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mention numpy & scipy convolve functions in gotchas doc. (#3214)

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docs/notebooks/Common_Gotchas_in_JAX.ipynb
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@ -1,32 +1,4 @@
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@ -618,11 +590,11 @@
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"Note that due to this behavior np.nanargmin and np.nanargmax return -1 for slices consisting of NaNs whereas Numpy would throw an error."
]
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"Note that due to this behavior np.nanargmin and np.nanargmax return -1 for slices consisting of NaNs whereas Numpy would throw an error."
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@ -1627,7 +1599,9 @@
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"source": [
"JAX and XLA offer the very general N-dimensional __conv_general_dilated__ function, but it's not very obvious how to use it. We'll give some examples of the common use-cases. There are also the convenience functions `lax.conv` and `lax.conv_general_padding` for the most common kinds of convolutions.\n",
"JAX and XLA offer the very general N-dimensional __conv_general_dilated__ function, but it's not very obvious how to use it. We'll give some examples of the common use-cases.\n",
"\n",
"For the most common kinds of convolutions, see also the convenience functions lax.conv and lax.conv_general_padding, as well as jax.numpy.convolve and jax.scipy.signal.convolve/jax.scipy.signal.convolve2d for an interface similar to that of the numpy and scipy packages.\n",
"\n",
"A survey of the family of convolutional operators, [a guide to convolutional arithmetic](https://arxiv.org/abs/1603.07285) is highly recommended reading!\n",
"\n",
@ -2625,5 +2599,33 @@
"If something's not covered here that has caused you weeping and gnashing of teeth, please let us know and we'll extend these introductory _advisos_!"
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