minrk

#!/usr/bin/env python
"""strip outputs from an IPython Notebook

Opens a notebook, strips its output, and writes the outputless version to the original file.

Useful mainly as a git filter or pre-commit hook for users who don't want to track output in VCS.

This does mostly the same thing as the `Clear All Output` command in the notebook UI.

LICENSE: Public Domain

kevin-smets

Default

Powerlevel10k

cfobel

GIMP Palette
Name: color-brewer-qualitative
Columns: 3
# This color palette combines the following three palletes developed
# by Cynthia Brewer (see http://colorbrewer2.org/):
#  - Dark2_8.gpl
#  - Pastel2_8.gpl
#  - Set2_8.gpl
179 226 205  light_cyan
253 205 172  light_orange

alexsavio

"""
Some python code for
Markov Chain Monte Carlo and Gibs sampling
by Bruce Walsh
"""


import numpy as np
import numpy.linalg as npla

pbugnion

This gist lets you keep IPython notebooks in git repositories. It tells git to ignore prompt numbers and program outputs when checking that a file has changed.

To use the script, follow the instructions given in the script's docstring.

For further details, read this blogpost.

The procedure outlined here is inspired by this answer on Stack Overflow.

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

thomasp85

library(ggraph)
library(gganimate)
library(igraph)
# Data from http://konect.uni-koblenz.de/networks/sociopatterns-infectious
infect <- read.table('out.sociopatterns-infectious', skip = 2, sep = ' ', stringsAsFactors = FALSE)
infect$V3 <- NULL
names(infect) <- c('from', 'to', 'time')
infect$timebins <- as.numeric(cut(infect$time, breaks = 100))

# We want that nice fading effect so we need to add extra data for the trailing

shagunsodhani

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

Introduction

• The paper introduces a novel technique to explain the predictions of any classifier in an interpretable and faithful manner.
• It also proposes a method to explain models by obtaining representative individual predictions and their explanations.
• Link to the paper
• Demo

Desired Characteristics for Explanations

tomfa

# This file is your Lambda function
import json
import boto3


def save_to_bucket(event, context):
    AWS_BUCKET_NAME = 'my-bucket-name'
    s3 = boto3.resource('s3')
    bucket = s3.Bucket(AWS_BUCKET_NAME)
    path = 'my-path-name.txt'

gilrosenthal

!pip install fastai
!apt-get -qq install -y libsm6 libxext6 && pip install -q -U opencv-python
import cv2
from os import path
from wheel.pep425tags import get_abbr_impl, get_impl_ver, get_abi_tag
platform = '{}{}-{}'.format(get_abbr_impl(), get_impl_ver(), get_abi_tag())

accelerator = 'cu80' if path.exists('/opt/bin/nvidia-smi') else 'cpu'

!pip install -q http://download.pytorch.org/whl/{accelerator}/torch-0.3.0.post4-{platform}-linux_x86_64.whl torchvision