This new blog series aims to help DevOps leaders in organisation get stakeholder buy-in. It covers approaches to talking about why, as well as specific things you can do to sell your ideas.
In this article, a CRF (Conditional Random Field) will be trained to learn how to segment Latin text. Using only very basic features and easily accessible training data, we are going to achieve a segmentation accuracy of 98 %.
Clustering is a type of Unsupervised learning. This is very often used when you don’t have labeled data. K-Means Clustering is one of the popular clustering algorithm. The goal of this algorithm is to find groups(clusters) in the given data. In this post we will implement K-Means algorithm using Python from scratch.
n this post I’m going to look at a bit of Python code I optimized recently, and then compare the process of making this code faster to the process of how I make Ruby code faster.
Python project structure and packaging can be intimidating, but, if you take it step by step, it doesn’t have to be.
Look at other people’s code, particularly smaller, modular projects, break the work up into pieces, and work through it piece by piece, until you’re all the way down the rabbit hole.
Learn how innovations in computer vision improve customer experiences.
This is the first part of a multi-part series discussing the limitation of WSGI-based Python web applications and the ways to overcome these limitations.
Using MPG diagrams to see the differences between Threading, Multiprocessing and Asyncio, the 3 official CPython options, and Go Runtime.
In the first part of this series we discussed the problems and limitations which inheres within WSGI-based Python web applications. In this part we will discuss what concurrency is and what is an event driven architecture
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There are various situations where quants look at different scenarios of an event when making investment decisions. Running simulated scenarios is an invaluable tool for all finance/investment managers as it allows them to measure likely performance for various states.
I wrote a Python script (GitHub) that does a few things. First and foremost, I wanted to know every minute on the minute what my CPU core temps were regardless of whether I’m getting throttled or not so that I had the option to chart this (I haven’t done this, as I think I’ve found the culprit but I wanted to keep my options open). I also wanted to know if my laptop fan was functioning as desired in relation to the CPU temps, so I needed to grab fan RPM.
This NetworkX tutorial will show you how to do graph optimization in Python by solving the Chinese Postman Problem in Python.
implement a fixed-size hash map that associates string keys with arbitrary data object references.
Python 3.6.3 is the third maintenance release of Python 3.6. The Python 3.6 series contains many new features and optimizations. See the What’s New In Python 3.6 document for more information.
Do you know what is the heaviest book ever printed? Let’s find out by exploring the Open Library data set using Spark in Python.
A setuptools/wheel/cffi extension to embed a binary data in wheels.
Read a packet capture, extract HTTP requests and turn them into cURL commands for replay.
Dockerized Selenium and Python with support for Chrome and Firefox.
Create and maintain dynamic Plex libraries based on recipes.
BigQuery Foreign Data Wrapper for PostgreSQL.
A simple sync tool to sync task from Workflowy to Teambition.
Simple ASCII Art Library For Python