dexhunter

<section data-background-transition='zoom' data-transition='concave' data-background='http://ryanjarvinen.com/presentations/shared/img/broadcast_reveal_dark.png' data-state='blackout'>
  <h2>Gist-Powered</h2>
  <h1>Reveal.js</h1>
  <h2>Slideshow Presentations</h2>
  <br/>
  <h1 class='fragment grow'><a style='color:deepskyblue;' href='http://gist-reveal.it'>gist-reveal.it</a></h1>
</section>
<section data-background-transition='zoom' data-transition='linear' id='try-it'>
  <h2>Try it out!</h2>
  <p>Create your own deck by forking a copy of <a href='https://gist.github.com/ryanj/af84d40e58c5c2a908dd'>this github gist</a>: <br /><a href='https://gist.github.com/ryanj/af84d40e58c5c2a908dd'>https://gist.github.com/ryanj/af84d40e58c5c2a908dd</a></p>

dexhunter

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

---

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.

---

dexhunter

Oh yes, this is nice:

git revert --strategy resolve <sha-ish>

Given a git log of A--B--C--D--E you can remove commit C using:

git revert --strategy resolve <sha-of-C> 

Which will produce:

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/*! = $rembase: 14px
--------------------------------------------------------------
* hmtl { font-size: 87.5%; }
* body { font-size: 14px; font-size: 1rem; line-height: 1; }
* 4px    	0.28571429rem
* 8px		0.571428571rem
* 12px		0.857142857rem
* 13px		0.928571429rem
* 14px		1rem
* 16px		1.142857143rem

dexhunter

import numpy as np
import pandas as pd


def compute_market_prices(prices):
    """Compute market prices according to the trading competition recipe.

    Parameters
    ----------
    prices : DataFrame

dexhunter

wordlist created from original 41G stash via:

grep -rohP '(?<=:).*$' | uniq > breachcompilation.txt

Then, compressed with:

7z a breachcompilation.txt.7z breachcompilation.txt

Size:

dexhunter

Universities

Massachusetts Institute of Technology (MIT)

###phd###

• MIT Graduate Admissions link
• PhD Program - MIT Sloan School of Management link
• MIT - Massachusetts Institute of Technology link
• to view contact information for all departments. - MIT link

dexhunter

#!/usr/bin/env python
from glob import glob                                                           
import cv2 
pngs = glob('./*.png')

for j in pngs:
    img = cv2.imread(j)
    cv2.imwrite(j[:-3] + 'jpg', img)

dexhunter

##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

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""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym

# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward