danawoodman

React "Hello World" Examples

Below are a small collection of React examples to get anyone started using React. They progress from simpler to more complex/full featured.

They will hopefully get you over the initial learning curve of the hard parts of React (JSX, props vs. state, lifecycle events, etc).

Usage

You will want to create an index.html file and copy/paste the contents of 1-base.html and then create a scripts.js file and copy/paste the contents of one of the examples into it.

hrbrmstr

library(doParallel)

rpts <- list(list(out="one.html", params=list(some_var="One")),
             list(out="two.html", params=list(some_var="Two")),
             list(out="three.html", params=list(some_var="Three")),
             list(out="four.html", params=list(some_var="Four")))

do_rpt <- function(r) {

  require(rmarkdown)

chrisguttandin

What else can we do with the Web Audio API?

Of course the Web Audio API is meant for synthesizing and processing audio data. It is tailored for that use case. But at least in our digital world audio data is just a series of numbers, which are typically somewhere between +1 and -1. So why can't we use the Web Audio API for general computations?

Almost a year ago I had the pleasure to give a talk at the Web Audio Conference in Atlanta. The conference featured a lot of great talks, which I really appreciated as an attendee. However, as a speaker it was tough to reduce my own talk until it was short enough to fit into the schedule. I had the feeling that I had to rush through my slides. Since then I planned to write down my findings in a more detailed way, but I never got around to it. Luckily I was asked to repeat my talk at our local Web Audio Meetup here in

fauxneticien

import pandas as pd

from collections import Counter
from tqdm.contrib.concurrent import process_map

def get_vocab(texts_list, ids_list=None):
    
    def sum_counters(counter_list):

        '''

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