Sprints & Workshops

Hands on python-cryptography by Tomáš Babej

Have you ever wanted to secure your python application with some cryptography, but got confused by APIs which were too low level? Python-cryptography is a new emerging library which strives to provide "crypto for humans" in Python. In this workshop, we will explore how to use it to secure our applications in a reliable manner. No substantial cryptography knowledge required.

Web scraping by Tomáš Bedřich

We will try out the process of website scraping including HTML and AJAX inspection; usage of requests and BeautifulSoup modules. The workshop will be divided into theoretical section and 4 examples, mainly focused on begginers. The only required knowledge is Python and HTML.

Python & OpenStack - easy way to create a remote controller for OpenStack by Martin Korbel

If you are thinking about integration of OpenStack in your application and you afraid that it is so difficult. Don't worry, it is very easy when we can use the novaclient module. We will show how can you upload your public ssh key, create a new instance, join it to a security group, make a snapshot, and do many other tasks. We will present all these things in a simple example, where we will create our own “remote controller of OpenStack”.

GIS in Python by Jachym Cepicky

Python is one of the most popular programming languages, with strong support for today's GIS technologies. This workshop will give you overview of some basic libraries and their Python bindings (GDAL, Proj4), more abstract and easier to be used libraries (Rasterio, Fiona, Shapely), and show you how to access GeoServer and Mapserver from Python scripts. OGC Standards and how to deal with them will be demonstrated using OWSLib.

Of course, the topic is so wide, that at the end nobody is expected to master the mentioned (or not mentioned) technologies. This workshop is meant as an introduction to the world of GeoPython, and a brief overview.

Event-Sourced Domain Models in Python by Robert Smallshire

In first part of this workshop we explore how to implement rich domain models using plain-old Python objects which are completely independent of any particular persistence technology such as an object-relational mapper. Domain models often embody the core value of software systems, so implementing models independently of - often ephemeral - technology choices is an important strategy for long-lived, high-value systems.

In the second part of this workshop we implement an event-sourced architecture for persistence of our domain model, whereby all changes applied to the model are recorded as events in a simple, append-only event-store. From this event store we can reconstitute the model state as it was at any historical time, something that is difficult – if not impossible – to do with most object-relational solutions. Furthermore, we can project the event stream into other representations to support queries which are not conveniently supported by the entities in our model, or which roll-up historical data. Among many other benefits, such projected read-models gives very high scalability on the read-side

This workshop is aimed at Python developers who want to step beyond the limits of canned framework-based solutions such as Django models, or toolkits such as SQLAlchemy, (which are ultimately about managing the horrors of shared-mutable state). Event-sourced domain models facilitate allow us to combine the best aspects of object-oriented design with robust and simple persistence inspired by functional-programming practice.

Introduction to Deep Learning for Natural Language Processing by Nischal HP

In fields like computer vision, speech recognition and natural language processing, deep learning has produced state-of-art results. And they are showing lot of promise in other fields too. This workshop will provide an introduction to deep learning for natural language processing (NLP). It will cover some of the common deep learning architectures, describe advantages and concerns, and provide some hands-on experience.

Content

1. What is deep learning?
2. Motivation: Some use cases where it has produced state-of-art results for NLP
3. Introduction to neural networks; backpropagation algorithm
4. Supervised learning: Text classification using multi-layer perceptron, recurrent neural networks
5. Text generation using recurrent neural networks
6. Impact of GPUs