jaidevd

# List comprehension
x = [i for i in range(1, 21)]
y = [i ** 2 for i in x]
y = [i ** 2 for i in range(1, 21)]


# Median of a list of numbers
def median(x):
    x.sort()
    L = len(x)

jaidevd

let g:python2_host_prog = '/home/jaidevd/anaconda3/bin/python'

call plug#begin('~/.local/share/nvim/plugged')

" Autocomplete stuff
if has('nvim')
  Plug 'Shougo/deoplete.nvim', { 'do': ':UpdateRemotePlugins' }
else
  Plug 'Shougo/deoplete.nvim'
  Plug 'roxma/nvim-yarp'

jaidevd

{
  "$schema": "https://vega.github.io/schema/vega/v5.json",
  "description": "An example of treemap layout for hierarchical data.",
  "width": 960,
  "height": 500,
  "padding": 2.5,
  "autosize": "none",

  "scales": [
    {

jaidevd

[{"temp":39.2,"date":"2010-01-01","time":"1:00 am"},{"temp":39.0,"date":"2010-01-01","time":"2:00 am"},{"temp":38.9,"date":"2010-01-01","time":"3:00 am"},{"temp":38.8,"date":"2010-01-01","time":"4:00 am"},{"temp":38.7,"date":"2010-01-01","time":"5:00 am"},{"temp":38.7,"date":"2010-01-01","time":"6:00 am"},{"temp":38.6,"date":"2010-01-01","time":"7:00 am"},{"temp":38.7,"date":"2010-01-01","time":"8:00 am"},{"temp":39.2,"date":"2010-01-01","time":"9:00 am"},{"temp":40.1,"date":"2010-01-01","time":"10:00 am"},{"temp":41.3,"date":"2010-01-01","time":"11:00 am"},{"temp":42.5,"date":"2010-01-01","time":"Noon"},{"temp":43.2,"date":"2010-01-01","time":"1:00 pm"},{"temp":43.5,"date":"2010-01-01","time":"2:00 pm"},{"temp":43.3,"date":"2010-01-01","time":"3:00 pm"},{"temp":42.7,"date":"2010-01-01","time":"4:00 pm"},{"temp":41.7,"date":"2010-01-01","time":"5:00 pm"},{"temp":41.2,"date":"2010-01-01","time":"6:00 pm"},{"temp":40.9,"date":"2010-01-01","time":"7:00 pm"},{"temp":40.7,"date":"2010-01-01","time":"8:00 pm"},{"te

jaidevd

jaidevd

{
  "$schema": "https://vega.github.io/schema/vega/v5.json",
  "description": "A heatmap showing average daily temperatures in Seattle for each hour of the day.",
  "width": 800,
  "height": 500,
  "padding": 5,

  "title": {
    "text": "Seattle Annual Temperatures",
    "anchor": "middle",

jaidevd

jaidevd

{
  "$schema": "https://vega.github.io/schema/vega/v4.json",
  "description": "https://gramener.invisionapp.com/d/main/#/console/7709561/326727774/preview",
  "width": 495,
  "height": 320,
  "autosize": "fit",
  "padding": {
    "left": 5,
    "top": 25,
    "right": 100,

jaidevd

#!/usr/bin/env python
# coding: utf-8

# In[1]:


get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')

jaidevd

Deep Learning with the Scientific Method

The Problem

As technology becomes cheaper and more available, we start taking it for granted. Nowhere is this more true than in machine learning. As machines become cheaper and data becomes more and more voluminous, our approach to specific machine learning problems often, and understandably, becomes haphazard. Since GPUs are much cheaper and more widely available than ever before, we implicitly believe that throwing enough artificial neurons at a problem will eventually solve it. While this by itself may be true, it is not uncommon for ML practitioners to realize - unfortunately only in hindsight - that most of the iterations required to build a successful predictive model were unnecessary. Ironically, these 'missteps' are often what lead us to the correct answer. Solving a machine learning problem is like traversing a minefield, where the safest path can only be determined by blowing up a significantly large number of mines. You can only figure out the right a