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And yet neural networks, with merely thousands or millions of parameters, somehow manage this classification task with ease.
As a result, most topic models generate topics independently from a single underlying distribution and require millions of parameters, in the form of multinomial distributions over the vocabulary.
However, estimating millions of parameters of a deep CNN requires a large number of annotated samples, which currently prevents many superior deep CNNs (such as AlexNet, VGG, ResNet) being applied to problems with limited training data.
Deep learning can automatically learn feature representation from big data, including millions of parameters.
So the way to think about it is you have millions of parameters and millions of data points.
Moreover, the millions of parameters in the full connected layers need large space in storage and memory.
Similar(44)
The resulting optimization problems often have millions or even billions of parameters.
This makes the use of ODE based kinetic models impossible on genome-scale networks, as thousands of parameters would have to be estimated.
Certainly such uniform bound has no chance of explaining why neural networks with billions of parameters would generalize with a data set of merely millions of examples.
But we're talking about even if you're doing a fairly simple project with 10,000 loci and 5 or 10 populations, you're talking about as many tens of thousands of parameters.
The most recent revolution in black-box technology came in 1990, with the introduction of a Solid State flight-data recorder that could record hundreds of parameters more than magnetic tape could handle.
More suggestions(16)
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com