Python Weekly (Issue 344 - April 26 2018)

Python Weekly - Issue 344Â

April 26, 2018

Python Weekly

Welcome to issue 344 of Python Weekly. Let's get straight to the links this week.

Learn Python like a Professional! Start from the basics and go all the way to creating your own applications and games!

News

Anyone interested in speaking is encouraged to submit a proposal. There are no restrictions on topics, but we recommend they be of interest to Pythonistas. Talks will fill slots of 25 minutes and 40 minutes, including time for questions and answers.

Articles, Tutorials and Talks

Applying hardware acceleration to deep neuroevolution in what is now an open source project, Uber AI Labs was able to train a neural network to play Atari in just a few hours on a single personal computer, making this type of research accessible to a far greater number of people.

This is the story of a debugging odyssey that could have been avoided by a more careful reading of documentation. I’m telling it not only to draw attention to the documentation, but also because it’s the story of a fun hunt. And there are good lessons to be learned for any of us.

Pipenv is a packaging tool for Python that solves some common problems associated with the typical workflow using pip, virtualenv, and the good old requirements.txt. This guide goes over what problems Pipenv solves and how to manage your Python dependencies with it.

In last week’s post, you learned how to train a Convolutional Neural Network (CNN) with Keras. In this post, we’re going to take this trained Keras model and deploy it to an iPhone and iOS app using what Apple has dubbed “CoreML”, an easy-to-use machine learning framework for Apple applications.

In part 1 of the Bayesian Machine Learning project, we outline our problem, performe a full exploratory data analysis, select our features, and establish benchmarks.

  • Part 2 - In this part, we will implement Bayesian Linear Regression in Python to build a model. After we have trained our model, we will interpret the model parameters and use the model to make predictions. 

Prototype for a garage door monitor/controller using MicroPython and an ESP8266.

Learn how to get started with Arcade, an easy-to-use Python library for creating 2D video games.

This session discusses the Flask contexts and the very confusing errors that these can cause when used incorrectly. It includes the Q&A portion as well.

In this post, we’ll build an Instagram “Pin” effect in Python, where an image is made to stay in specific position while the camera moves around it. This pin effect is now much less impressive now that Apple’s ARKit has become commonplace, but working with a simple interface like Dlib’s correlation tracker gives us a great starting point without needing sensor data like in the iPhone. So we’ll build a tool for creating videos with pinned images in them in Python. Along the way, we’ll build an interactive environment to test candidate positions for our correlation trackers, allowing us to preview just how well our places will be tracked.

How to effectively work with file system paths in Python 3 using the new "pathlib" module in the standard library.

The previous posts about The Great Khan Academy Python Refactor of 2017 and Also 2018 answered two questions: why and how did we refactor all of our Python code? In this post, I want to look closer at a major goal of this project: cleaning up dependencies between parts of our Python codebase.

This article explains how to write a tiny and basic SOCKS 5 server in Python 3.6. I am assuming that you already have a basic understanding of proxy servers.

In this article I will talk about the npyscreen — library for creating console applications.

Python comprehensions can have duplicate function calls (e.g. [foo(x) for x in ... if foo(x)]). If these function calls are expensive, we need to rewrite our comprehensions to avoid the cost of calling them multiple times. In this post, we solve this by writing a decorator that converts a function in to AST, optimizes away duplicate function calls and compiles it at runtime in ~200 lines of code.

Books

Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of this comprehensive TensorFlow guide. Throughout the book, you'll learn how to implement deep learning algorithms for machine learning systems and integrate them into your product offerings, including search, image recognition, and language processing. Additionally, you'll learn how to analyze and improve the performance of deep learning models. This can be done by comparing algorithms against benchmarks, along with machine intelligence, to learn from the information and determine ideal behaviors within a specific context.

Interesting Projects, Tools and Libraries

Official python API for Phish.AI public and private API to detect zero-day phishing websites.

Build Your Own Photo Sharing App in 5 minutes using Django and React Native.

Building the largest open-source Ecommerce scraper with Python and BeautifulSoup4.

IPython magic command to format python code in cell using black.

HackBox is the combination of awesome tools and techniques.

minebash is a minesweeper implementation in Python for the Linux command line. 

Example project combining Neo4j, Python and Github API.

Fast and simple music and audio analysis using RNN in Python.

Bruteforce protection for Django projects based on Redis. Simple, powerful, extendable.

SMBrute is a program that can be used to bruteforce username and passwords of servers that are using SMB (Samba).

Red team Arsenal - An intelligent scanner to detect security vulnerabilities in company's layer 7 assets.

New Releases

Trade-off memory for compute, Windows support, 24 distributions with cdf, variance etc., dtypes, zero-dimensional Tensors, Tensor-Variable merge, , faster distributed, perf and bug fixes, CuDNN 7.1.

The PyPy team is proud to release both PyPy2.7 v6.0 (an interpreter supporting Python 2.7 syntax), and a PyPy3.5 v6.0 (an interpreter supporting Python 3.5 syntax). The two releases are both based on much the same codebase, thus the dual release.

Upcoming Events and Webinars

There will be following talks

  • Real-Time, Continuous ML/AI Model Training, Optimizing, and Predicting with Scikit-Learn, TensorFlow, Spark ML, GPU, TPU, Kafka, and Kubernetes

  • Training and serving ML models using Tensorflow

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