2.0 -//Pentabarf//Schedule//EN PUBLISH YGS9E7@@pretalx.com -YGS9E7 Opening PyCon Sweden 2021 en en 20211021T090000 20211021T092500 002500

Opening PyCon Sweden 2021

Words from the organizers of PyCon Sweden 2021. PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2021/talk/YGS9E7/ Main Track Christine Winter PUBLISH WVW3YS@@pretalx.com -WVW3YS Keynote - Bridging Productivity, Portability, and Performance with Data-Centric Python en en 20211021T093000 20211021T103000 010000

Keynote - Bridging Productivity, Portability, and Performance with Data-Centric Python

Python is rapidly becoming the language of choice for scientific computing, due to its high productivity and vast software ecosystem. As an interpreted language, however, it is challenging to produce high-performance code from arbitrary programs. In this talk we will present the Data-Centric (DaCe) parallel programming framework (https://www.github.com/spcl/dace), a representation and workflow that enables taking (restricted) Python code and generating high-performance programs that run on multi-core CPUs, accelerators such as GPUs and FPGAs, and clusters thereof. The core concept in DaCe is an intermediate representation and interface that separates the definition of "what" to compute from "how" to compute it efficiently and map it onto hardware. The representation can then be transformed automatically, programmatically, or interactively, without modifying the original Python code. To aid code optimization, DaCe provides programmers with visual transformation and editing tools, integrated in the Visual Studio Code IDE, which allow for manual fine-tuning optimizations. DaCe successfully accelerates several frameworks and applications, including NumPy, deep learning with PyTorch/ONNX, numerical weather prediction systems, and supercomputer-scale quantum transport simulations. The generated data-centric programs are on par and outperform the existing state-of-the-art. The talk will highlight the restrictions and challenges when converting scientific code into a data-centric representation, how performance engineering is facilitated with DaCe, and showcase some of the applications that use the framework. PUBLIC CONFIRMED Keynote https://pretalx.com/pycon-sweden-2021/talk/WVW3YS/ Main Track Tal Ben-Nun PUBLISH NLUWSL@@pretalx.com -NLUWSL Keynote - Not fading away: A tale about a 20-year old Python project en en 20211021T130000 20211021T140000 010000

Keynote - Not fading away: A tale about a 20-year old Python project

The Plone CMS is one of the oldest, and most successful open source stories of the Python community. Created by Alex Limi and Alan Runyan in 1999 to be a better UI for Zope, the project grew to be a very stable and secure solution, used by governments, corporations and NGO's to power their public sites and intranets. The community surrounding Plone has been the key to keeping the project alive. This talk will focus on the community itself, and how meeting technical and organizational challenges it has adapted and evolved to avoid fading away. PUBLIC CONFIRMED Keynote https://pretalx.com/pycon-sweden-2021/talk/NLUWSL/ Main Track Érico Andrei PUBLISH XXQETS@@pretalx.com -XXQETS Panel - Careers with Python en en 20211021T143000 20211021T153000 010000

Panel - Careers with Python

A discussion with companies' recruiters from different areas about the expectations on python programmers, the trends, and the difficulties nowadays. Participants are Björn Hertzberg from H&M, Magnus Perman from Nexer, Carolina J. Säll from 46elks, Lisa Mellor from Klarna and Chris Valle from Funnel. PUBLIC CONFIRMED Panels https://pretalx.com/pycon-sweden-2021/talk/XXQETS/ Main Track Tamara Thornquist PUBLISH 8RZZJH@@pretalx.com -8RZZJH Lightning talks en en 20211021T153000 20211021T153500 000500

Lightning talks

Lightning talks PUBLIC CONFIRMED Lightning talk https://pretalx.com/pycon-sweden-2021/talk/8RZZJH/ Main Track PUBLISH 9J3AYV@@pretalx.com -9J3AYV Closing Day 1 PyCon Sweden 2021 en en 20211021T170000 20211021T171000 001000

Closing Day 1 PyCon Sweden 2021

Words from the organizers of PyCon Sweden 2021. PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2021/talk/9J3AYV/ Main Track Christine Winter PUBLISH ABFJMT@@pretalx.com -ABFJMT First steps to learn Pyspark en en 20211021T103000 20211021T120000 013000

First steps to learn Pyspark

Workshop steps: - Introduction: Motivation, intro to parallel data processing, Spark's main concepts (transformations versus actions, dataframes versus RDDs), and overall architecture, focusing on Spark SQL - Setup environment: There are two ways of executing the notebook with the exercises. The first one is creating an account on Databricks community and cloning the notebook. The alternative is running the notebook locally as described in the instructions. - Exercises: Going through a series of exercises covering Spark's main transformations (filter, select, groupBy) and ways to visualize them. The idea is to give people some time to complete each exercise and then solve it in an interactive way PUBLIC CONFIRMED Workshop https://pretalx.com/pycon-sweden-2021/talk/ABFJMT/ Workshops Natalia Pipas PUBLISH JRCLRG@@pretalx.com -JRCLRG Airflow 2.0 for ML pipelines – design, implementation and management en en 20211021T140000 20211021T153000 013000

Airflow 2.0 for ML pipelines – design, implementation and management

## Prerequisites 1. Install [Docker Desktop](https://www.docker.com/get-started) (with minimum 3GB memory allocated) 2. Start Docker engine 3. Clone the workshop repo with `git clone https://github.com/pycon-ml/airflow_workshop.git` 4. Run `docker-compose pull` inside repo folder `airflow_workshop` ## Agenda - 05 min: Introduction - 05 min: Major changes in Airflow 2.0 - 05 min: Pre-requisites setup overview - 10 min: Walkthrough of different backend components - 10 min: Different stages of a DAG file – steps and operators - 10 min: Dynamic DAG creation to improve parallelism - 15 min: How to trigger Airflow DAG runs - 15 min: Debug and clear Airflow task errors - 10 min: Overview of production-level Airflow-based architecture - 05 min: Wrap up questions PUBLIC CONFIRMED Workshop https://pretalx.com/pycon-sweden-2021/talk/JRCLRG/ Workshops Alen Jacob Scott Zhou Lini Jose Nitin Bisht PUBLISH DJ7LWR@@pretalx.com -DJ7LWR Writing Python extensions in Rust en en 20211021T153000 20211021T170000 013000

Writing Python extensions in Rust

If you are using latest [cryptography](https://cryptography.io/en/latest/) in any of your project (which you most probably already do), you are using one of the most powerful and trusted Python module where a part is written in [Rust](https://www.rust-lang.org/). In this workshop we will go through a given git repository (no prior Rust knowledge is required) and start building a Python extension module step by step. ### Outline - Initial module creation - single function - functions with arguments - Help documentation - Functions to read files - Exception generation - Dictionaries, lists - Creating your own class - A module with some real life work done in Rust We will follow prewritten code for most of the sessions, I will ask you to modify those as exercises during the session. PUBLIC CONFIRMED Workshop https://pretalx.com/pycon-sweden-2021/talk/DJ7LWR/ Workshops Kushal Das PUBLISH EGMFSZ@@pretalx.com -EGMFSZ Architecture for the extraction, automation and massive data processing en en 20211021T103000 20211021T105500 002500

Architecture for the extraction, automation and massive data processing

Due to the diversity of content on the web, its formats and technologies, the talk proposes a micro-service architecture solution built in Python, but that integrates a workflow with advanced scraping techniques and that allows the transformation of the data obtained. up to service application for NLP and ML classification. The proposal implies the use of Linux, postgresql, redis, mongodb, clickhouse, airflow, among others, but above all, their own developments and frameworks that consider not only the extraction process but also the consumption of RAM, parallel processing and even the website blocking, as well as the analysis and transformation processes of the data obtained. PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2021/talk/EGMFSZ/ Data Alfonso de la Guarda PUBLISH 9NEFHA@@pretalx.com -9NEFHA Dynamic resource allocation for machine learning jobs at H&M Group en en 20211021T110000 20211021T112500 002500

Dynamic resource allocation for machine learning jobs at H&M Group

At H&M we are using Airflow, kubernetes as main components for the machine learning workflow. The increase of the Online shopping during the last two years has impacted the data volume significantly. A lot of companies are struggling with the infrastructure cost when adopting airflow kubernetes/docker as technologies, any person interested can join to have a high level explanation of the solution H&M Group has adapted to encounter this. PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2021/talk/9NEFHA/ Data Jialun Song Amira DINARI PUBLISH KR99KF@@pretalx.com -KR99KF Fullstack datascientist v.2021 (how much of software engineering should a modern datascientist know) en en 20211021T113000 20211021T115500 002500

Fullstack datascientist v.2021 (how much of software engineering should a modern datascientist know)

Data science went from universities and research labs to small to big commercial companies in different business areas. From experimentation phase it's going to production and not everyone knows how to build teams around datascience projects, and datascientist need to know more about software engineering, especially when they have to work a lot alone, without proper support from software engineers. No longer is data science just some experimental code, and no, a jupyter notebook is not enough. The industrialization of data science required more, broader skills. We - Sergei Beilin, Ph.D., software engineering consultant in AI/ML, and his wife Natalia Beylina, Ph.D., datascientist - will go through the most important things a modern datascientist needs to know about software engineering, from both software engineer and datascientist point of views, and using our own experience. We both have Ph.Ds in mathematics and worked for quite some time in research and education, so at some point we had this experience of "research to business" mindset shift. In this talk we tried to collect our experience of working in different datascience projects and companies as well as helping others move to data science from different fields. PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2021/talk/KR99KF/ Data Sergei Beilin Natalia Beylina PUBLISH ZT793W@@pretalx.com -ZT793W 5 Recipes to Fashionable Airflow Data Engineering Pipelines en en 20211021T140000 20211021T142500 002500

5 Recipes to Fashionable Airflow Data Engineering Pipelines

In this session, we will present five recipes that have helped us scale data jobs and to reinforce as a data-driven AI-leading company. Key take-ways: - Orchestrate Architecture - Auto-Build Airflow DAG - Data Quality - Auto-Cost Evaluation - Auto-Cataloging PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2021/talk/ZT793W/ Data MENG Qiang Dahmane Sheikh Grzegorz Skibinski PUBLISH PTRPEQ@@pretalx.com -PTRPEQ Building Machine Learning demos with Python en en 20211021T143000 20211021T145500 002500

Building Machine Learning demos with Python

How can you show what a Machine Learning model does once it's trained? In this talk, you're going to learn how to create Machine Learning apps and demos using Streamlit and Gradio, Python libraries for this purpose. Additionally, we'll see how to share them with the rest of the Open Source ecosystem. Learning to create graphic interfaces for models is extremely useful for sharing with other people interesting with them. Some demo examples are: - https://huggingface.co/spaces/flax-community/dalle-mini - https://huggingface.co/spaces/flax-community/chef-transformer - https://huggingface.co/spaces/nielsr/LayoutLMv2-FUNSD PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2021/talk/PTRPEQ/ Data Omar Sanseviero PUBLISH 87ZDJ3@@pretalx.com -87ZDJ3 Building a Highly Scalable Facial Recognition Pipelines en en 20211021T150000 20211021T152500 002500

Building a Highly Scalable Facial Recognition Pipelines

Facial recognition has been a challenging task for a long time. Nowadays, we can reach and pass the human level accuracy with deep learning based state-of-the-art models. In this talk, you are going to learn how to build highly scalable facial recognition pipelines in python programming language with DeepFace library from its creator. DeepFace is the most lightweight facial recognition and facial attribute analysis (age, gender, emotion / facial expression, race / ethnicity) library for Python. It wraps many state-of-the-art face recognition models: VGG-Face, Google FaceNet, OpenFace, Facebook DeepFace, DeepID, Dlib and ArcFace. Experiments show that human beings have 97.53% score on LFW dataset whereas VGG, FaceNet, Dlib and ArcFace are passed that level already. Besides, OpenFace, DeepID and DeepFace have a close score as well. You can also build and run any one those cutting-edge models with just a few lines of code. The library got almost 2K stars on GitHub and 200K installations on PyPi / Pip. PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2021/talk/87ZDJ3/ Data Sefik Ilkin Serengil PUBLISH SV7TSD@@pretalx.com -SV7TSD Solving one of marketer’s biggest challenges using markov chain en en 20211021T153000 20211021T155500 002500

Solving one of marketer’s biggest challenges using markov chain

For any organization, measuring the value of the campaigns and channels that are reaching their potential customers is very important but not very straightforward. Data scientists/ Analysts can help in these scenarios . Data-driven attribution models can eliminate the biases associated with traditional attribution mechanisms, and understand how various messages influence potential customers and the variances by geography and revenue type. 1. Introduction to marketing attribution **(3 Minutes)** * Overview * importance * Challenges 2. Introduction to Markov chain model **(8 Minutes)** * Overview * States of the Markov Chain Model * Transition Probabilities 3. Implementation of markov chain model **(12 Minutes)** * Data Preprocessing * Calculate the Transition Probabilities * Calculate removal effects * Interpretation and Prediction * Assumptions and Limitations of Markov Model 4. Conclusion and final remarks **(2 Minutes)** * Summarizing what we discussed and discuss other sources to increase the knowledge 5. Q&A **(5 Minutes)** PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2021/talk/SV7TSD/ Data Ravi Singh PUBLISH HDVQ9U@@pretalx.com -HDVQ9U Infrastructure as code for Data Science using Python en en 20211021T160000 20211021T162500 002500

Infrastructure as code for Data Science using Python

For the compute heavy Data Science practice the adoption of Cloud and the flexibility of deploying enviroments has become a vital success factor. You can have a "supercomputer" at your fingertips for a short time and then you can decomission it again when your work is ready. But to be able to use this approach over and over again with the same configuration the actual infrastructure need to be saved in code. Of course this can be done with the help of Python and ideally this should be automated in a true DevOps and CI/CD manner. I will walk through some of my key take aways of working with infrastructure as code for Data Science projects. PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2021/talk/HDVQ9U/ Data Magnus Perman PUBLISH 7MGULT@@pretalx.com -7MGULT Make it Simple - Machine Learning in Time Series Forecasting - Development to Deployment with Python en en 20211021T163000 20211021T165500 002500

Make it Simple - Machine Learning in Time Series Forecasting - Development to Deployment with Python

Description We break down the talk into four components: 1. “The problem” - The first 5 minutes are about understanding the problem before diving into the code (2 min context of the time series challenges within demand planning in large organisations today + 3 min on time series forecasting and machine learning vs classical statistical models including the importance of good benchmark models) 2. “The setup” - The next 5 minutes are about getting set up correctly on how to analyse this before we test our models (2 min walkthrough of the structure & plan for the code (python & jupyter notebooks + 3 min in terms of data pre-processing and success metrics (comparison to benchmark) 3. “The Models” - Then the next 10 minutes are to select and test models (3 min model selection and explanation + 2 min running models + 5 min explaining & discussing results) 4. “The Deployment” - The final 5 minutes are about deployment and what the pros and cons are with these, depending on the organisation. PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2021/talk/7MGULT/ Data Olle Green PUBLISH GPFCZR@@pretalx.com -GPFCZR Advanced Flask: Recipes For An All-weather Craft en en 20211021T103000 20211021T105500 002500

Advanced Flask: Recipes For An All-weather Craft

Flask is great, lean and up to the point. A full blown Flask app presents challenges which time and again causes you to change your codebase. When embarking on Flask projects, people rarely think about having an advanded, simple setup to begin with which fines them along the road. That's why it is imperative to have a great base to start with. For companies choosing Flask as their flagship would want to lay out the groundwork with as much foresight as one can. This talk focuses on twerking the factory pattern to perfection, building painless modules for very organised codebases; going far beyond blueprints, integrating flask-restx with the overall structure, using celery as an integration testcase. It also covers nice-to-haves like the best way to handle migrations, implementing a theming system as well as JWT mechanics. A short 25 mins, feature-packed talk featuring years of usage experience. PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2021/talk/GPFCZR/ Software Abdur-Rahmaan Janhangeer PUBLISH XC7Q9H@@pretalx.com -XC7Q9H Robyn: An async python web framework with a Rust runtime en en 20211021T110000 20211021T112500 002500

Robyn: An async python web framework with a Rust runtime

Robyn is a web framework written with a runtime written in Rust. Robyn tries to achieve near-native rust performance. This talk will demonstrate the reason why Robyn was created, some of the technical decisions taken behind Robyn, the increased performance by using the rust runtime, and most importantly, how to use Robyn to develop web apps. Finally, I will be sharing the future plans of Robyn and would love to get feedback from the developers to see what they would like to see in it. PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2021/talk/XC7Q9H/ Software Sanskar Jethi PUBLISH ZMZWT9@@pretalx.com -ZMZWT9 Unpack Python Packages 📦 – Deep dive into the wheels of python packaging en en 20211021T113000 20211021T115500 002500

Unpack Python Packages 📦 – Deep dive into the wheels of python packaging

During this talk, we will explore python packaging. Before we begin our deep dive, I will provide some background and information guiding the audience into where we are today and how we ended up here. First, we will deconstruct a package and deep dive into the file format. Next, we will learn what makes up a python package, how to create a package from zero, and how to create a small tool from scratch to package up a python project and submit it to PyPI. Closing up the talk, I will introduce the reader to modern tooling that makes the process painless. The presentation aims to educate the audience in python packaging and demystify what these tools do behind the scenes. In the end, I'd like to have a Q&A summing up questions. I will publish all the code and content in a GitHub repository. The idea is that said repository can act as a handy reference guide for the audience whenever they need to look something up related to the python packaging format. Rough outline, this might be altered slightly - Introduction - Presentation of me - Outline - Why do we care? - How? - Cheatsheet - What is a Python Package? - Wheels - Distribution types - Crack the egg - What's wrong with sdist? - Deconstruct a package _(practical/demo)_ - Build a package from scratch _(practical/demo)_ - Package it up _(practical/demo)_ - Submit to (test) PyPI _(practical/demo)_ - Modern tooling - More resources - Q&A PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2021/talk/ZMZWT9/ Software Alexander Hultnér PUBLISH FPFGMC@@pretalx.com -FPFGMC Security considerations in Python Packaging en en 20211021T143000 20211021T145500 002500

