Deep Learning
A tuning parameter in an optimization algorithm that determines the step size at each iteration while moving toward a minimum of a loss function.
Learning Rate
Underfitting
MNIST
Values distant from most other values are called what?
Outliers
What is the output of a softmax function?
A vector of probabilities, adding up to 1
A full training pass over the entire dataset such that each example has been seen once is called what?
What is a confusion matrix in binary classification?
A 2x2 grid that has four parts, the number of true positives, false positives, true negatives, and false negatives, showing comparisons vs actual results.
What was the name of the first computer that beat the world champion at chess?
Deep Blue
The two parameters of a normal distribution.
Mean and standard deviation
Popular programming language designed by statisticians.
R
What does LSTM stand for?
What is it called when you combine individual models together with the purpose of improving the predictive power of the overall model?
Ensembling
Who coined the term Deep Learning in 2006?
Geoffrey Hinton
How is precision rate calculated?
True Positives / (True Positives + False Positives)
What is the range of sigmoid function output?
0 to 1
A technique to downsample feature maps, often used in convolutional models.
Pooling
Name two types of unsupervised learning algorithms.
Clustering methods, autoencoders, latent variable models, etc.
PyTorch was primarily developed by which company?
Meta (Facebook)
A theory that states as the number of trials increases, the average of the result will become closer to the expected value.
Law of Large Numbers
What are the two main components of a GAN?
Generator and Discriminator
A regularization method that works by removing a random selection of units in a network layer for a single gradient step.
Dropout
Name three types of kernels of support vector machines (SVM).
Linear, polynomial, radical/radial, sigmoid
Who is known as the founding father of convolutional nets?
Yann LeCun
A variable that influences both the dependent variable and the independent variable, causing a spurious association.
Confounding variable
The logical error of concentrating on the people or things that made it past some selection process and overlooking those that did not.
Survivorship Bias