The chances of arriving at best set of hyper parameters is high in random search.
Answers
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?
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.
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?
False
True
We've seen in lecture that some hyperparameters, such as the learning rate, are more critical than others.
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
The amount of computational power you can access
The number of hyperparameters you have to tune
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)
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?
False
True
Note: Minor changes in your model could potentially need you to find good hyperparameters again from scratch.
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]
In the normalization formula, why do we use epsilon?
To avoid division by zero
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.
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.
Which of these statements about deep learning programming frameworks are true? (Check all that apply)
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.
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.