Python Weekly (Issue 371 November 1 2018)

Python Weekly - Issue 371

November 01, 2018

Python Weekly

Welcome to issue 371 of Python Weekly. Let's get straight to the links this week.

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News

One package contained a clipboard hijacker that replaced victims' Bitcoin addresses in an attempt to hijack funds from users.

Articles, Tutorials and Talks

Shaobo Guan explains how he built a novel GAN architecture at Insight that allows us to generate custom photo-realistic images of faces based on any attribute.

At some point in time, each of us would have went through the process of either renting or buying a house. Whether we realize or not, a lot of factors we consider important are heavily influenced by location. In this article, we apply the data wrangling capabilities of scientific Python ecosystem and geospatial data visualization & analysis capabilities of the ArcGIS platform to build a model that will help shortlist good properties (houses). 

In this tutorial, you will learn how to use the dlib library to efficiently track multiple objects in real-time video.

Accelerate your pandas workflows by changing one line of code.

In this brief Python data analysis tutorial we will learn how to carry out a repeated measures ANOVA using Statsmodels. More specifically, we will learn how to use the AnovaRM class from statsmodels anova module.

Last week, we explored different techniques for de-duplication for identifying similar documents using BOW, TFIDF, and Xgboost. We found that the traditional methods such as TFIDF can achieve some impressive results. That’s one of the reasons that Google’s long been using TFIDF in indexing and information retrieval to figure out the importance of a given keyword to a given page. To continue our learning journey and grow our skills, today, we will explore how to solve the same matching and de-duplication problem using a different method, again, we will tackle the task of de-duplication as an extension of the classifier. Let’s get started!

Read this Ultimate Introduction to Machine Learning Guide to learn how to take your first steps when adventuring into the possibilities of AI.

Python 3.6 introduced a new module called secrets for generating a strong and secure random number. In this article, we will learn how to use the secrets module.

In this post, I’ll give a short explanation of neuron, a Visual Studio Code extension that aims to be a one-stop-shop for data scientists. It’s an extension I developed as part of a team of students at Imperial College London, in collaboration with Microsoft, in the summer of 2018.

Have you ever asked yourself what happens when you start a Celery worker? Ok, it might not have been on your mind. But you might have come across things like execution pool, concurrency settings, prefork, gevent, eventlet and solo. So, what is it all about? How does it all fit together? And how is it related to the mechanics of a Celery worker?

Parallel processing is a mode of operation where the task is executed simultaneously in multiple processors in the same computer. It is meant to reduce the overall processing time. In this tutorial, you’ll understand the procedure to parallelize any typical logic using python’s multiprocessing module.

Books

Program a graphical adventure game in this hands-on, beginner-friendly introduction to coding in the Python language.

Python Jobs of the Week

At Canvass Analytics, we are disrupting the way industrial operational data is being used to make critical business decisions. To stay ahead of the curve and continue to scale rapidly, Canvass Analytics is adding a Team Lead, Software Development to the team. We are looking for a senior developer, a strong mentor and a lifelong learner, who is open to new concepts. We want someone with the knowledge to introduce ideas and the development experience to make those ideas a reality.

Interesting Projects, Tools and Libraries

A modern continuous integration, deployment, and self-service automation platform.

VergeML is a command line based environment for exploring, training and running state-of-the-art Machine Learning models. It provides ready-to-use models, handles data preprocessing and augmentation, tracks your AI's training sessions and provides other goodies such as an automatic REST interface.

An experimental code editor for writing algorithms.

AdaNet is a lightweight and scalable TensorFlow AutoML framework for training and deploying adaptive neural networks using the AdaNet algorithm. AdaNet combines several learned subnetworks in order to mitigate the complexity inherent in designing effective neural networks.

Flexible command line tool to create graphs from CSV data.

This is a lightweight neural network analyzer based on PyTorch. It is designed to make building your networks quick and easy, with the ability to debug them.

This repository contains ClassificaIO, a Python package that provides a graphical user interface (GUI) for machine learning algorithms from scikit-learn.

This free SaaS boilerplate allows you to create a working SaaS application with minimal efforts.

Nmap Web Dashboard and Reporting.

A generative model conditioned on shape and appearance.

Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch.

New Releases

Upcoming Events and Webinars

The os module and the os.path module are full of helper utilities and so is the shutil module. Python's pathlib module is a new(ish) part of the standard library that can be used as a replacement for the many functions buried in os, os.path, and other parts of the standard library. Join in this live chat for a discussion about pathlib and how it can help us write more readable Python code.

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