Security considerations in Python Packaging

Popular programming language index websites (TIOBE index) and developer surveys (Stack Overflow) place Python as one of the fastest-growing programming languages. However, this popularity also puts in the target range of attackers. The attackers perform malicious dependency attacks and use misconfiguration tools to reveal confidential information. Jukka Ruohonen, Kalle Hjerppe, and Kalle Rindell in their research paper "A Large-Scale Security-Oriented Static Analysis of Python Packages in PyPI" claimed that they scanned PyPI for security issues in Python packages and found the presence of at least one security issue in about 46% of the Python packages. In addition, security vulnerabilities can be present in the source code of the package. In this talk, we will address the security issues related to python packaging and possible solutions to make python packages secure. The talk begins with the importance of a secure package and vulnerabilities in the Python package index. Then, I will discuss Python packages such as Bandit for identifying common security issues in Python code and “safety” for dependency check. Next, I will discuss verifying and signing Python packages using GPG. Finally, I will discuss general guidelines for secure coding practices in Python. Outline 1. Importance of a secure package and vulnerabilities in python package index. (05 Minutes) 2. Bandit for identifying common security issues in Python code (03 Minutes) 3. safety for dependency check (04 Minutes) 4. Verifying and signing PyPI and conda packages using GPG and Twine(05 Minutes) 5. General guidelines for secure coding practices in Python (05 Minutes) 6. Summary and Questions (03 Minute ) PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2021/talk/FPFGMC/ Software Gajendra Deshpande PUBLISH BD7SMY@@pretalx.com -BD7SMY Defining cloud infrastructure as Python with AWS CDK en en 20211021T150000 20211021T152500 002500

Defining cloud infrastructure as Python with AWS CDK

Configuration files used to manage Infrastructure as Code are traditionally implemented as YAML or JSON text files and are missing most of the advantages of modern programming languages. Wouldn't it be better to use the expressive power of Python to define your cloud infrastructure? The AWS Cloud Development Kit (AWS CDK) is an open-source framework from AWS that enables developers to harness the full power of modern programming languages to define reusable cloud components and provision applications built from those components using AWS CloudFormation. In this session, we'll quickly cover the basic concepts of the CDK, and then we'll spend the majority of our time building a serverless application with the CDK. We'll show you how to use the CDK to assemble your AWS infrastructure using the Python CDK quickly. PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2021/talk/BD7SMY/ Software Gunnar Grosch PUBLISH TTKFLH@@pretalx.com -TTKFLH Django security against OWASP top 10 en en 20211021T160000 20211021T162500 002500

Django security against OWASP top 10

As a web developer, using a framework that guarantees security is always great but it’s even better to measure all the vulnerabilities involved while building your application and to also know how to protect yourself from them. The Open Web Application Security Project (OWASP) is an online community that provides the top 10 vulnerabilities in web application security based on what security experts see while performing penetration testing. These vulnerabilities range from SQL injection attacks to authentication and CSRF and Django was built to minimize those security risks and give developers the ability to avoid and reduce those vulnerabilities by themselves by using better practices. It offers many security-minded functions right out of the box, without sacrificing ease of development and integration with both front-end and back-end components. We will share what we have learn so far and encourage you to try it with your own projects. We'll walk through a simple example, with screenshots and code wherever required. PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2021/talk/TTKFLH/ Software Pratibha Jagnere PUBLISH QFXNXL@@pretalx.com -QFXNXL Keynote - Managing cloud infrastructure as code in Python en en 20211022T090000 20211022T100000 010000

Keynote - Managing cloud infrastructure as code in Python

Python is a language of choice for a wide spectrum of software products nowadays. It is applied in APIs, data science models, high-performance computing, and more. But, software also needs hardware/infrastructure to run on. The good news is that you can provision and manage cloud infrastructure of any complexity without leaving the comfort of Python. In this talk, we will learn how to do exactly that while discovering the added benefits of using Python for this task. We will mainly focus on AWS but will touch other major clouds as well. PUBLIC CONFIRMED Keynote https://pretalx.com/pycon-sweden-2021/talk/QFXNXL/ Main Track Alexey Isavnin PUBLISH J7JYHA@@pretalx.com -J7JYHA Keynote - The best way to learn Python - for the absolute beginners and improvers en en 20211022T130000 20211022T140000 010000

Keynote - The best way to learn Python - for the absolute beginners and improvers

In the 2021 StackOverflow survey, Python remains the most wanted programming language (5 years in a row). As a Pythonista, I have been asked many times about what is the best way to learn Python. There are many resources, especially online, free or paid, that is available. From working as a data scientist to become an open-source developer, I would like to sum up my Python learning experience to make recommendations to different groups of people. Whether you are an absolute beginner with no programming experience, a beginner with other programming language experience or a beginner in Python who wants to get better and know more about the language, you will get an idea of how to use Python at a whole new level. PUBLIC CONFIRMED Keynote https://pretalx.com/pycon-sweden-2021/talk/J7JYHA/ Main Track Cheuk Ting Ho PUBLISH 7PUWHD@@pretalx.com -7PUWHD Closing PyCon Sweden 2021 en en 20211022T153000 20211022T155500 002500

