tsiege

ANNOUNCEMENT

I have moved this over to the Tech Interview Cheat Sheet Repo and has been expanded and even has code challenges you can run and practice against!






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rtt

Tinder API documentation

Note: this was written in April/May 2014 and the API may has definitely changed since. I have nothing to do with Tinder, nor its API, and I do not offer any support for anything you may build on top of this. Proceed with caution

http://rsty.org/

I've sniffed most of the Tinder API to see how it works. You can use this to create bots (etc) very trivially. Some example python bot code is here -> https://gist.github.com/rtt/5a2e0cfa638c938cca59 (horribly quick and dirty, you've been warned!)

tylerneylon

--[[ json.lua

A compact pure-Lua JSON library.
The main functions are: json.stringify, json.parse.

## json.stringify:

This expects the following to be true of any tables being encoded:
 * They only have string or number keys. Number keys must be represented as
   strings in json; this is part of the json spec.

bsweger

Useful Pandas Snippets

A personal diary of DataFrame munging over the years.

Data Types and Conversion

Convert Series datatype to numeric (will error if column has non-numeric values)
(h/t @makmanalp)

nylki

do androids dream of cooking?

The following recipes are sampled from a trained neural net. You can find the repo to train your own neural net here: https://github.com/karpathy/char-rnn Thanks to Andrej Karpathy for the great code! It's really easy to setup.

The recipes I used for training the char-rnn are from a recipe collection called ffts.com And here is the actual zipped data (uncompressed ~35 MB) I used for training. The ZIP is also archived @ archive.org in case the original links becomes invalid in the future.

jakevdp

# By Jake VanderPlas
# License: BSD-style

import matplotlib.pyplot as plt
import numpy as np


def discrete_cmap(N, base_cmap=None):
    """Create an N-bin discrete colormap from the specified input map"""

fyears

if you are using linux, unix, os x:

pip install -U setuptools
pip install -U pip

pip install numpy
pip install scipy
pip install matplotlib
#pip install PySide

fchollet

'''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

baraldilorenzo

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

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

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

zaemiel

1. Go to Sublime Text to: Tools -> Build System -> New Build System 
and put the next lines:

{
    "cmd": ["python3", "-i", "-u", "$file"],
    "file_regex": "^[ ]File \"(...?)\", line ([0-9]*)",
    "selector": "source.python"
}

Then save it with a meaningful name like: python3.sublime-build