Meaning of parameters in torch.nn.conv2d
In the fastai cutting edge deep learning for coders course lecture 7.

 self.conv1 = nn.Conv2d(3,10,kernel_size = 5,stride=1,padding=2)


Does 10 there mean the number of filters or the number activations the filter will give? 

Here is what you may find

torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros')

Parameters

in_channels (int) – Number of channels in the input image
out_channels (int) – Number of channels produced by the convolution
kernel_size (int or tuple) – Size of the convolving kernel
stride (int or tuple, optional) – Stride of the convolution. (Default: 1)
padding (int or tuple, optional) – Zero-padding added to both sides of the input (Default: 0)
padding_mode (string, optional) – zeros
dilation (int or tuple, optional) – Spacing between kernel elements. (Default: 1)
groups (int, optional) – Number of blocked connections from input to output channels. (Default: 1)
bias (bool, optional) – If True, adds a learnable bias to the output. (Default: True)

And this URL has helpful visualization of the process.
So the in_channels in the beginning is 3 for images with 3 channels (colored images).
For images black and white it should be 1.
Some satellite images should have 4.
The out_channels is the number of filters and you can set this arbitrary.
Let's create an example to &quot;prove&quot; that.
import torch
import torch.nn as nn
c = nn.Conv2d(1,3, stride = 1, kernel_size=(4,5))
print(c.weight.shape)
print(c.weight)

Out
torch.Size([3, 1, 4, 5])
Parameter containing:
tensor([[[[ 0.1571,  0.0723,  0.0900,  0.1573,  0.0537],
          [-0.1213,  0.0579,  0.0009, -0.1750,  0.1616],
          [-0.0427,  0.1968,  0.1861, -0.1787, -0.2035],
          [-0.0796,  0.1741, -0.2231,  0.2020, -0.1762]]],


        [[[ 0.1811,  0.0660,  0.1653,  0.0605,  0.0417],
          [ 0.1885, -0.0440, -0.1638,  0.1429, -0.0606],
          [-0.1395, -0.1202,  0.0498,  0.0432, -0.1132],
          [-0.2073,  0.1480, -0.1296, -0.1661, -0.0633]]],


        [[[ 0.0435, -0.2017,  0.0676, -0.0711, -0.1972],
          [ 0.0968, -0.1157,  0.1012,  0.0863, -0.1844],
          [-0.2080, -0.1355, -0.1842, -0.0017, -0.2123],
          [-0.1495, -0.2196,  0.1811,  0.1672, -0.1817]]]], requires_grad=True)

If we would alter the number of out_channels,
c = nn.Conv2d(1,5, stride = 1, kernel_size=(4,5))
print(c.weight.shape) # torch.Size([5, 1, 4, 5])

We will get 5 filters each filter 4x5 as this is our kernel size.
If we would set 2 channels, (some images may have 2 channels only)
c = nn.Conv2d(2,5, stride = 1, kernel_size=(4,5))
print(c.weight.shape) # torch.Size([5, 2, 4, 5])

our filter will have 2 channels.
I think they have terms from this book and since they haven't called it filters, they haven't used that term.
So you are right; filters are what conv layer is learning and the number of filters is the number of out channels. They are set randomly at the start.
Number of activations is calculated based on bs and image dimension:
bs=16
x = torch.randn(bs, 3, 28, 28)
c = nn.Conv2d(3,10,kernel_size=5,stride=1,padding=2)
out = c(x)
print(out.nelement()) #125440 number of activations

