|Welcome to issue 208 of Python Weekly. We have a packed issue this week. Enjoy it!
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Articles, Tutorials and Talks
Simple Genetic Algorithm In 15 Lines Of Python
A simple yet effective genetic algorithm implementation used to train a neural network in 15 lines of code.
Time Maps: Visualizing Discrete Events Across Many Timescales
Visualizing many events over long time periods poses a unique set of challenges. We show how two-dimensional plots displaying the timings between events can reveal both outliers and hidden structure. Adopted from the field of chaotic systems, these "time maps" allow users to identify features that can take place on timescales ranging from milliseconds to months, all within a single image. The exploratory value of time maps is demonstrated using examples from Twitter and online bot behavior.
Episode #24: Fluent Python
Are you fluent in Python or do you speak the language with an accent? Maybe you have a hint of C++ in your for-in loop or even a little C# coming through in your function names. Python's ease of learning can also lead to non-pythonic patterns for even experienced developers. It's so easy to jump in and (superficially) learn the language that you might miss the deeper understanding and Pythonic thinking. Luciano Ramalho is here to help us clear up that accent that has been giving us away to our peers and he is giving everyone a deeper understanding of this language we love with his just released book "Fluent Python".
Introduction to Monte Carlo Tree Search
This article describes how MCTS works, specifically a variant called Upper Confidence bound applied to Trees (UCT), and then will show you how to build a basic implementation in Python.
Implementing a Neural Network from Scratch - An Introduction
In this post we will implement a simple 3-layer neural network from scratch. We won't derive all the math that's required, but I will try to give an intuitive explanation of what we are doing. I will also point to resources for you read up on the details.
Blur detection with OpenCV
This post shows you how to compute the amount of blur in an image using OpenCV, Python, and the Laplacian operator. By the end of this post, you'll be able to apply the variance of the Laplacian method to your own photos to detect the amount of blurring.
Analyzing Reddit Comments with Dask and Castra
Dask is designed to fit the space between in memory tools like NumPy/Pandas and distributed tools like Spark/Hadoop. By using blocked algorithms and the existing Python ecosystem, it's able to work efficiently on large arrays or dataframes - often in parallel. People have been writing about Dask a lot lately. In this post we'll show a complete workflow of using Dask to analyze a large dataset.
Quick Coding Intro to Neural Networks
In this tutorial, we'll use Python with the Numpy and Theano to get a feel for writing machine learning algorithms. We'll start with a brief intro those libraries, and then implement a logistic regression and a neural network, looking at some properties of the implementations as we train them.
Cluster Analysis is an important problem in data analysis. Data scientists use clustering to identify malfunctioning servers, group genes with similar expression patterns, or various other applications. This post covers a family of techniques known as density-based clustering. Compared to centroid-based clustering like K-Means, density-based clustering works by identifying "dense" clusters of points, allowing it to learn clusters of arbitrary shape and identify outliers in the data.
How We Designed Matplotlib's New Default Colormap (and You Can Too)
In this talk, we'll present our new colormap and the theory, tools, data, and motivations behind its design together with a short and friendly tutorial on color theory and colormap design for the working scientist.
Introduction to working with APIs in Python
In this post, we'll be querying a simple API to retrieve data about the International Space Station (ISS). Using an API will save us time and effort over doing all the computation ourselves.
How to Use a Machine Learning Checklist to Get Accurate Predictions, Reliably
How do you get accurate results using machine learning on problem after problem? The difficulty is that each problem is unique, requiring different data sources, features, algorithms, algorithm configurations and on and on. The solution is to use a checklist that guarantees a good result every time. In this post you will discover a checklist that you can use to reliably get good results on your machine learning problems.
Podcast.__init__ Episode 22 - Bryan Van de Ven on Bokeh
Comparing Python Command-Line Parsing Libraries - Argparse, Docopt, and Click
Warm Phone Call Transfers with Python, Flask and Twilio Voice
How Proof of Work Works
Kiwi Pycon 2015 Videos
10 Reasons to love SQLAlchemy
If you are a scientist, programmer, software engineer, or student who has working knowledge of matplotlib and now want to extend your usage of matplotlib to plot complex graphs and charts and handle large datasets, then this book is for you.
Python Jobs of the Week
Full Stack Engineer at Zapier
Most of what you'll do each day is guiding, building and maintaining Zapier integrations across a community of 500+ companies. You'll work in the Zapier codebase, the developer platform and more. The stack is built on Python, Django, React, node.js and AWS. We use modern tools, which means you'll have the opportunity to work with software like RabbitMQ, Zookeeper, Docker, Redis, Jenkins, Puppet, Ansible and much more.
Interesting Projects, Tools and Libraries
Sleepy Puppy is a cross-site scripting (XSS) payload management framework which simplifies the ability to capture, manage, and track XSS propagation over long periods of time.
An artificial machine learning program that attempts to impersonate the writing style of any given text training set.
This library facilitates automated, commission-free stock trading from Python using Robinhood's API.
Hashmal is an IDE for Bitcoin transaction scripts. Its purpose is to make it easier to write, evaluate, and learn about transaction scripts. Hashmal is intended for cryptocurrency developers and power users.
VaaS - Varnish as a Service
VaaS enables you to manage cluster(s) of Varnish servers from one place, via a web GUI or a REST API. Information about your Varnish servers and their backends, directors and probes is saved into a database. It is then used to automatically generate and distribute VCLs.
A python compiler for the Skoar musical programming language.
An Easy to Use Webhooks Service, using RethinkDB, Redis and Python 3.
News Corpus Builder
A Python module that allows users to generate a custom corpus specific to their particular topic/s and store text and associated label in sqlite database or as flat files. The corpora that can be created with News Corpus Builder can be used in a variety of natural language processing & machine learning tasks.
A standalone Jupyter environment for doing data science using Python. It aims to include many useful working libraries and packages, while remaining super easy to install and use.
Man in the Middle analysis tool for Bluetooth.
fiddle is a Python code editor designed as an alternate to Python's default IDLE development environment. It is aimed at both beginning Python programmers just learning the language and experience Python developers that may not require a full IDE for simpler projects.
Implementation of A Neural Algorithm of Artistic Style. A method to transfer the style of one image to the subject of another image.
Fact Extraction from Wikipedia Text.
A small framework to create a whatsapp bot, with regex-callback message routing.
Upcoming Events and Webinars
PyData NYC 2015
PyData brings together both users and developers of data analysis tools to share ideas and learn from each other. The PyData community will gather in New York to discuss how best to apply Python tools, as well as those using R and Julia, to meet the evolving challenges in data management, processing, analytics, and visualization.
An overview of models in Django - Philadelphia, PA
In this talk you'll hear about: Django model fields and methods, Model inheritance, Relational databases, Migrations and How to access the data stored within your models.
PyHou Meetup September 2015 - Houston, TX
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