shagunsodhani

A Few Useful Things to Know about Machine Learning

The paper presents some key lessons and "folk wisdom" that machine learning researchers and practitioners have learnt from experience and which are hard to find in textbooks.

1. Learning = Representation + Evaluation + Optimization

All machine learning algorithms have three components:

• Representation for a learner is the set if classifiers/functions that can be possibly learnt. This set is called hypothesis space. If a function is not in hypothesis space, it can not be learnt.
• Evaluation function tells how good the machine learning model is.
• Optimisation is the method to search for the most optimal learning model.

EtzAlex

shotData<- c(1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0,
             1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1,
             0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
             1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0)

#figure 1 from blog, likelihood curve for 58/100 shots

x = seq(.001, .999, .001) ##Set up for creating the distributions
y2 = dbeta(x, 1 + 58, 1 + 42) # data for likelihood curve, plotted as the posterior from a beta(1,1)

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.

adewes

To use this bot:

• Download ads_bot.py and requirements.txt.
• Type pip install -r requirements.txt to install the requirements.
• Fill out the required information in the Python file.
• Ideally, create a (free) Slack account and set up a web hook to receive notifications from the bot.
• Run the script :)
• Relax and be ready to answer incoming calls :D

rrees

    base_data_sources = make_dict({
        'business': BusinessDataSource(clientUS),
        'money': USMoneyDataSource(clientUS),
        'technology': TechnologyDataSource(clientUS),
        'sport': SportUSDataSource(clientUS),
        'comment': CommentIsFreeDataSource(clientUS),
        'culture': CultureDataSource(clientUS),
        'top_stories': TopStoriesDataSource(clientUS),
        'video': VideoDataSource(clientUS),
    })

peterkuma

#!/bin/python

import os
from flask import Flask, Response, request, abort, render_template_string, send_from_directory
import Image
import StringIO

app = Flask(__name__)

WIDTH = 1000

lebedov

#!/usr/bin/env python

"""
Allocate a GPUArray in one process and access it in another process using IPC
handles.
"""

import multiprocessing as mp
import numpy as np
import zmq

AlexandreAbraham

""" Unsupervised evaluation metrics. """

# License: BSD Style.

from itertools import combinations
import numpy as np

from sklearn.utils import check_random_state
from sklearn.metrics.pairwise import distance_metrics
from sklearn.metrics.pairwise import pairwise_distances

jboner

Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD