Why Deep Learning Works Even Though It Shouldn't
The article explores the reasons behind the effectiveness of deep learning models despite skepticism from traditional statistics. It discusses how larger and deeper models tend to perform better, even with less data. The author shares intuitive insights that may not be formally proven but highlight the unique characteristics of high-dimensional spaces in deep learning.
- ▪Deep learning models improve with increased size and depth, regardless of data volume.
- ▪The author argues that traditional statistics often underestimates the effectiveness of deep learning.
- ▪High-dimensional spaces allow for better parameter initialization and optimization.
Opening excerpt (first ~120 words) tap to expand
Why Deep Learning Works Even Though It Shouldn’t Ryan MoultonOctober 18, 2020April 2, 2021Statistics, Technical Post navigation PreviousNext This is a big question, and I’m not a particularly big person. As such, these are all likely to be obvious observations to someone deep in the literature and theory. What I find however is that there are a base of unspoken intuitions that underlie expert understanding of a field, that are never directly stated in the literature, because they can’t be easily proved with the rigor that the literature demands. And as a result, the insights exist only in conversation and subtext, which make them inaccessible to the casual reader.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at Ryan Moulton's Articles.
Discussion
0 commentsMore from Ryan Moulton's Articles