Closing PyCon Sweden 2021

Words from the organizers of PyCon Sweden 2021. PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2021/talk/7PUWHD/ Main Track Steven Chien Christine Winter PUBLISH PP8L7D@@pretalx.com -PP8L7D Build an answering machine with Flask 📞 en en 20211022T100000 20211022T113000 013000

Build an answering machine with Flask 📞

<b>What will we be accomplishing during this code along:</b><br>When you receive a phone call, if you're not available the caller hears a recording asking them to leave a message. If the caller leaves a message, the audio file is downloaded to a specific folder. When you are available, the call is forwarded to your real mobile number 📞 PUBLIC CONFIRMED Workshop https://pretalx.com/pycon-sweden-2021/talk/PP8L7D/ Workshops Carolina J. Säll Victoria Wagman PUBLISH 993BDA@@pretalx.com -993BDA Zero To Hero Tutorial on a Deep Learning Classification Task en en 20211022T140000 20211022T153000 013000

Zero To Hero Tutorial on a Deep Learning Classification Task

A Zero-To-Hero workshop that will demonstrate how to solve classification tasks on datasets and tasks of increasing complexity. The workshop will present both how to solve the tasks and how to structure a codebase according to software development best practices. The first challenge presented in the workshop will be the classification of synthetically generated Gaussian blobs. First, we will be classifying low-dimensional Gaussian blobs and then we will extend the algorithm to higher-dimensional blobs. Moreover, the demo will also showcase Tensorboard as a tool to monitor model learning. The model presented in this initial part of the tutorial is the Fully Connected Multi-Layer Perceptron (MLP), the most well-known type of neural network. The second challenge will be the classification of garments from images. To this end, we will use the well-known FashionMNIST dataset. The model presented in this second part is the Convolutional Neural Network (CNN), the mainstream neural network for image analysis. After solving the classification tasks, we will demonstrate how to obtain numerical embeddings from the CNN that can be used to solve a multitude of downstream tasks. We will demonstrate how these embeddings can be used to find and cluster similar items. In the third and final part of the workshop, we will work with graph data and try to predict whether two authors of scientific papers are co-authors or not. We will demonstrate this task using the collab open source dataset, that is, a graph where the nodes will represent the authors and the edges will connect the co-authors. The problem we will solve is framed as a link prediction task, that is, to a certain extent, analogous to a classification task since we are going to try to predict whether an edge exists or not between two nodes. The model that will solve this task consists of two stages. The model presented in this final part of the tutorial is the Graph Neural Network (GNN), a powerful formalism for analysing graph data. The models are going to be implemented using PyTorch-Lightning, that enforces a modular and maintainable software structure. PUBLIC CONFIRMED Workshop https://pretalx.com/pycon-sweden-2021/talk/993BDA/ Workshops Georgios Deligiorgis Marco Trincavelli David Andersson PUBLISH 9GGSNU@@pretalx.com -9GGSNU Is the news media polarized? Or are we conditioned to think it is? en en 20211022T100000 20211022T102500 002500

Is the news media polarized? Or are we conditioned to think it is?

As social media sites across the world grapple with hate speech, calls for genocides and sexual harassment on their platforms, as policy scientists look up the various biases in our justice system’s usage of language and as most of the people in the world struggle with what is globally called ”media bias”, I believe that as mathematics and statistics became commonplace measures, so will Machine Learning models. This work is an example of an intersection of a non scientific field with computer science and mathematics, trying to quantify, measure and identify non mathematical phenomena in the language of mathematics. It is important because it could be the basis of the scientific approaches that the next generation policy makers, voters, non profit social organizations and governments could use to make life changing decisions for their citizens. 2 The questions that this study tries to answer is whether a neural network can learn biases from the news media based on perceived bias scores obtained from independent agencies. It also seeks to answer whether any of these political leanings of the news media affect the vulnerability of their consumer when it comes to fake news. The results of this experiment aim to show Conclusions 1. SVMs perform better clustering with respect to the categories than neural networks, however the maximum does not cross 67% 2. The most significant conclusion from this work is that though there is a perceived bias when it comes to news agencies, when looked at from a neural networks standpoint, it is negligible. Mainstream news agencies are not able to polarize a neural network with inherent biases in their headlines. 3. There may be topical biases that need to be examined by using an Entity linking and bias calculation approach 4. Most mainstream news agencies do not make the consumer vulnerable to believing fake news. This study needs to be conducted with data from popular social media ”news” groups or popular TV shows that masquerade as news but may technically not even be news channels. 5. It is safe to conclude that the perceived bias that stems from social media polarization is being extended to news media when their contribution to the polarization may be negligible. Significance of Work 1. The significance of this work is to be able to transform a social problem into a technical one and using neural networks and Machine learning techniques to try to gain some insights. 2. Hopefully using these techniques to find deeper trends will become mainstream and help policy makers and the general citizens approach news media bias in a better light. Future Work Some further studies to take up are as follows: 1. Effect of news media on Perceptron Networks 2. Better Annotated Datasets to perform bias analysis 3. Effect on memory models of media bias 4. Experiment on some of the most polarizing news epochs in time 5. Studies on country level news bias PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2021/talk/9GGSNU/ Data Aroma Rodrigues PUBLISH RPYSGE@@pretalx.com -RPYSGE Using optimal decision making tools for balancing in-game economies en en 20211022T110000 20211022T112500 002500

