stevedonovan

package csvdata
// csvdata complements the csv package by allowing you to map a custom structure to
// the columns of data in a CSV file. The struct needs to be annotated so that each
// field can match a column in the data
//
//    type Person struct {
//       FirstName string `field:"First Name"`
//       Second_Name string
//       Age int
//    }

angeldm

package main

import (
	"container/heap"
	"fmt"
	"math/rand"
	"time"
)

const nRequester = 100

dominicsayers

First install Virtualbox

1. sudo pico /etc/apt/sources.list.d/virtualbox.list
2. Add deb http://download.virtualbox.org/virtualbox/debian saucy contrib non-free
3. wget -q http://download.virtualbox.org/virtualbox/debian/oracle_vbox.asc -O- | sudo apt-key add -
4. sudo apt-get update
5. sudo apt-get install dkms unzip
6. sudo apt-get install virtualbox-4.3 --no-install-recommends
7. wget http://download.virtualbox.org/virtualbox/4.3.6/Oracle_VM_VirtualBox_Extension_Pack-4.3.6.vbox-extpack
8. sudo VBoxManage extpack install Oracle_VM_VirtualBox_Extension_Pack-4.3.6.vbox-extpack

paulgb

'''
Convert Yelp Academic Dataset from JSON to CSV

Requires Pandas (https://pypi.python.org/pypi/pandas)

By Paul Butler, No Rights Reserved
'''

import json
import pandas as pd

jasonwbw

//  Copyright 2013 Google Inc. All Rights Reserved.
//
//  Licensed under the Apache License, Version 2.0 (the "License");
//  you may not use this file except in compliance with the License.
//  You may obtain a copy of the License at
//
//      http://www.apache.org/licenses/LICENSE-2.0
//
//  Unless required by applicable law or agreed to in writing, software
//  distributed under the License is distributed on an "AS IS" BASIS,

marekrei

import numpy as np
import os, sys, getopt

# Main path to your caffe installation
caffe_root = '/path/to/your/caffe/'

# Model prototxt file
model_prototxt = caffe_root + 'models/bvlc_googlenet/deploy.prototxt'

# Model caffemodel file

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)

shagunsodhani

Efficient Estimation of Word Representations in Vector Space

Introduction

• Introduces techniques to learn word vectors from large text datasets.
• Can be used to find similar words (semantically, syntactically, etc).
• Link to the paper
• Link to open source implementation

Model Architecture

maurelian

Start with these resources

• https://ethresear.ch/t/on-chain-scaling-to-potentially-500-tx-sec-through-mass-tx-validation/3477 - Vitalik
  • Despite this being a ZK focused approach, and only supporting deposit/withdraw/send/receive, it’s nicely laidout as a starting point
• https://ethresear.ch/t/minimal-viable-merged-consensus/5617 - John Adler
  • I think this is the first description of the optimistic model
• https://medium.com/@adlerjohn/the-why-s-of-optimistic-rollup-7c6a22cbb61a - John Adler
• This article focuses a bit less on the mechanics, and more on the beneficial properties.