Alexnet implementation in tensorflow has incomplete architecture where 2 convolution neural layers are missing. This issue is in reference to the python notebook mentioned below.

https://github.com/donnemartin/data-science-ipython-notebooks/blob/master/deep-learning/tensor-flow-examples/notebooks/3_neural_networks/alexnet.ipynb

If you join Dask DataFrame on a categorical column, then the outputted Dask DataFrame column is still category dtype. However, the moment you .compute() the outputted Dask DataFrame, then the column is the wrong dtype, not categorical.

Tested on Dask 2.14.0 and Pandas 1.0.3
This example where the category type looks like a float, so after .compute(), the dtype is float.

import dask.d

In my opinion, some people might not be able to contribute to CuPy because of not having an NVIDIA GPU. But they might not know that we can build a development env on google colab(As I did here).

import os
from google.colab import drive
drive.mount('/content/drive')
os.chdir("/content/drive/My Drive/")

!git clone ht

Description

STFT allocates an output buffer, but sometimes you might want to have it compute directly into an existing buffer. For example, in griffin-lim, the method alternates stft/istft for each iterate, which is then discarded. It would be better if we could give it an out= variable, which it would use instead of allocating a new buffer; this way, we could cut down on redundant mem

https://www.python.org/dev/peps/pep-0257/ lists some conventions how dostrings should be formatted. Most of the docstrings are compatible already, some are not.

Some are descriptive which violate the rule that a docstring should prescribe the function or method's effect as a command "Do this", "Return that", instead of "Returns the pathname ...".

http://stackoverflow.com/questions/11469336/what-is-the-difference-between-pylab-and-pyplot

Fixes

NameError: global name 'pylab' is not defined

and

Collecting pylab
  Could not find a version that satisfies the requirement pylab (from versions: )
No matching distribution found for pylab

Description of the desired feature

Verde has utilities for selecting scattered spatial data based on windows and blocks (block_split and the new rolling_window). We don't really have any examples or tutorial

Hi, there is a small typo in the documentation of the function

reduce_to_pole

in fatiando.gravmag.transform. A square is missing in the numerator at

    RTP(k_x, k_y) = \frac{|k|}{
        a_1 k_x^2 + a_2 k_y^2 + a_3 k_x k_y +
        i|k|(b_1 k_x + b_2 k_y)}

(i.e, it should be \frac{|k|^2}{ ), same for the link

https://www.fatiando.org/api/gravmag.transform.html#fa

Function parameters like time_samples and timestamps are used in multiple places varyingly, but they mean the same thing. Secondly, many distributions don't accept sampling frequency as an input. This needs to be clarified in all generators and processors.

The global fitting problem seems to have become quite popular, but with it come some issues, such as this new one on SO: https://stackoverflow.com/questions/58341121/global-fitting-with-symfit-typestructure-of-dataset/58353356?noredirect=1#comment103098637_58353356

The documentation should be updated to warn people of certain issues when using many component models and a great many datasets.