Deep Learning Isn’t The End-All Be-All Solution

Pablo Cordero explains that deep learning solutions are not the best choice in all cases:

The second preconception I hear the most is the hype. Many yet-to-be practitioners expect deep nets to give them a mythical performance boost just because it worked in other fields. Others are inspired by impressive work in modeling and manipulating images, music, and language – three data types close to any human heart – and rush headfirst into the field by trying to train the latest GAN architecture. The hype is real in many ways. Deep learning has become an undeniable force in machine learning and an important tool in the arsenal of any data modeler. Its popularity has brought forth essential frameworks such as tensorflow and pytorch that are incredibly useful even outside deep learning. Its underdog to superstar origin story has inspired researchers to revisit other previously obscure methods like evolutionary strategies and reinforcement learning. But it’s not a panacea by any means. Aside from lunch considerations, deep learning models can be very nuanced and require careful and sometimes very expensive hyperparameter searches, tuning, and testing (much more on this later in the post). Besides, there are many cases where using deep learning just doesn’t make sense from a practical perspective and simpler models work much better.

It’s a very interesting article, pointing out that deep learning solutions work better than expected on smaller data sizes, but there are areas where it’s preferable to choose something else.

Related Posts

Anomaly Detection With Python

Robert Sheldon continues his SQL Server Machine Learning Series: As important as these concepts are to working Python and MLS, the purpose in covering them was meant only to provide you with a foundation for doing what’s really important in MLS, that is, using Python (or the R language) to analyze data and present the […]

Read More

Non-English Natural Language Processing

The folks at BNOSAC have announced a new natural language processing toolkit for R: BNOSAC is happy to announce the release of the udpipe R package (https://bnosac.github.io/udpipe/en) which is a Natural Language Processing toolkit that provides language-agnostic ‘tokenization’, ‘parts of speech tagging’, ‘lemmatization’, ‘morphological feature tagging’ and ‘dependency parsing’ of raw text. Next to text […]

Read More

Categories

August 2017
MTWTFSS
« Jul Sep »
 123456
78910111213
14151617181920
21222324252627
28293031