🖍️N D Lewis - Collected Quotes

"Deep learning is an area of machine learning that emerged from the intersection of neural networks, artificial intelligence, graphical modeling, optimization, pattern recognition and signal processing." (N D Lewis, "Deep Learning Made Easy with R: A Gentle Introduction for Data Science", 2016)

"Overfitting is like attending a concert of your favorite band. Depending on the acoustics of the concert venue you will hear both music and noise from the screams of the crowd to reverberations off walls and so on. Overfitting happens when your model perfectly fits both the music and the noise when the intent is to fit the structure (the music). It is generally a result of the predictor being too complex (recall Occams Razor) so that it fits the underlying structure as well as the noise. The consequence is a small or zero test set classification error. Alas, this super low error rate will fail to materialize on future unseen samples. One consequence of overfitting is poor generalization (prediction) on future data." (N D Lewis, "Deep Learning Made Easy with R: A Gentle Introduction for Data Science", 2016)

"Roughly stated, the No Free Lunch theorem states that in the lack of prior knowledge (i.e. inductive bias) on average all predictive algorithms that search for the minimum classification error (or extremum over any risk metric) have identical performance according to any measure." (N D Lewis, "Deep Learning Made Easy with R: A Gentle Introduction for Data Science", 2016)

"Underfitting can also be a problem. It happens when the predictor is too simplistic or rigid to capture the underlying characteristics of the data. In this case the test error will be rather large." (N D Lewis, "Deep Learning Made Easy with R: A Gentle Introduction for Data Science", 2016)

"The bias variance decomposition is a useful tool for understanding classifier behavior. It turns out the expected misclassification rate can be decomposed into two components, a reducible error and irreducible error [...] Irreducible error or inherent uncertainty is associated with the natural variability in the phenomenon under study, and is therefore beyond our control. [...] Reducible error, as the name suggests, can be minimized. It can be decomposed into error due to squared bias and error due to variance." (N D Lewis, "Deep Learning Made Easy with R: A Gentle Introduction for Data Science", 2016)

"The power of deep learning models comes from their ability to classify or predict nonlinear data using a modest number of parallel nonlinear steps4. A deep learning model learns the input data features hierarchy all the way from raw data input to the actual classification of the data. Each layer extracts features from the output of the previous layer." (N D Lewis, "Deep Learning Made Easy with R: A Gentle Introduction for Data Science", 2016)