PurpleBooth

Project Title

One Paragraph of project description goes here

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

nateGeorge

# taken from here: http://web.archive.org/web/20110527163743/https://svn.enthought.com/enthought/browser/sandbox/docs/coding_standard.py

""" This module is an example of the Enthought Python coding standards.

It was adapted from the Python Enhancement Proposal 8 (aka PEP 8) titled
'Style Guide for Python Code' (http://www.python.org/peps/pep-0008.html).

The first item in a module must be a documentation string (docstring).  The
first line of the docstring should be a one line summary.  If a more
detailed description is required, put an empty line before it.

fffaraz

from bs4 import BeautifulSoup
import requests
 
def getPlaylistLinks(url):
    sourceCode = requests.get(url).text
    soup = BeautifulSoup(sourceCode, 'html.parser')
    domain = 'https://www.youtube.com'
    for link in soup.find_all("a", {"dir": "ltr"}):
        href = link.get('href')
        if href.startswith('/watch?'):

thomaswieland

import imaplib2, time
from threading import *
 
# This is the threading object that does all the waiting on 
# the event
class Idler(object):
    def __init__(self, conn):
        self.thread = Thread(target=self.idle)
        self.M = conn
        self.event = Event()

veekaybee

Machine Learning Design Patterns

This book is all about patterns for doing ML. It's broken up into several key parts, building and serving. Both of these are intertwined so it makes sense to read through the whole thing, there are very many good pieces of advice from seasoned professionals. The parts you can safely ignore relate to anything where they specifically use GCP. The other issue with the book it it's very heavily focused on deep learning cases. Not all modeling problems require these. Regardless, let's dive in. I've included the stuff that was relevant to me in the notes.

Most Interesting Bullets:

• Machine learning models are not deterministic, so there are a number of ways we deal with them when building software, including setting random seeds in models during training and allowing for stateless functions, freezing layers, checkpointing, and generally making sure that flows are as reproducible as possib

josephmachado

import json
from fasthtml.common import * 

import requests
import logging
import sys

app, rt = fast_app(hdrs=(Script(src="https://cdn.plot.ly/plotly-2.32.0.min.js"),))

# Extract function