How Important Is Human Competency In Machine Learning Success

How Important Is Human Competency In Machine Learning Success

Researchers from Delft University of Technology, Delft, The Netherlands surveyed a group of ML engineers of varying expertise. Hyperparameters are usually tuned by a human operator such as an ML engineer. This is still a standard practice despite the great success of AutoML platforms.

yperparameters are usually tuned by a human operator such as an ML engineer. This is still a standard practice despite the great success of AutoML platforms. Though there is no doubt that businesses are more readily embracing AutoML tools, the role of a human operator cannot be disregarded. So, now the question is — does the result of machine learning models depend on the competencies of the human operator. The answer is, of course, a plain YES. But that wouldn’t suffice. Organisations invest heavily in picking the right candidate. So, it is crucial to know about this aspect in more detail.

To find out, researchers from Delft University of Technology, Delft, The Netherlands surveyed a group of ML engineers of varying expertise. The results of this survey were published recently in a paper titled, ‘Black Magic in Deep Learning: How Human Skill Impacts Network Training’. 

The extraordinary skill of a human expert to tune hyperparameters, wrote the researchers, is informally referred to as “black magic” in deep learning here.


Does Experience Really Matter

Source: Kanav Anand et al.,PIN IT

For the experiment, the researchers selected the Squeezenet model as they found it to be efficient to train and achieve a reasonable accuracy compared to more complex networks. To prevent exploiting model-specific knowledge, they did not share the network design with the participants.

Participants were given access to 15 common hyperparameters. Mandatory ones were — number of epochs, batch size, loss function, and optimiser. The other 11 optional hyperparameters were set to their default values.


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