nicolewhite

From SQL to Neo4j: Northwind

SQL Model

Neo4j Model

Get the SQL Dump

The SQL dump was stolen from here and imported into MySQL.

honnibal

"""A stripped-down MLP example, using Theano.

Based on the tutorial here: http://deeplearning.net/tutorial/mlp.html

This example trims away some complexities, and makes it easier to see how Theano works.

Design changes:

* Model compiled in a distinct function, so that symbolic variables are not in run-time scope.
* No classes. Network shown by chained function calls.

Marak

module['exports'] = function simpleHttpRequest (hook) {
  // npm modules available, see: http://hook.io/modules
  var request = require('request');
  request.get('http://httpbin.org/ip', function(err, res, body){
    if (err) {
      return hook.res.end(err.messsage);
    }
    hook.res.end(body);
  })
};

kylemcdonald

udibr

# variation to https://github.com/ryankiros/skip-thoughts/blob/master/decoding/search.py

def keras_rnn_predict(samples, empty=empty, rnn_model=model, maxlen=maxlen):
    """for every sample, calculate probability for every possible label
    you need to supply your RNN model and maxlen - the length of sequences it can handle
    """
    data = sequence.pad_sequences(samples, maxlen=maxlen, value=empty)
    return rnn_model.predict(data, verbose=0)

def beamsearch(predict=keras_rnn_predict,

rocknrollnerd

import theano
import theano.tensor as T
from theano.tensor.shared_randomstreams import RandomStreams
from theano.sandbox.rng_mrg import MRG_RandomStreams
from lasagne.updates import adam
from lasagne.utils import collect_shared_vars

from sklearn.datasets import fetch_mldata
from sklearn.cross_validation import train_test_split
from sklearn import preprocessing

udibr

import numpy as np
from keras.layers import GRU, initializations, K
from collections import OrderedDict

class GRULN(GRU):
    '''Gated Recurrent Unit with Layer Normalization
    Current impelemtation only works with consume_less = 'gpu' which is already
    set.

    # Arguments

wassname

"""
A keras attention layer that wraps RNN layers.

Based on tensorflows [attention_decoder](https://github.com/tensorflow/tensorflow/blob/c8a45a8e236776bed1d14fd71f3b6755bd63cc58/tensorflow/python/ops/seq2seq.py#L506) 
and [Grammar as a Foreign Language](https://arxiv.org/abs/1412.7449).

date: 20161101
author: wassname
url: https://gist.github.com/wassname/5292f95000e409e239b9dc973295327a
"""

parse.rs

use cpython::{PyResult, Python, PyObject, ObjectProtocol, PyErr, exc};

py_module_initializer!(fastdtparse, initfastdtparse, PyInit_fastdtparse, |py, m| {
    try!(m.add(py, "__doc__", "This module is implemented in Rust."));
    try!(m.add(py, "parse_isoformat", py_fn!(py, parse_isoformat_py(datestr: &str))));
    Ok(())
});

fn parse_isoformat(datestr: &str) -> Result<(i32, u8, u8, u8, u8, u8), &'static str> {
    if datestr.len() < 19 {

yossorion

What I Wish I'd Known About Equity Before Joining A Unicorn

Disclaimer: This piece is written anonymously. The names of a few particular companies are mentioned, but as common examples only.

This is a short write-up on things that I wish I'd known and considered before joining a private company (aka startup, aka unicorn in some cases). I'm not trying to make the case that you should never join a private company, but the power imbalance between founder and employee is extreme, and that potential candidates would