Python Weekly (Issue 343 - April 19 2018)

Python Weekly - Issue 343

April 19, 2018

Python Weekly

Welcome to issue 343 of Python Weekly. Let's get straight to the links this week.

From Our Sponsor 

Track down issues quickly with Datadog's distributed request tracing for Python applications. Get end-to-end visibility into your application to find slow queries, bugs, and inefficient code.

News

The PSF is launching an exciting fundraising drive with a goal of raising $20,000.00 USD by May 12th.  The drive begins April 16, 2018 and concludes at PyCon on May 12th. Your donations help the Python community worldwide by supporting sprints, meetups, community events and projects, the Python Ambassador Program, fiscal sponsorships, and of course, software development and open source projects

Articles, Tutorials and Talks

This gentle guide will show you how to implement, train, and evaluate your first Convolutional Neural Network (CNN) with Keras and deep learning.

Recommenders, generally associated with e-commerce, sift though a huge inventory of available items to find and recommend ones that a user will like. Different from search, recommenders rely on historical data to tease out user preference. How does a recommender accomplish this? In this post we explore building simple recommendation systems in PyTorch using the Movielens 100K data, which has 100,000 ratings (1-5) that 943 users provided on 1682 movies.

This article explores Python modules and Python packages, two mechanisms that facilitate modular programming.

In this article we'll see what support vector machines algorithms are, the brief theory behind support vector machine and their implementation in Python's Scikit-Learn library. We will then move towards an advanced SVM concept, known as Kernel SVM, and will also implement it with the help of Scikit-Learn.

Or how to remotely control a browser from python.

This post demonstrates how to make a Flask app serverless without locking you into any tooling or cloud platforms. It doesn’t require any application code changes or serverless frameworks so you can continue to develop locally like you always have.

Teaching a machine to master car racing and fireball avoidance through “World Models”.

This is the second part of the tutorial, we'll setup redux and react router in our note taking application. And later connect this frontend to a API backend.

Books

The expanded edition of this practical book not only introduces you web scraping, but also serves as a comprehensive guide to scraping almost every type of data from the modern web. Part I focuses on web scraping mechanics: using Python to request information from a web server, performing basic handling of the server’s response, and interacting with sites in an automated fashion. Part II explores a variety of more specific tools and applications to fit any web scraping scenario you’re likely to encounter.

Interesting Projects, Tools and Libraries

Command-line tool that instantly fetches Stack Overflow results when you get a compiler error.

Booksoup allows you to analyse and traverse your downloaded facebook data, including features such as sentiment analysis and message frequency analysis over time.

The most complete open-source tool for Twitter intelligence analysis.

Python library providing function decorators for configurable backoff and retry.

Easy to use library to bring Tensorflow on Apache Spark.

Multimodal Unsupervised Image-to-Image Translation.

Simple schedule optimization library for Python.

Password spraying using AWS Lambda for IP rotation.

Microsoft Machine Learning for Apache Spark.

Declarative model lifecycle hooks, inspired by Rails callbacks.

A poorman's proxycannon and botnet, using docker, ovpn files, and a dante socks5 proxy.

An awesome open source and portable collection of Solitaire/Patience games written in Python.

Python implementation of the deep ensemble algorithm.

Generate identicons for DNA sequences with Python.

New Releases

Upcoming Events and Webinars

Sets and logic are strongly related. That's why proper use of set operations can eliminate lots of nested loops and ifs, producing code that is more readable and faster. Let's talk about using sets in practice, and learn great API design ideas from Python's set types.

In this presentation, Bernat Gabor will detail his experience using type hinting for both adding type hinting and checking type correctness for a library supporting both Python 2 and 3, and reusing this information to automatically insert type data into the generated Sphinx documentation.

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