Using optimal decision making tools for balancing in-game economies

In this talk, we will go through several aspects of the end-to-end optimization workflow: - What is optimization - The different Python libraries that can be used to perform optimization in practice - How to optimize an in-game economy - How this task differs from other optimization workflows (e.g. tuning hyperparameters etc.) - Case study: using Nevergrad to create optimal reward packs in Candy Crush Soda PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2021/talk/RPYSGE/ Data Maria Paskevich PUBLISH 7FCJMW@@pretalx.com -7FCJMW Towards causality without the use of controlled experiments in e-commerce en en 20211022T140000 20211022T142500 002500

Towards causality without the use of controlled experiments in e-commerce

Controlled experiments such as A/B tests are a gold standard for determining whether changes to a website significantly impacted user behaviour, however they are not always possible. In this talk we walk through a iPython Notebook and describe a non-parametric method for determining whether changes to e-commerce product pages impacted conversion to basket without the use of controlled experiments. PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2021/talk/7FCJMW/ Data Emir Uz PUBLISH J9YM9F@@pretalx.com -J9YM9F Pro Python tips for Data Analysts en en 20211022T143000 20211022T145500 002500

Pro Python tips for Data Analysts

Pandas, MatPlotLib and scikit-learn are fantastic libraries. Glue them together with a little bit of Python and you can do so many things. Your favourite search engine fills in the gaps when you're stuck. You wield your favourite weapons in the war against meaningless data with ease and style. So what can you learn from someone who started coding in the late '70s? What could an experienced Python trainer/engineer possibly know that you can't find online yourself? Whilst doing data analysis projects I regularly drop back on my Python and computer science knowledge. Sometimes a few lines of code will be enough to solve a tricky data cleaning challenge. Knowing how to write functions stops me from getting lost in many copies of very similar code. Combining the ease of Jupyter cells with the rigours of clean code lets me write large programs without tearing my hair out. Doing a quick analysis is easy. Writing code which is still useful in years to come, which evolves without degrading into a big mess, takes experience. I will share examples of how I've used pure Python in my data analysis and give you simple tips on applying software development best practices to your code. Let's learn from each other. Telling people with much more data analysis experience than myself how to do their job feels a little scary. There may be a better way that I've missed. If so, please tell me after the talk - just be gentle - thanks ;-) PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2021/talk/J9YM9F/ Data Coen de Groot PUBLISH UJMLVE@@pretalx.com -UJMLVE Implementing Mask RCNN to identify defects in wood cuts and understand wood hardiness en en 20211022T150000 20211022T152500 002500

Implementing Mask RCNN to identify defects in wood cuts and understand wood hardiness

Wood cutting properties for the chains of chainsaw is measured in the lab by analysing the force, torque, consumed power and other aspects of the chain as it cuts through the wood log. One of the essential properties of the chains is the cutting efficiency which is the measured cutting surface per the power used for cutting per the time unit. These data are not available beforehand and therefore, cutting efficiency cannot be measured before performing the cut. The efficiency of the chainsaw is closely related to wood hardness and defects like knots and cracks are very important properties when measuring wood hardness. Mask-RCNN is a widely used machine learning model in computer vision that is used to perform instance segmentation. In my work Mask RCNN was used to identify each instance of knots and cracks in a wood cut and then the instance information was used to understand wood hardness. OpenCV was used to perform image processing, open source platform tensorflow and libraries like sklearn, matplotlib, numpy were used to implement the model, perform the tasks and visualise the result. Therefore, I think it can interest other python and machine learning enthusiasts to know about my work. PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2021/talk/UJMLVE/ Data Md Tahseen Anam PUBLISH MDVUSZ@@pretalx.com -MDVUSZ Python++? Here's how, and why not en en 20211022T100000 20211022T102500 002500

