Marc Romeyn marcromeyn

ANNOUNCEMENT

I have moved this over to the Tech Interview Cheat Sheet Repo and has been expanded and even has code challenges you can run and practice against!

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Get Homebrew installed on your mac if you don't already have it

Install highlight. "brew install highlight". (This brings down Lua and Boost as well)

	/**
	* Inspired by @MoOx original script: https://gist.github.com/MoOx/93c2853fee760f42d97f
	* Adds file download per @micalevisk https://gist.github.com/MoOx/93c2853fee760f42d97f#gistcomment-2660220
	*
	* Changes include:
	* - Get the description from the `title` attribute instead of `aria-label` (doesn't exist anymore)
	* - Use style.backgroundColor and parse the rgb(...) to hex (rather than regex parsing of 'style' string)
	* - Downloads labels to a JSON file named after the webpage to know which GitHub repo they came from.
	*
	* Last tested 2019-July-27:

	"""An example of how to use tf.Dataset in Keras Model"""
	import tensorflow as tf # only work from tensorflow==1.9.0-rc1 and after

	_EPOCHS = 5
	_NUM_CLASSES = 10
	_BATCH_SIZE = 128

	def training_pipeline():
	# #############
	# Load Dataset

	# Option 1: Use apt-get

	# keys taken from https://software.intel.com/en-us/articles/installing-intel-free-libs-and-python-apt-repo
	cd ~/GitHub/r-with-intel-mkl/
	wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB
	apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB

	sudo sh -c 'echo deb https://apt.repos.intel.com/mkl all main > /etc/apt/sources.list.d/intel-mkl.list'
	sudo apt-get update && sudo apt-get install intel-mkl-64bit

	import torch
	import torch.nn as nn
	import numpy as np
	import torch.optim as optim
	from torch.autograd import Variable

	# (1, 0) => target labels 0+2
	# (0, 1) => target labels 1
	# (1, 1) => target labels 3
	train = []

	import numpy as np
	from tensorflow.python.summary.event_accumulator import EventAccumulator

	import matplotlib as mpl
	import matplotlib.pyplot as plt

	def plot_tensorflow_log(path):

	# Loading too much data is slow...
	tf_size_guidance = {