# Because Python has first-class functions they can be used to emulate switch/case statements
def dispatch_if(operator, x, y):
if operator == 'add':
return x + y
elif operator == 'sub':
return x - y
elif operator == 'mul':
return x * y
elif operator == 'div':
return x / y
else:
return None
def dispatch_dict(operator, x, y):
return {
'add': lambda: x + y,
'sub': lambda: x - y,
'mul': lambda: x * y,
'div': lambda: x / y,
}.get(operator, lambda: None)()
#lambda
»> dispatch_if('mul', 2, 8)
16
»> dispatch_dict('mul', 2, 8)
16
»> dispatch_if('unknown', 2, 8)
None
»> dispatch_dict('unknown', 2, 8)
None
http://techioz.com/lambda-function-in-python/
#lambda function is defined using lambda keyword. It’s an anonymous function without name.
#python#learn
https://github.com/Miserlou/Zappa#about
Zappa makes it super easy to build and deploy all Python #WSGI applications on #AWS Lambda + #API Gateway. Think of it as "#serverless" #web hosting for your Python apps. That means infinite scaling, zero downtime, zero maintenance - and at a fraction of the cost of your current deployments!
If you've got a Python web app (including Django and Flask apps), it's as easy as:
$ pip install zappa
$ zappa init
$ zappa deploy
and now you're server-less! Wow!
What do you mean "serverless"?
Okay, so there still is a server - but it only has a 40 millisecond life cycle! Serverless in this case means "without any permanent infrastructure."
https://hackernoon.com/absolute-fundamentals-of-machine-learning-dca5deee78df?gi=2c99287cb9f5
#machine_learning , what a buzzword. I’m sure you all want to understand machine learning, and that’s what I’m going to teach in this article.
I found that learning the theroetical side alongside the programming side makes it easier to learn both, so this article features both easy to understand mathematics and the algorithms implemented in Python. Also, technology becomes outdated — fast. The code used in this tutorial will likely be meaningless in 5 years time. So for that reason, I’ve decided to also teach the mathematical side to Machine Learning that will not die out in a few years.
https://www.udemy.com/machinelearning/learn/v4/content
#machine_learning A-Z™: Hands-On #Python & R In #Data_Science
https://machinelearningmastery.com/setup-python-environment-machine-learning-deep-learning-anaconda/
It can be difficult to install a #Python#machine_learning environment on some platforms.
Python itself must be installed first and then there are many packages to install, and it can be confusing for beginners.
In this tutorial, you will discover how to set up a Python machine learning development environment using #Anaconda.
http://www.paulbrownmagic.com/blog/vslambda
Python has support for #lambda functions, Haskell is built upon lambda calculus. The two are not the same and this is the reason why lambda should have been removed in #Python3. This post examines the differences, reviews the use in Python, and offers a more pythonic, honest syntax.
#learn
http://www.aparat.com/v/0scM5
Irene Chen A Beginner's Guide to Deep Learning.
What is #Deep_Learning ? It has recently exploded in popularity as a complex and incredibly powerful tool. This talk will present the basic concepts underlying deep learning in understandable pieces for complete beginners to #machine_learning.
http://www.aparat.com/v/Corus
Advanced users #Deep_Learning, anyone who has followed #machine_learning over the past years has heard it. In this talk I will go past the hype and show what deep learning actually means and how one goes about solving complex machine learning task with a minimum amount of code, with the help of theano, an amazing python library for deep learning.
https://www.python-course.eu/machine_learning.php
Tutorial and Online Course
#machine_learning
machine learning: robot jugglers
This is a completely new and incomplete chapter of our tutorial! We started work in January 2017!
#learn