Python++? Here's how, and why not

Brief outline of the talk: - Short comparison of Python with other common languages like C++ or Java, and pythonic features they've received in recent years - Recent updates to Python, and why we don't need to manually do those anymore - like match-case and walrus operator. - Implementing std::cout and std::cin in python using special classes. This is actually quite simple to do. - Implementing final decorator and type hint to achieve "constants". This can already be found in libraries on pip. How to break them. - Marking methods as private - this can be done through decorators. - Why you really shouldn't be doing this – What is considered pythonic? What does it mean? Why are other languages like they are? Will include code examples, using the inspect module, some python hacks, and other ideas on the same topic. Obviously with a comedic touch, but the main idea is to educate on python fundamentals and differences in programming paradigms. PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2021/talk/MDVUSZ/ Software Marcus Näslund PUBLISH PRWSVC@@pretalx.com -PRWSVC Rules Rule (Creating and Using a Rules Engine) en en 20211022T103000 20211022T105500 002500

Rules Rule (Creating and Using a Rules Engine)

This talk introduces the concept of rules and rules engines as well as a way of implementing this in Python. Building on top of the initial, simple implementation, it is then further demonstrated how to expand it to handle additional use cases that goes beyond the basic aim of refactoring nested conditional constructs such as if...else, including parallel execution of your rules. The talk is suitable for anyone using Python as the concepts are pedagogically introduced and at the same time the overarching concept is a powerful tool. PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2021/talk/PRWSVC/ Software Lennart Fridén PUBLISH BMPR7N@@pretalx.com -BMPR7N self.tracking and self.improving using your habits with python ecosystem and low-code tools en en 20211022T110000 20211022T112500 002500

self.tracking and self.improving using your habits with python ecosystem and low-code tools

There is a trend of modern time-tracking and self-improving screaming at you from every corner and messing up with your plans to watch the latest season of Rick & Morty. In this talk I want to show how one can use python [and a few other middle-code/low-code tools] so that you can have easier time tracking your habits and personal skills and plans. We will go through building a simple dashboard and how one can approach a side project like that PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2021/talk/BMPR7N/ Software Igor Mosyagin PUBLISH YTLMGX@@pretalx.com -YTLMGX Python as an OOP teaching tool in the Information Systems course at the State University of Minas Gerais (Brazil) en en 20211022T140000 20211022T142500 002500

Python as an OOP teaching tool in the Information Systems course at the State University of Minas Gerais (Brazil)

In this conversation, we will talk about the seven steps of the methodological proposal to practically implement the use of Python, increase student engagement and make use of real examples and practices: Methodological Proposal Lectures 1. Easy to get started 2. Clear problem definition 3. Growing difficulty 4. Collaborative work 5. Market Practices 6. Multidisciplinary 7. All open and published 8. Online classes PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2021/talk/YTLMGX/ Software Tiago Bacciotti Moreira PUBLISH 7DBTK9@@pretalx.com -7DBTK9 Build custom robot in ROS en en 20211022T143000 20211022T145500 002500

Build custom robot in ROS

The point of using ROS is to create a robotics standard, so that you don’t need to reinvent the wheel anymore when building a new robotic software. In this talk, you will learn to create a robot from scratch using URDF and XACRO files. This will enable us to define the inertial, visual and collision aspects of the robot and also we will be able to integrate sensors into it. The method discussed will be very generic and can be used in other bots as well. Along with building robots, we will also learn to create a new world in a gazebo where we will spawn our robot. This robot will then be controlled using python and we will also capture the video coming from the camera and do image processing over it in python. This will be a major building block towards building smart robots. PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2021/talk/7DBTK9/ Software Harsh Mittal PUBLISH H399LQ@@pretalx.com -H399LQ Zaunic : Acceleration Simplified with Python en en 20211022T150000 20211022T152500 002500

Zaunic : Acceleration Simplified with Python

Completely revamping the solution accelerator demoed at PyCon Sweden 2020, this year's talk is focused on taking a closer look at a lighter, more slick, easier-to-manage and scale framework, "Zaunic". Zaunic breaks down the code into "templates" and "data" that is injected into templates. Multiple files and project folders can be now easily generated by using YAML based books. The books make the whole process repeatable and easier to manage. Teams working towards onboarding new team members or helping existing ones, should find this intuitive. The demo shows how a Python based framework is being leverage to generate Elixir application code and Infrastructure as Code. For teams that intend to follow maintain high level of consistency and strict patterns in terms of code/resource nomenclature, this is an extremely important useful tool. Feel free to pull down code, experiment and add your own books and templates. The project contains samples to get started: https://github.com/razaibi/zaunic PUBLIC CONFIRMED Talk https://pretalx.com/pycon-sweden-2021/talk/H399LQ/ Software RAZA BALBALE