▸Hyperparameter tuning, Batch Normalization, Programming Frameworks :

 Improving Deep Neural Networks Week-3 (MCQ)

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?

• True

• False

1. 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?

• True

• False
Yes. We’ve seen in lecture that some hyperparameters, such as the learning rate, are more critical than others.

1. 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

1. 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 = r*0.09 + 0.9


• r = np.random.rand()
beta = 1-10**(- r - 1)


• r = np.random.rand()
beta = 1-10**(- r + 1)


• r = np.random.rand()
beta = r*0.9 + 0.09

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?

• True

• False

1. In batch normalization as presented in the videos, if you apply it on the lth layer of your neural network, what are you normalizing?

1. In the normalization formula $\inline&space;\dpi{150}&space;\large&space;z_{norm}^{(i)}&space;=&space;\frac{z^{(i)}-\mu}{\sqrt{\mu^2+\varepsilon}}$, why do we use epsilon?

• To speed up convergence

• In case μ is too small

• To have a more accurate normalization

• To avoid division by zero

1. Which of the following statements about γ and β in Batch Norm are true?

Check-out our free tutorials on IOT (Internet of Things):

1. After training a neural network with Batch Norm, at test time, to evaluate the neural network on a new example you should:

1. 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.

• Deep learning programming frameworks require cloud-based machines to run.

• 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.

&
Click here to see more codes for Raspberry Pi 3 and similar Family.
&
Click here to see more codes for NodeMCU ESP8266 and similar Family.
&
Click here to see more codes for Arduino Mega (ATMega 2560) and similar Family.

Feel free to ask doubts in the comment section. I will try my best to answer it.
If you find this helpful by any mean like, comment and share the post.
This is the simplest way to encourage me to keep doing such work.

Thanks & Regards,
- APDaga DumpBox