AK391

# launch your own Gradio Web Demo of Arcane style transfer by following the steps below
# open a jupyter notebook, code editor (vs code etc), or google colab
# pip install gradio
# copy the code below into a file or cell in a python notebook and run it
# that's it, a web demo will appear in your python notebook or web browser
# github: https://github.com/jjeamin/anime_style_transfer_pytorch
# HF blog: https://huggingface.co/blog/gradio-spaces

import gradio as gr

sbalnojan

import s3fs
import pickle
import json
import numpy as np

BUCKET_NAME = "my-bucket"

# definitions, keras/tf/... imports...

if __name__ == "__main__":

cjw85

"""
Reimplementation of nanonet using keras.

Follow the instructions at
https://www.tensorflow.org/install/install_linux
to setup an NVIDIA GPU with CUDA8.0 and cuDNN v5.1.

virtualenv venv --python=python3
. venv/bin/activate
pip install numpy

dave-andersen

import tensorflow as tf
import numpy as np
import time

N=10000
K=4
MAX_ITERS = 1000

start = time.time()

genekogan

Instructions for making a Neural-Style movie

The following instructions are for creating your own animations using the style transfer technique described by Gatys, Ecker, and Bethge, and implemented by Justin Johnson. To see an example of such an animation, see this video of Alice in Wonderland re-styled by 17 paintings.

Setting up the environment

The easiest way to set up the environment is to simply load Samim's a pre-built Terminal.com snap or use another cloud service like Amazon EC2. Unfortunately the g2.2xlarge GPU instances cost $0.99 per hour, and depending on parameters selected, it may take 10-15 minutes to produce a 512px-wide image, so it can cost $2-3 to generate 1 sec of video at 12fps.

If you do load the

TomAugspurger

fonnesbeck

Horizons in Probabilistic Programming and Bayesian Analysis

Representations:

• Hierarchical models
• Hidden Markov models
• Graphical models
• Non-parametric Bayes (distributions over functions)

Inference Approaches:

robertsdionne

#!/usr/bin/env bash

# Assuming OS X Yosemite 10.10.4

# Install XCode and command line tools
# See https://itunes.apple.com/us/app/xcode/id497799835?mt=12#
# See https://developer.apple.com/library/mac/documentation/Darwin/Reference/ManPages/man1/xcode-select.1.html
xcode-select --install

djhocking

one <- seq(1:10)
two <- rnorm(10)
three <- runif(10, 1, 2)
four <- -10:-1

df <- data.frame(one, two, three)
df2 <- data.frame(one, two, three, four)

str(df)

Chaser324

Whether you're trying to give back to the open source community or collaborating on your own projects, knowing how to properly fork and generate pull requests is essential. Unfortunately, it's quite easy to make mistakes or not know what you should do when you're initially learning the process. I know that I certainly had considerable initial trouble with it, and I found a lot of the information on GitHub and around the internet to be rather piecemeal and incomplete - part of the process described here, another there, common hangups in a different place, and so on.

In an attempt to coallate this information for myself and others, this short tutorial is what I've found to be fairly standard procedure for creating a fork, doing your work, issuing a pull request, and merging that pull request back into the original project.

Creating a Fork

Just head over to the GitHub page and click the "Fork" button. It's just that simple. Once you've done that, you can use your favorite git client to clone your repo or j