| # https://github.com/onnx/models/tree/master/vision/body_analysis/emotion_ferplus | |
| require "onnxruntime" | |
| require "mini_magick" | |
| img = MiniMagick::Image.open("ranger.jpg") | |
| img.crop "100x100+60+20", "-gravity", "center" | |
| img.resize "64x64^", "-gravity", "center", "-extent", "64x64" | |
| img.colorspace "Gray" | |
| img.write("resized.jpg") |
Collection of License badges for your Project's README file.
This list includes the most common open source and open data licenses.
Easily copy and paste the code under the badges into your Markdown files.
Translations: (No guarantee that the translations are up-to-date)
| from sklearn.datasets import fetch_20newsgroups | |
| from sklearn.feature_extraction.text import TfidfVectorizer | |
| from sklearn.linear_model import LogisticRegression | |
| from sklearn.pipeline import Pipeline | |
| import pandas as pd | |
| import numpy as np | |
| # Grab just two categories from the 20 newsgroups dataset | |
| categories=['sci.space', 'rec.autos'] |
This is the "Iris" dataset. Originally published at UCI Machine Learning Repository: Iris Data Set, this small dataset from 1936 is often used for testing out machine learning algorithms and visualizations (for example, Scatter Plot). Each row of the table represents an iris flower, including its species and dimensions of its botanical parts, sepal and petal, in centimeters.
The HTML page provides the basic code required to load the data and display it on the page (as JSON) using D3.js.
For a more up to date code example with React & D3, see (VizHub: Stylized Scatter Plot)[https://vizhub.com/curran/3d631093c2334030a6b27fa979bb4a0d?edit=files&file=index.js].
| // Reference: http://www.blackdogfoundry.com/blog/moving-repository-from-bitbucket-to-github/ | |
| // See also: http://www.paulund.co.uk/change-url-of-git-repository | |
| $ cd $HOME/Code/repo-directory | |
| $ git remote rename origin bitbucket | |
| $ git remote add origin https://github.com/mandiwise/awesome-new-repo.git | |
| $ git push origin master | |
| $ git remote rm bitbucket |
| """Information Retrieval metrics | |
| Useful Resources: | |
| http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt | |
| http://www.nii.ac.jp/TechReports/05-014E.pdf | |
| http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf | |
| http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf | |
| Learning to Rank for Information Retrieval (Tie-Yan Liu) | |
| """ | |
| import numpy as np |
| <!doctype html> | |
| <title>Site Maintenance</title> | |
| <style> | |
| body { text-align: center; padding: 150px; } | |
| h1 { font-size: 50px; } | |
| body { font: 20px Helvetica, sans-serif; color: #333; } | |
| article { display: block; text-align: left; width: 650px; margin: 0 auto; } | |
| a { color: #dc8100; text-decoration: none; } | |
| a:hover { color: #333; text-decoration: none; } | |
| </style> |
