Data Science
Data science is an inter-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge from structured and unstructured data. Data scientists perform data analysis and preparation, and their findings inform high-level decisions in many organizations.
Here are 18,636 public repositories matching this topic...
Describe the issue linked to the documentation
Videos on contributing to scikit-learn reference were created prior to renaming the branch name, and so refer to master, not main
Suggest a potential alternative/fix
In this file, under "Video Resources" section: https://github.com/scikit-learn/scikit-learn/blob/d01014c3198aafa336d9f8ed306f292d68c4a886/doc/developers/contributing.r
-
Updated
May 1, 2021 - Jupyter Notebook
-
Updated
Mar 17, 2021 - Jupyter Notebook
-
Updated
Feb 18, 2021 - Python
-
Updated
May 6, 2021 - Python
-
Updated
Apr 29, 2021 - Python
@richardliaw could we take in the data path as a command line arg for all these examples that defaults to "~/data"? That way the user can specify their own data paths without having to modify the code.
Originally posted by @amogkam in ray-project/ray#15260 (comment)
-
Updated
Apr 30, 2021
%cpaste? claims "IPython statements (magics, shell escapes) are not supported (yet)." But at least magics apparently are!
In recent versions (can't say from exactly when), there seems to be an off-by-one error in dcc.DatePickerRange. I set max_date_allowed = datetime.today().date(), but in the calendar, yesterday is the maximum date allowed. I see it in my apps, and it is also present in the first example on the DatePickerRange documentation page.
E
Summary
When import streamlit and supervisely_lib together in a project there occurs a TypeError.
Steps to reproduce
Code snippet:
import streamlit as st
import supervisely_lib as sly
If applicable, please provide the steps we should take to reproduce the bug:
- run the code with streamlit run ...
- see error/traceback when you open the streamlit page
🚀 Feature
Motivation
This should work. Add a test to make sure this works reliably in multi-gpu setting.
class MyModel(pl.LightningModule):
def on_training_epoch_start():
# calling trainer.predict inside trainer.fit routine.
# is this okay?
pred_outputs = self.trainer.pre-
Updated
Apr 28, 2021 - Jupyter Notebook
Not a high-priority at all, but it'd be more sensible for such a tutorial/testing utility corpus to be implemented elsewhere - maybe under /test/ or some other data- or doc- related module – rather than in gensim.models.word2vec.
Originally posted by @gojomo in RaRe-Technologies/gensim#2939 (comment)
-
Updated
May 20, 2020
-
Updated
May 2, 2021
-
Updated
Oct 16, 2020 - Jupyter Notebook
-
Updated
Mar 15, 2021
-
Updated
Apr 16, 2021 - JavaScript
-
Updated
May 6, 2021 - Python
-
Updated
Apr 20, 2021
-
Updated
May 1, 2021
-
Updated
May 5, 2021 - Python
-
Updated
May 6, 2021 - Python
I'm using mxnet to do some work, but there is nothing when I search the mxnet trial and example.
-
Updated
Jan 25, 2021 - Python
-
Updated
Apr 4, 2021 - Jupyter Notebook
- Wikipedia
- Wikipedia

(e.g. for links and images), because some of these examples are now being rendered in the docs.
Added by @fchollet in requests for contributions.