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Machine learning is the practice of teaching a computer to learn. The concept uses pattern recognition, as well as other forms of predictive algorithms, to make judgments on incoming data. This field is closely related to artificial intelligence and computational statistics.

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This repositary is a combination of different resources lying scattered all over the internet. The reason for making such an repositary is to combine all the valuable resources in a sequential manner, so that it helps every beginners who are in a search of free and structured learning resource for Data Science. For Constant Updates Follow me in Twitter.

  • Updated Feb 10, 2021
BenikaHall commented Feb 10, 2021

Describe the bug
After applying the unstack function, the variable names change to numeric format.

Steps/Code to reproduce bug

def get_df(length, num_cols, num_months, acc_offset):
    cols = [ 'var_{}'.format(i) for i in range(num_cols)]
    df = cudf.DataFrame({col: cupy.random.rand(length * num_months) for col in cols})
    df['acc_id'] = cupy.repeat(cupy.arange(length), nu
evelynmitchell commented Oct 9, 2020 includes a link to the assets/igel-help.gif, but that path is broken on readthedocs.

readme.rst is included as ../readme.rst in the sphinx build.
The gifs are in asses/igel-help.gif

The sphinx build needs to point to the asset directory, absolutely:

.. image:: /assets/igel-help.gif

I haven't made a patch, because I haven't

beckernick commented Mar 1, 2021

confusion_matrix should automatically convert dtypes as appropriate in order to avoid failing, like other metric functions.

from sklearn.metrics import confusion_matrix
import numpy as np
import cumly = np.array([0.0, 1.0, 0.0])
y_pred = np.array([0.0, 1.0, 1.0])
print(confusion_matrix(y, y_pred))
cuml.metrics.confusion_matrix(y, y_pred)
[[1 1]
 [0 1]]
adocherty commented Nov 27, 2019


Currently our unit tests are disorganized and each test creates example StellarGraph graphs in different or similar ways with no sharing of this code.

This issue is to improve the unit tests by making functions to create example graphs available to all unit tests by, for example, making them pytest fixtures at the top level of the tests (see

StrikerRUS commented Oct 18, 2019

I'm sorry if I missed this functionality, but CLI version hasn't it for sure (I saw the related code only in I guess it will be very useful to eliminate copy-paste phase, especially for large models.

Of course, piping is a solution, but not for development in Jupyter Notebook, for example.

ssimontacchi commented Jun 20, 2020

Hi, Thanks for the awesome library!

So I am running a Kmeans on lots of different datasets, which all have roughly four shapes, so I initialize with those shapes and it works well, except for just a few times. There are a few datasets that look different enough that I end up with empty clusters and the algorithm just hangs ("Resumed because of empty cluster" again and again).

I conceptually