The 'fastai' <https://docs.fast.ai/index.html> library
simplifies training fast and accurate neural networks
using modern best practices. It is based on research
in to deep learning best practices undertaken
at 'fast.ai', including 'out of the box' support
for vision, text, tabular, audio, time series, and
collaborative filtering models.

Interface to 'fastai'

package-ubuntu-20.04-r-4.0.5#cran#fastai-2.0.7

Step for SGD with momentum with 'lr'

rms_prop_step(p, lr, sqr_avg, eps, grad_avg = NULL, ...)

<table summary="R argblock">
<tr valign="top">
<td><code>p</code></td>
<td>
p
</td>
</tr>
<tr valign="top">
<td><code>lr</code></td>
<td>
learning rate
</td>
</tr>
<tr valign="top">
<td><code>sqr_avg</code></td>
<td>
sqr average
</td>
</tr>
<tr valign="top">
<td><code>eps</code></td>
<td>
epsilon
</td>
</tr>
<tr valign="top">
<td><code>grad_avg</code></td>
<td>
grad average
</td>
</tr>
<tr valign="top">
<td><code>...</code></td>
<td>
additional arguments to pass
</td>
</tr>
</table>

package-ubuntu-20.04-r-4.0.5#cran#fastai-2.0.7#docs#rms_prop_step

`p`	p
`lr`	learning rate
`sqr_avg`	sqr average
`eps`	epsilon
`grad_avg`	grad average
`...`	additional arguments to pass

rms_prop_step

Description

Usage

Arguments

Value

fastai

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