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Let’s imagine one wants to create two algorithms which both aim at answering the following question: is there a car on the picture?
One of them would be based on machine learning whereas the second one would be based on deep learning. How would the car features be learned by each of he programs? (features = significant elements that enable the computer to recognize them)
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In order to get good results when implementing deep learning, one needs to prepare his data before using it. What action should be done during this preparation?6
What is the missing word in the following sentence: Overfitting is the production of a model that corresponds too closely to the training data set and may therefore fail to fit other data sets and so fail to perform any reliable forecast.4
What are the differences/similarities between a loss function, an error function, and a cost function?5
What is ensemble learning? It consists in …8
How to handle missing data in a given training data set ?7
The validation set is used to provide frequent and unbiased evaluations of the model’s fit on the training set while tuning its hyperparameters/parameters: in other words,the model is found and then tested on the validation set before to be improved once again.16
How to handle missing data in a given training data set ?