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@yegappan
yegappan / VimScriptForPythonDevelopers.MD
Last active June 30, 2025 14:54
Vim script for Python Developers

Vim Script for Python Developers

This is a guide to Vim Script development for Python developers. Sample code for the various expressions, statements, functions and programming constructs is shown in both Python and Vim Script. This is not intended to be a tutorial for developing Vim scripts. It is assumed that the reader is familiar with Python programming.

For an introduction to Vim Script development, refer to usr_41.txt, eval.txt and Learn Vimscript the Hard Way

For a guide similar to this one for JavaScript developers, refer to Vim Script for the JavaScripter

This guide only describes the programming constructs that are present in both Python and Vim. The constructs that are unique to Vim (e.g. autocommands, [key-mapping](https://vimhelp.org/map.txt.html#key-m

@johnhw
johnhw / umap_sparse.py
Last active May 11, 2025 07:18
1 million prime UMAP layout
### JHW 2018
import numpy as np
import umap
# This code from the excellent module at:
# https://stackoverflow.com/questions/4643647/fast-prime-factorization-module
import random
@kmjjacobs
kmjjacobs / gru_tensorflow.py
Last active August 14, 2022 17:10
GRU (Gated Recurrent Unit) implementation in TensorFlow and used in a simple Machine Learning task. The corresponding tutorial is found on Data Blogger: https://www.data-blogger.com/2017/08/27/gru-implementation-tensorflow/.
#%% (0) Important libraries
import tensorflow as tf
import numpy as np
from numpy import random
import matplotlib.pyplot as plt
from IPython import display
% matplotlib inline
#%% (1) Dataset creation.
@EncodeTS
EncodeTS / keras VGG-Face Model.md
Last active February 19, 2024 06:56
VGG-Face model for keras

VGG-Face model for Keras

This is the Keras model of VGG-Face.

It has been obtained through the following method:

  • vgg-face-keras:directly convert the vgg-face matconvnet model to keras model
  • vgg-face-keras-fc:first convert vgg-face caffe model to mxnet model,and then convert it to keras model

Details about the network architecture can be found in the following paper: