Python Weekly (Issue 342 - April 12 2018)

Python Weekly - Issue 342Â

April 12, 2018

Python Weekly

Welcome to issue 342 of Python Weekly. Let's get straight to the links this week.

From Our Sponsor 

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Articles, Tutorials and Talks

We turned a MacBook into a touchscreen using only $1 of hardware and a little bit of computer vision. The proof-of-concept, dubbed “Project Sistine” after our recreation of the famous painting in the Sistine Chapel, was prototyped in about 16 hours.

The real world poses challenges like having limited data and having tiny hardware like Mobile Phones and Raspberry Pis which can’t run complex Deep Learning models. This post demonstrates how you can detect objects using a Raspberry Pi. Like cars on a road, oranges in a fridge, signatures in a document and teslas in space.

Learn how you can quickly build an image dataset suitable for deep learning and training a Convolutional Neural Network (CNN) using Python and the (free) Bing Image Search API.

Using TensorFlow as a distributed computation framework for dataflow programs we give a full implementation of the SPDZ protocol with networking, in turn enabling optimised machine learning on encrypted data.

In this hands-on tutorial, you will learn the basics of using pdb, Python's interactive source code debugger. Pdb is a great tool for tracking down hard-to-find bugs and allows you to fix faulty code more quickly.

One common cause of bugs in many applications is that development and production environments differ. Although in most cases it’s not possible to provide an exact copy of the production environment for development, pursuing dev-prod parity is a worthwhile cause. Most web applications are deployed to some sort of Linux VM. If you’re using a traditional web-host, this is referred to as VPS hosting. If we want to develop in an environment similar to our production environment, how could we approach this? The closest would be to set up a second VM for development purposes. So let’s have a look to see how we can connect PyCharm to a VPS box.

In this post, we will explore our first reinforcement learning methods for estimating value. It’s the first taste of real RL in this series.

How to take advantage of vectorization and broadcasting so you can use NumPy to its full capacity. In this tutorial you'll see step-by-step how these advanced features in NumPy help you writer faster code.

This article is the first part in a short tutorial series I’m writing to document my progress in a personal side project where I’m hoping to analyze and visualize the complete works of Beethoven. The goal of this project is to explore the connection between music and emotion, while also experimenting with different ways of visualizing music data, especially with regard to color.

Books

This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications.

Interesting Projects, Tools and Libraries

FlameScope is a visualization tool for exploring different time ranges as Flame Graphs.

AWS Serverless Application Model (AWS SAM) prescribes rules for expressing Serverless applications on AWS.

Non-local Neural Networks for Video Classification.

A Multi-Qubit Ideal Quantum Computer Simulator.

Python Script to download hundreds of images from 'Google Images'. It is a ready-to-run code!

Automated Security Testing For REST API's.

Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow.

The easiest way to add rest API to an arbitrary DB.

It can detect and decode encoded strings, recursively.

Unofficial GoPro API Library for Python - connect to HERO3/3+/4/5/+/6 via WiFi.

An Open-source Neural Sequence Labeling Toolkit. It includes character LSTM/CNN, word LSTM/CNN and softmax/CRF components.

End-to-End Learning of Motion Representation for Video Understanding.

Tool to scan for secret files on HTTP servers.

Easily configure MacOS security settings from the terminal.

A project template to simplify building and training deep learning models using Keras.

Upcoming Events and Webinars

Python's comparison operators are pretty powerful. But this power means there's quite a bit to understand about them. In this chat we'll review the various comparisons in Python: equality/inequality and the ordering operators. We'll also discuss the difference between equality and identity and we'll likely take a look at how many of these operators are "deep" operations in Python

There will be following talks

  • Building desktop applications using PyQt

  • Python for Data Science

This month's topic is Intro to Software Testing with PyTest with Justin Grindal.

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Welcome to issue 342 of Python Weekly. Let's get straight to the links this week.