evancz

A vision for data interchange in Elm

How do you send information between clients and servers? What format should that information be in? What happens when the server changes the format, but the client has not been updated yet? What happens when the server changes the format, but the database cannot be updated?

These are difficult questions. It is not just about picking a format, but rather picking a format that can evolve as your application evolves.

Literature Review

By now there are many approaches to communicating between client and server. These approaches tend to be known within specific companies and language communities, but the techniques do not cross borders. I will outline JSON, ProtoBuf, and GraphQL here so we can learn from them all.

bitwalker

defmodule Config do
  @moduledoc """
  This module handles fetching values from the config with some additional niceties
  """

  @doc """
  Fetches a value from the config, or from the environment if {:system, "VAR"}
  is provided.

  An optional default value can be provided if desired.

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

kscottz

import cv2
import numpy as np
import time
import livestreamer

# use live streamer to figure out the stream info
streams = livestreamer.streams("http://www.twitch.tv/inostupid")
stream = streams['best']
# open our out file. 
fname = "test.mpg"

baraldilorenzo

##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

Botffy

package niochat;

import java.net.*;
import java.nio.*;
import java.nio.channels.*;
import java.io.IOException;
import java.util.*;

public class NiochatServer implements Runnable {
	private final int port;