thundergolfer

Me On Reddit.com / [Your Username]

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meonreddit.com

Some Reddit users are fantastic and dedicated contributors to subreddits, but don't have a way to easily communicate it. Once you connect your profile on meonreddit.com, the site will display your profile with only professionally oriented content.

Here's an example for me. I'm no guru.

How It Works

thundergolfer

import arrow
import praw
import collections

import argparse
import logging

parser = argparse.ArgumentParser(description='Manage relative comment ranking.')
parser.add_argument("-v", "--verbose", help="enable logging", action="store_true")

thundergolfer

import praw
import arrow
import heapq

reddit = praw.Reddit(client_id='51SqtrEzmCTnuA',
                     client_secret='xehsxI2Sm81wHZpSa_PcmKcv5_k',
                     user_agent='me-on-reddit')

# for submission in reddit.subreddit('samharris').hot(limit=10):
    # if(len(submission.comments) < 10):

thundergolfer

import pandas as pd

def union(a, b):
    return list(set(a) | set(b))

def get_freq(A):
	vals = {}
	for a in A:
		if a in vals:
			vals[a] += 1.0

thundergolfer

import tensorflow as tf

""" NOTES:
           Currently this class has a "calculate" static method that is used to calculate cosine distances given two 
           Tensorflow tensors. Calculating distances for *multiple* vector pairs is conceptually incompatible with this
           class. 
           
           This class is an object that wraps the creation/calculation of *one* Cosine Distance measure. It was intended to 
           be used in a situation like this. 
           

thundergolfer

Keybase proof

I hereby claim:

• I am thundergolfer on github.
• I am thundergolfer (https://keybase.io/thundergolfer) on keybase.
• I have a public key ASD1RjXvPOp4x6lkAc5IZ8WhVl53xX2xLPlDKapEwJwNUQo

To claim this, I am signing this object:

thundergolfer

Hey r/learnmachinelearning

I'm writing this short post in response to this infographic post, The 4 Stages of Machine Learning. I basically replied to it saying that it encapsulates reasonably well ML in a research context, but not so much the greater problem of production ML systems. People asked me to expand on that so here it is.

What's my experience with production ML? Pretty limited, but my very short time in it has been eye-opening. I started a year long data engineering internship with Zendesk's ML product team in November 2016. It's the team that posted Serving Tensorflow in Production at Zendesk recently. Our product is [Automatic Answers](https://www.zend

thundergolfer

# coding: utf-8

# In[35]:

import argparse
import html
import json
import requests
from requests.utils import quote

thundergolfer

AGENTS

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AGENTS - PERSONAL

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• amazon alexa
• Cortana
• Allo
• Facebook
• Siri
• Replika

thundergolfer

Binary Orders of Magnitude - A journey from the indivisible to the incomprehensible

2⁰ - 1 bit (b)

2¹ - 1 crumb

2² - 1 nibble,semioctet,halfbyte

2³ - 1 byte (B), octet