git config filter.strip-notebook-output.clean 'jupyter nbconvert --ClearOutputPreprocessor.enabled=True --to=notebook --stdin --stdout --log-level=ERROR'
Create a .gitattributes file inside the directory with the notebooks
Add the following to that file:
| http://nadbordrozd.github.io/blog/2016/05/20/text-classification-with-word2vec/ | |
| https://blog.keras.io/using-pre-trained-word-embeddings-in-a-keras-model.html | |
| https://stackoverflow.com/questions/31321209/doc2vec-how-to-get-document-vectors | |
| http://linanqiu.github.io/2015/10/07/word2vec-sentiment/ | |
| https://rare-technologies.com/doc2vec-tutorial/ | |
| https://radimrehurek.com/gensim/models/doc2vec.html#gensim.models.doc2vec.TaggedDocument | |
| https://www.kaggle.com/tj2552/sentiment-classification-in-5-classes-doc2vec | |
| https://github.com/ibrahimsharaf/Doc2vec |
| # USAGE | |
| # python build_face_dataset.py --cascade haarcascade_frontalface_default.xml --output dataset/adrian | |
| # import the necessary packages | |
| from imutils.video import VideoStream | |
| import argparse | |
| import imutils | |
| import time | |
| import cv2 | |
| import os |
| # USAGE | |
| # python face_detection.py --image face1.jpg | |
| # import the necessary packages | |
| # from imutils import face_utils | |
| # import numpy as np | |
| import argparse | |
| import imutils | |
| import dlib | |
| import cv2 |
| '''This script goes along the blog post | |
| "Building powerful image classification models using very little data" | |
| from blog.keras.io. | |
| It uses data that can be downloaded at: | |
| https://www.kaggle.com/c/dogs-vs-cats/data | |
| In our setup, we: | |
| - created a data/ folder | |
| - created train/ and validation/ subfolders inside data/ | |
| - created cats/ and dogs/ subfolders inside train/ and validation/ | |
| - put the cat pictures index 0-999 in data/train/cats |
| # Implementation of a simple MLP network with one hidden layer. Tested on the iris data set. | |
| # Requires: numpy, sklearn>=0.18.1, tensorflow>=1.0 | |
| # NOTE: In order to make the code simple, we rewrite x * W_1 + b_1 = x' * W_1' | |
| # where x' = [x | 1] and W_1' is the matrix W_1 appended with a new row with elements b_1's. | |
| # Similarly, for h * W_2 + b_2 | |
| import tensorflow as tf | |
| import numpy as np | |
| from sklearn import datasets | |
| from sklearn.model_selection import train_test_split |
| #!/usr/bin/env PYTHONIOENCODING="utf-8" python | |
| """ | |
| A simple neural network learning the XOR function | |
| """ | |
| import tensorflow as tf | |
| sess = tf.InteractiveSession() | |
| # Desired input output mapping of XOR function: | |
| x_ = [[0, 0], [0, 1], [1, 0], [1, 1]] # input | |
| #labels=[0, 1, 1, 0] # output => |
Now located at https://github.com/JeffPaine/beautiful_idiomatic_python.
Github gists don't support Pull Requests or any notifications, which made it impossible for me to maintain this (surprisingly popular) gist with fixes, respond to comments and so on. In the interest of maintaining the quality of this resource for others, I've moved it to a proper repo. Cheers!
Author: Josef Jezek
sudo apt-get install python-setuptools
| from cvxopt import matrix as cvxmat, sparse, spmatrix | |
| from cvxopt.solvers import qp, options | |
| import numpy as np | |
| def quadprog(H, f, Aeq, beq, lb, ub): | |
| """ | |
| minimize: | |
| (1/2)*x'*H*x + f'*x | |
| subject to: | |
| Aeq*x = beq |