Welcome to issue 288 of Python Weekly. Let's get straight to the links this week.

Articles, Tutorials and Talks

pdb Tutorial
A simple tutorial about effectively using pdb.

Using Deep Learning to Classify White Blood Cells 
What would healthcare look like if you could discover your blood cell count as quickly and cheaply as your temperature? At Athelas, we are fascinated by this possibility and believe that such a future is within sight thanks to modern Deep Learning techniques. In this post, we’ll demonstrate a simple toy example of how we can leverage deep learning techniques to classify white blood cell images. Our example model will classify white blood cells as Polynuclear or Mononuclear with an accuracy of 98% on our reference dataset.

Episode #105: A Pythonic Database Tour
There are many reasons it's a great time to be a developer. One of them is because there are so many choices around data access and databases. So this week we take tour with our guest Jim Fulton of some databases you may not have heard of or given a try. You'll hear about the pure Python database ZODB. There's Zero DB, an end-to-end encrypted database in which the database server knows nothing about the data it is storing, and NewtDb spanning the world of ZODB and JSON friendly Postgres. 

Python's Instance, Class, and Static Methods Demystified
In this tutorial I’ll help demystify what’s behind class methods, static methods, and regular instance methods. If you develop an intuitive understanding for their differences you’ll be able to write object-oriented Python that communicates its intent more clearly and will be easier to maintain in the long run.

Build a talking, face-recognizing doorbell for about $100
DIY with Amazon Echo and Raspberry PI: Recognize thousands of people at your door every month, for pennies.

Podcast.__init__ Episode 102 - Digital Identity, Privacy, and Security with Brian Warner 
As the internet and digital technologies continue to infiltrate our way of life, we are forced to consider how our concepts of identity and security are reflected in these spaces. Brian Warner joins me this week to discuss his work on privacy focused projects that he has worked on, including the Tahoe LAFS, Firefox Sync, and Magic Wormhole. He also has some intriguing ideas about how we can replace passwords and what it means to have an online identity.

How to Handle Missing Data with Python
Real-world data often has missing values. Data can have missing values for a number of reasons such as observations that were not recorded and data corruption. Handling missing data is important as many machine learning algorithms do not support data with missing values. In this tutorial, you will discover how to handle missing data for machine learning with Python.

Get started developing workflows with Apache Airflow
Apache Airflow is an open-source tool for orchestrating complex computational workflows and data processing pipelines. If you find yourself running cron task which execute ever longer scripts, or keeping a calendar of big data processing batch jobs then Airflow can probably help you. This article provides an introductory tutorial for people who want to get started writing pipelines with Airflow.

Dask and Pandas and XGBoost 
This post talks about distributing Pandas Dataframes with Dask and then handing them over to distributed XGBoost for training. More generally it discusses the value of launching multiple distributed systems in the same shared-memory processes and smoothly handing data back and forth between them.

Fantastic GANs and where to find them
Have you ever wanted to know about Generative Adversarial Networks (GANs)? Maybe you just want to catch up on the topic? Or maybe you simply want to see how these networks have been refined over these last years? Well, in these cases, this post might interest you.

Escaping a Python sandbox with a memory corruption bug

A Magical Introduction to Classification Algorithms

Introducing Box – Python dictionaries with recursive dot notation access 

DeepDream: Accelerating Deep Learning With Hardware


Computational and Inferential Thinking
This is the textbook for the Foundations of Data Science class at UC Berkeley. The course combines three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social issues surrounding data analysis such as privacy and design.

Interesting Projects, Tools and Libraries

A fully functional local AWS cloud stack. Develop and test your cloud apps offline!

The aim of the Golem project is to create a global prosumer market for computing power, in which producers may sell spare CPU time of their personal computers and consumers may acquire resources for computation-intensive tasks. In technical terms, Golem is designed as a decentralised peer-to-peer network established by nodes running the Golem client software. For the purpose of this paper we assume that there are two types of nodes in the Golem network: requestor nodes that announce computing tasks and compute nodes that perform computations (in the actual implementation nodes may switch between both roles).

Computer Vision Drone
Building an interactive computer vision drone.

Python one-liners: Awk-like one-liners for python.

A report card for your Python application. This inspects a python project is hosted on Github and analyze the source code quality (pep8, pyflakes and bandit etc.), existence of license file, and some useful statistics of whole codebase. Then shows its analysis results on web.

It understands your voice commands, searches news and knowledge sources, and summarizes and reads out content to you.

Recurrent Neural Networks - A Short TensorFlow Tutorial.

Easy Swagger UI for your Flask API.

django-cruds is simple drop-in django app that creates CRUD for faster prototyping.

This code implements a deep learning architecture based on Residual Network that dynamically adjusts the number of executed layers for the regions of the image. The architecture is end-to-end trainable, deterministic and problem-agnostic. The included code applies this to the CIFAR-10 an ImageNet image classification problems. It is implemented using TensorFlow and TF-Slim.
A lightning fast multithreaded network scanner framework with modules.

PyMedium is an unofficial Medium API written in python flask. It provides developers to access to user, post list and detail information from Medium website. This is a read-only API to access public information from Medium, you can customize this API to fit your requirements and deploy on your own server.

Upcoming Events and Webinars

Online Course: Audio Signal Processing for Music Applications
In this course you will learn about audio signal processing methodologies that are specific for music and of use in real applications. We focus on the spectral processing techniques of relevance for the description and transformation of sounds, developing the basic theoretical and practical knowledge with which to analyze, synthesize, transform and describe audio signals in the context of music applications.

New York Python Meetup April 2017 - New York, NY
There will be following talks

  • What is new in Pandas!
  • Theano and Python! 

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