Tatsh

#!/usr/bin/env python
from __future__ import print_function
import json
import sys

from authentication import authenticate
from future.moves.urllib.parse import urlencode

if __name__ == '__main__':
    username = sys.argv[1]

chaser92

# BEFORE STARTING PUT libcudnn5_5.1.10-1+cuda8.0_amd64.deb AND libcudnn5-dev_5.1.10-1+cuda8.0_amd64.deb IN /tmp
# OTHERWISE THIS WON'T WORK
# This has been tested only on EC2 P2 xlarge instance with 16 GB storage and stock Ubuntu 16.04
# It's a setup for a playground EC2 machine to perform workshops with Jupyter Notebook on GPU.
# It might contain some unnecessary crap
# The process takes like 15 minutes

pushd /tmp &&
stat libcudnn5_5.1.10-1+cuda8.0_amd64.deb &&
stat libcudnn5-dev_5.1.10-1+cuda8.0_amd64.deb &&

numpde

Reinforcement learning through imitation of successful peers

Introduction

Reinforcement learning is a mode of machine learning driven by the feedback from the environment on how good a string of actions of the learning agent turns out to be.

We consider here a reinforcement learning mechanism for neural networks

Bhavdip

###Sketch trial non stop

Open hosts files:

$ open /private/etc/hosts

Edit the file adding:

127.0.0.1 backend.bohemiancoding.com

127.0.0.1 bohemiancoding.sketch.analytics.s3-website-us-east-1.amazonaws.com

imadx

<style type="text/css">
	body{
		font-weight: 100;
	}
	div.displayContainer{
		background: #eee;
		padding: 10px;
		box-shadow: inset 0 0 3px -2px #000, inset 0 6px 6px -6px #000;
	}
	div.displayContainer h2{

ageitgey

Build tensorflow on OSX with NVIDIA CUDA support (GPU acceleration)

These instructions are based on Mistobaan's gist but expanded and updated to work with the latest tensorflow OSX CUDA PR.

Requirements

OS X 10.10 (Yosemite) or newer

vsooda

"""
This is a batched LSTM forward and backward pass
the comment is writen by karpathy, except the comment start with #sooda:
#sooda: will add some comment corresponding the equtions (ref: lstm.png)
"""
import numpy as np
import code

class LSTM:
  

glamp

Modifying an Existing Docker Image

To install a custom package or modify an existing docker image we need to

1. run a docker a container from the image we wish to modify
2. modify the docker container
3. commit the changes to the container as a docker image
4. test changes made to image

1.) Running a docker container from an image

hellpanderrr

from scipy.spatial import Delaunay, ConvexHull
import networkx as nx
 
points = [ [0,0],[0,50],[50,50],[50,0],[0,400],[0,450],[50,400],[50,450],[700,300],[700,350],[750,300],[750,350],
          [900,600],[950,650],[950,600],[900,650]
]
def concave(points,alpha_x=150,alpha_y=250):
    points = [(i[0],i[1]) if type(i) <> tuple else i for i in points]
    de = Delaunay(points)

staltz

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

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This tutorial as a series of videos

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

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