary β? (Check the two that apply)
+
+    - Increasing β will shift the red line slightly to the right.
+    - Decreasing β will create more oscillation within the red line.
+
+8. Consider this figure:
+
+    These plots were generated with gradient descent; with gradient descent with momentum (β = 0.5) and gradient descent with momentum (β = 0.9). Which curve corresponds to which algorithm?
+
+    (1) is gradient descent. (2) is gradient descent with momentum (small β). (3) is gradient descent with momentum (large β)
+
+9. Suppose batch gradient descent in a deep network is taking excessively long to find a value of the parameters that achieves a small value for the cost function J(W[1],b[1],...,W[L],b[L]). Which of the following techniques could help find parameter values that attain a small value forJ? (Check all that apply)
+
+    - [x] Try using Adam
+    - [x] Try better random initialization for the weights
+    - [x] Try tuning the learning rate α
+    - [x] Try mini-batch gradient descent
+    - [ ] Try initializing all the weights to zero
+
+10. Which of the following statements about Adam is False? 
+
+    - Adam should be used with batch gradient computations, not with mini-batches.
+
+    Note: Adam could be used with both.

Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/Week 3 Quiz - Hyperparameter tuning, Batch Normalization, Programming Frameworks.md

@@ -0,0 +1,61 @@
+## Week 3 Quiz - Hyperparameter tuning, Batch Normalization, Programming Frameworks
+
+1. If searching among a large number of hyperparameters, you should try values in a grid rather than random values, so that you can carry out the search more systematically and not rely on chance. True or False?
+
+    - [x] False
+    - [ ] True
+
+    Note: Try random values, don't do grid search. Because you don't know which hyperparamerters are more important than others.
+
+    >  And to take an extreme example, let's say that hyperparameter two was that value epsilon that you have in the denominator of the Adam algorithm. So your choice of alpha matters a lot and your choice of epsilon hardly matters.
+    
+2. Every hyperparameter, if set poorly, can have a huge negative impact on training, and so all hyperparameters are about equally important to tune well. True or False?
+
+    - [x] False
+    - [ ] True
+
+    > We've seen in lecture that some hyperparameters, such as the learning rate, are more critical than others.
+    
+3. During hyperparameter search, whether you try to babysit one model (“Panda” strategy) or train a lot of models in parallel (“Caviar”) is largely determined by:
+
+    - [ ] Whether you use batch or mini-batch optimization
+    - [ ] The presence of local minima (and saddle points) in your neural network
+    - [x] The amount of computational power you can access
+    - [ ] The number of hyperparameters you have to tune
+
+4. If you think β (hyperparameter for momentum) is between on 0.9 and 0.99, which of the following is the recommended way to sample a value for beta?
+
+    ```
+    r = np.random.rand()
+    beta = 1 - 10 ** (-r - 1)
+    ```
+
+5. Finding good hyperparameter values is very time-consuming. So typically you should do it once at the start of the project, and try to find very good hyperparameters so that you don’t ever have to revisit tuning them again. True or false?
+
+    - [x] False
+    - [ ] True
+
+    Note: Minor changes in your model could potentially need you to find good hyperparameters again from scratch.
+
+6. In batch normalization as presented in the videos, if you apply it on the lth layer of your neural network, what are you normalizing?
+
+    - z^[l]
+
+7. In the normalization formula, why do we use epsilon?
+
+    - To avoid division by zero
+
+8. Which of the following statements about γ and β in Batch Norm are true? **Only correct options listed**
+
+    - They can be learned using Adam, Gradient descent with momentum, or RMSprop, not just with gradient descent.
+    - They set the mean and variance of the linear variable z^[l] of a given layer.
+
+9. After training a neural network with Batch Norm, at test time, to evaluate the neural network on a new example you should:
+
+    - Perform the needed normalizations, use μ and σ^2 estimated using an exponentially weighted average across mini-batches seen during training.
+
+10. Which of these statements about deep learning programming frameworks are true? (Check all that apply)
+
+    - [x] A programming framework allows you to code up deep learning algorithms with typically fewer lines of code than a lower-level language such as Python.
+    - [x] Even if a project is currently open source, good governance of the project helps ensure that the it remains open even in the long term, rather than become closed or modified to benefit only one company.
+    - [ ] Deep learning programming frameworks require cloud-based machines to run.