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

qhadam_step(p, lr, mom, sqr_mom, sqr_avg, nu_1, nu_2, step, grad_avg, eps, ...)

<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>mom</code></td>
<td>
momentum
</td>
</tr>
<tr valign="top">
<td><code>sqr_mom</code></td>
<td>
sqr momentum
</td>
</tr>
<tr valign="top">
<td><code>sqr_avg</code></td>
<td>
sqr average
</td>
</tr>
<tr valign="top">
<td><code>nu_1</code></td>
<td>
nu_1
</td>
</tr>
<tr valign="top">
<td><code>nu_2</code></td>
<td>
nu_2
</td>
</tr>
<tr valign="top">
<td><code>step</code></td>
<td>
step
</td>
</tr>
<tr valign="top">
<td><code>grad_avg</code></td>
<td>
gradient average
</td>
</tr>
<tr valign="top">
<td><code>eps</code></td>
<td>
epsilon
</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#qhadam_step

`p`	p
`lr`	learning rate
`mom`	momentum
`sqr_mom`	sqr momentum
`sqr_avg`	sqr average
`nu_1`	nu_1
`nu_2`	nu_2
`step`	step
`grad_avg`	gradient average
`eps`	epsilon
`...`	additional arguments to pass

qhadam_step

Description

Usage

Arguments

Value

fastai

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