How Much Do You Know About Deep Learning?

Chaos table

Confusion matrix

Prediction plot

Residual plot

Optimizes parameters to improve the performance of a learning algorithm

Specifies the hyperplane that represents linear classifiers

Expands the parameter set of a model to improve performance

Takes parameter tuning so far that performance degrades

When a predictive model is accurate but takes too long to run

When a model learns specifics of the training data that do not generalize well to a new data set

When you apply a powerful deep learning algorithm to a simple machine learning problem

When you perform hyperparameter tuning and performance degrades

Regression learning

Sequence classification

Pixel segmentation

Semantic segmentation

Predicts numeric responses such as changes in temperature, date, or time

Assigns data to a predefined category

Finds patterns by clustering responses in groups based on similarity

Compares predicted data classifications to the actual class labels in the data

Data prophecy

Supervised learning

Unsupervised learning

Supervisory logic

Long short-term memory networks

YOLO-v3 networks

Generative adversarial networks

Super resolution networks

Machine learning

Transfer learning

Regression learning

Forecasting

Long short-term memory network

Attention network

Siamese network

Mini-batch algorithms

To test whether a model is superfitting the data

To measure the complexity of a model

To evaluate the model fit after training is complete

To test how well a model generalizes during training