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pure-rgb / 4Data.md
Created November 24, 2018 15:50 — forked from innat/4Data.md
4D - Four Dimensional Data

Let's consider a folowing 4D matrix.

import numpy as np
W = np.random.randn(2,2,3,100)

print(W.shape)

output:
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pure-rgb / DotMultiply.md
Created November 24, 2018 15:50 — forked from innat/DotMultiply.md
In Python np.dot and np.multiply with np.sum
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pure-rgb / ImageCenter.md
Created November 24, 2018 15:51 — forked from innat/ImageCenter.md
Center Images in GitHub README.md

For left alignment

 <img align="left" width="600" height="200" src="https://www.python.org/python-.png">

For right alignment

<img align="right" width="600" height="200" src="https://www.python.org/python-.png">

And for center alignment

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pure-rgb / Delete Key.md
Created November 24, 2018 15:51 — forked from innat/Delete Key.md
Removing Key from Python Dictionary.

We can delete a key from a Python dictionary by the some following approaches.

Using the del keyword; it's almost the same approach like you did though -

     myDict = {'one': 100, 'two': 200, 'three': 300 }
     print(myDict)  # {'one': 100, 'two': 200, 'three': 300}
     if myDict.get('one') : del myDict['one']
     print(myDict)  # {'two': 200, 'three': 300}

Some GitHub Extensions that I use. They may also enable you to improve your productivity on GitHub.

  1. Octotree allows easy exploration of the source code within the browser through a panel on the left.

chrome-github

2 github-dashboard provides the ability to filter events on the Github.com activity dashboard.

i

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pure-rgb / OpenCV_3_Python_3x.md
Created November 24, 2018 15:53 — forked from innat/OpenCV_3_Python_3x.md
Installing OpenCV3 for Python3x

Supported Python versions

Python 2.7 is the only supported version in 2.x series. Python 3.x releases follow Numpy releases. For example Python 3.3 is no longer supported by Numpy so support for it has been dropped in opencv-python, too.

Currently, builds for following Python versions are provided:

  • 2.7
  • 3.4
  • 3.5

Step by step tuts to setup apache spark ( pyspark ) on linux and setup environment for deep learning with Apache Spark using Deep-Learning-Pipelines.

Step 1 : Install Python 3 and Jupyter Notebook

Run following command. Someone may need to install pip first or any missing packages may need to download.

sudo apt install python3-pip sudo pip3 install jupyter