Python Weekly (Issue 369 October 18 2018)

Python Weekly - Issue 369

October 18, 2018

Python Weekly

Welcome to issue 369 of Python Weekly. Let's get straight to the links this week.

From Our Sponsor 

Learn how to create gorgeous user interfaces and data visualizations with Qt for Python in this free hands-on webinar. Also included are free examples of ready-made​ 2D/3D data visualizations, controls, charts, and more to get you started and code along, so register now!

News

PyCon 2019’s Call for Proposals has officially opened for talks, tutorials, posters, education summit presentations, as well as the hatchery program PyCon Charlas. PyCon is made by you, so we need you to share what you’re working on, how you’re working on it, what you’ve learned, what you’re learning, and so much more.

The PyCon conference prides itself on being affordable. However, registration is only one of several expenses an attendee must incur, and it’s likely the smallest one. Flying, whether halfway around the world or from a few hundred miles away, is more expensive. Staying in a hotel for a few days is also more expensive. All together, the cost of attending a conference can become prohibitively expensive. That’s where PyCon's Financial Aid program comes in. We’re opening applications for Financial Aid today, and we’ll be accepting them through February 12, 2019.

Articles, Tutorials and Talks

Learn the Python programming language in under 90 minutes. Slow paced for beginners.

What they are and how to use them.

In this tutorial you will train your own Keras CNN to classify the health of hydroponic plant root structures using Python, Keras, and Deep Learning.

Jupyter is now widely used for teaching and research. The use of Kubernetes for deploying a JupyterHub has enabled reliable setups scaling to thousands of users. There are many cloud computing vendors (Google, Amazon, …) and the first attempts to use JupyterHub with Kubernetes is based on them. But relying on vendor clouds increases the risk of vendor lock-in. In addition, there are many pre-existing academic clouds managed by people with a high level of expertise and a thorough knowledge of their infrastructure and associated tools. These are often more cost-effective for research and education. Could we build upon these academic cloud computing to provide scalable and high-quality infrastructure for education and research? In this post, we will focus on how to deploy JupyterHub with Kubernetes on OpenStack. A first attempt to create academic cloud computing in France.

How many times has it happened to you that you are searching for a parking spot by driving around and around the parking lot. How convenient would it be if your phone could tell you exactly where the closest parking spot is! It turns out that this is a relatively easy problem to solve using deep learning and OpenCV. All that is needed is an aerial shot of the parking lot. 

In this tutorial, we will be learning how to use Pipenv. Pipenv is a new package manager that combines pip and virtualenv into one easy-to-use tool. We will learn how to install pipenv, how to install new packages, how to manage our newly created environment for our project, and also look at some more advanced use cases. 

How to perform spinal cord gray matter segmentation using PyTorch medical imaging framework, MedicalTorch.

Gensim is billed as a Natural Language Processing package that does ‘Topic Modeling for Humans’. But it is practically much more than that. It is a leading and a state-of-the-art package for processing texts, working with word vector models (such as Word2Vec, FastText etc) and for building topic models. This post intends to give a practical overview of the nearly all major features, explained in a simple and easy to understand way.

In this tutorial, we’ll build a Recurrent Neural Network (RNN) in PyTorch that will classify people’s names by their languages. We assume that the reader has a basic understanding of PyTorch and machine learning in Python. At the end of this tutorial we’ll be able to predict the language of the names based on their spelling. 

I made a New Year’s resolution: every plot I make during 2018 will contain uncertainty estimates. Nine months in and I have learned a lot, so I put together a summary of some of the most useful methods.

In this data science project, we will analyze school performance data in Toronto as a function of income, location, school board, and whether the school is an elementary or secondary school. After that, we’ll see if we can predict the ratings given to the schools in 2017 with just those features.

Here’s a guide to creating a basic python lambda in AWS. Additionally, there is an example code for triggering this lambda using an API call, the most common type of request.

Learn NLP by following this semi-syllabus.

In this post I'll present a short code snippet demonstrating how to use Redis streams to implement a multi-process task queue with Python.

YouTube has a list of trending videos that is updated constantly. Here we will use Python with some packages like Pandas and Matplotlib to analyze a dataset that was collected over 205 days. For each of those days, the dataset contains data about the trending videos of that day. It contains data about more than 40,000 trending videos. We will analyze this data to get insights into YouTube trending videos, to see what is common between these videos. 

Python Jobs of the Week

Twyla is developing the next generation of conversational technologies. As an engineer focusing on our services and integrations, you will be responsible for delivering and maintaining a set of stateless services that can scale to cater for any number of simultaneous users from all over the globe. The team you are part of will also contribute significantly to the services strategy that enables conversation designers to create and collaborate in real-time

Interesting Projects, Tools and Libraries

Simple and ready-to-use tutorials for TensorFlow.

Reconnaissance Swiss Army Knife.

A medical imaging framework for Pytorch.

mmdetection is an open source object detection toolbox based on PyTorch. 

Stock analyzer and predictor using Elasticsearch, Twitter, News headlines and Python natural language processing and sentiment analysis.

S-RL Toolbox: Reinforcement Learning (RL) and State Representation Learning (SRL) Toolbox for Robotics.

A Flask webapp with a Mongo backend that will generate a name of a startup based on Hacker News.

Automated build repo for Python wheels.

A tool for diffing source codes directly against binaries.

High Fidelity Simulator for Reinforcement Learning and Robotics Research.

nonechucks is a library that provides wrappers for PyTorch's datasets, samplers, and transforms to allow for dropping unwanted or invalid samples dynamically.

SSRF are often used to leverage actions on other services, this framework aims to find and exploit these services easily. SSRFmap takes a Burp request file as input and a parameter to fuzz.

Upcoming Events and Webinars

There will be following talks

  • Django with CI/CD

  • How I Learned to Stop Worrying and Love atomic() 

There will be following talks

  • How traditional web frameworks can get more out of the modern cloud, by letting someone else look after the servers

  • Writing microservices using gRPC and python

During this meetup, we will become familiar with the functional programming paradigm and overview Python's support for it. The topics we are going to cover in this meeting include (among others) : Pure Functions, Lambda Expressions, Higher Order Functions and The Yield Statement.

There will be following talks

  • Python Meets Chemistry

  • Writing Python for Reproducable Research

There will be following talks

  • Documenting an API implemented with Django Rest Framework

  • GDPR at Disqus

Currently Scheduled talks:

  • A Flask Request from start to finish

  • Resizing phots

  • 10 labors of a data scientist

  • Zapier's Apache Airflow-powered Assertion Machine

  • Using Docker with Python

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