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 Also, technology becomes outdated — fast.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.
http://robotframework.org/
#Robot#Framework is a generic test #automation framework for acceptance testing and acceptance test-driven development (ATDD). It has easy-to-use tabular test data syntax and it utilizes the keyword-driven testing approach. Its testing capabilities can be extended by test libraries implemented either with Python or Java, and users can create new higher-level keywords from existing ones using the same syntax that is used for creating test cases.
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.
https://github.com/benhoff/vexbot
Pluggable #bot
Under heavy development. Not ready for general use outside of driving #chatimusmaximus
Requires python 3.5
Configuring Addresses
#Vexbot uses messaging and subprocesses for different services. This has some advantages/disadvantages of this approach, but the reason it's staying is it allows the developer some decreased congnitive load while developing this project.
The address expected is in the format of tcp://[ADDRESS]:[PORT_NUMBER]. For example tcp://127.0.0.1:5617 is a valid address. 127.0.0.1 is the ADDRESS and 5617 is the PORT_NUMBER.
127.0.0.1 was chosen specifially as an example because for IPV4 it is the "localhost". Localhost is the computer the program is being run on. So if you want the program to connect to a socket on your local computer (you probably do), use 127.0.0.1.
Port numbers range from 0-65536, and can be mostly aribratry chosen. For linux ports 0-1024 are reserved, so best to stay away from those. Port 5555 is usually used as an example port for coding examples, so probably best to stay away from that as well.
The value of the publish_address and subscribe_address at the top of the settings file are likely what you want to copy for the publish_address and subscribe_address under shell, irc, xmpp, youtube, and socket_io if you're running everything locally on one computer. But you don't have to. You could run all the services on one computer and the main #robot on a different computer. You would just need to configure the address and ports correctly, as well as work through any networking/port issues going across the local area network (LAN).
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
https://github.com/4Catalyzer/pykubectl
A python bridge to kubectl providing additional functionalities useful for CD and #automation.
#machine_learning
http://mdp.cdm.depaul.edu/DePy2016
3rd Annual #Conference on #Python applications in #Data_Analysis, #Machine_Learning, and Web
May 6, 7
DePaul University - Room LL105
14 E Jackson Blvd
Chicago IL 60604, USA