Fabrice Sodogandji fsodogandji

A Few Useful Things to Know about Machine Learning

The paper presents some key lessons and "folk wisdom" that machine learning researchers and practitioners have learnt from experience and which are hard to find in textbooks.

1. Learning = Representation + Evaluation + Optimization

All machine learning algorithms have three components:

Representation for a learner is the set if classifiers/functions that can be possibly learnt. This set is called hypothesis space. If a function is not in hypothesis space, it can not be learnt.
Evaluation function tells how good the machine learning model is.
Optimisation is the method to search for the most optimal learning model.

Currently, there is an explosion of tools that aim to manage secrets for automated, cloud native infrastructure management. Daniel Somerfield did some work classifying the various approaches, but (as far as I know) no one has made a recent effort to summarize the various tools.

This is an attempt to give a quick overview of what can be found out there. The list is alphabetical. There will be tools that are missing, and some of the facts might be wrong--I welcome your corrections. For the purpose, I can be reached via @maxvt on Twitter, or just leave me a comment here.

There is a companion feature matrix of various tools. Comments are welcome in the same manner.

	import numpy as np
	from scipy.ndimage.interpolation import map_coordinates
	from scipy.ndimage.filters import gaussian_filter

	def elastic_transform(image, alpha, sigma, random_state=None):
	"""Elastic deformation of images as described in [Simard2003]_.


	.. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for
	Convolutional Neural Networks applied to Visual Document Analysis", in

	#!/usr/local/bin/python3

	import glob
	import h5py
	import matplotlib.animation as animation
	import matplotlib.pyplot as plot
	import numpy as np
	import tensorflow as tf