Abridged metaprogramming classics - this episode: pytest
Oliver Bestwalter
Algorithms, Code-Review, APIs, Use Cases

Abridged metaprogramming classics - this episode: pytest. About the role of metaprogramming in the creation of a simple to use but powerful testing framework.

Algo.Rules - How do we get the ethics into the code?
Carla Hustedt
Algorithms

Algo.Rules - How do we get the ethics into the code? 9 rules for the design of algorithmic systems

An Introduction to Concurrency and Parallelism using Python Programming Language
Tanmoy Bandyopadhyay
Algorithms, Parallel Programming

Write simpler, faster code with Python concurrency and parallelism..

Automating feature engineering for supervised learning? Methods, open-source tools and prospects.
Thorben Jensen
Artificial Intelligence, Algorithms, Data Science, Machine Learning, Data Engineering

How to automate the labor-intensive task of feature engineering for Machine Learning? This talk gives an overview on methods, presents open-source libraries for Python, and compares their performance.

Avoiding ML FOBO
Rachel Berryman, Dânia Meira
Algorithms, Business & Start-Ups, Data Science, Machine Learning

Is FOBO (Fear Of Better Option) preventing you from delivering practical ML products? Join 'Avoiding ML FOBO' to learn tips for cutting through the hype.

Beyond Paradigms: a new key to grok Python & other languages
Luciano Ramalho
Algorithms, Code-Review

#BeyondParadigms: Languages like Python and Go don't fit programming paradigm categories very well. A more pragmatic and practical way to understand languages is focusing on features. This is what "Beyond Paradigms" is about.

CANCELED: Create CUDA kernels from Python using Numba and CuPy.
Valentin Haenel
Algorithms, Big Data, Data Science, Parallel Programming

Learn to program GPUs (e.g. Kernels) in Python with CuPy and Numba.

CANCELLED: First steps in Julia
Felicia Burtscher
Artificial Intelligence, Algorithms, Deep Learning, Data Science, Networks, Machine Learning, Science

#julia_introduction. why julia is better than python. machine learning made eady with juliabox.

Embrace uncertainty! Why to go beyond point estimators for valuable ML applications
Stefan Maier
Algorithms, Data Science, Machine Learning, Statistics

Usually, uncertainties of Machine Learning predictions are just regarded as a sign of poor prediction accuracy or as a consequence of lacking input features. This talk illustrates how modeling uncertainties can improve ML based decisions.

Equivariance in CNNs: how generalising the weight-sharing property increases data-efficiency
Marysia Winkels
Artificial Intelligence, Algorithms, Computer Vision, Deep Learning, Data Science, Machine Learning, Science

Equivariance in CNNs: how generalising the weight-sharing property increases data-efficiency

Gaussian Process for Time Series Analysis
Dr. Juan Orduz
Algorithms, Data Science, Machine Learning, Statistics

Gaussian process for regressions problems and time series forecasting

Gaussian Progress
Vincent Warmerdam
Artificial Intelligence, Algorithms, Data Science, IDEs/ Jupyter, Machine Learning, Statistics

gaussian progress. it's meta, but also the most normal conference title this year!

Get to grips with pandas and scikit-learn
Sandrine Pataut
Algorithms, Data Science, Machine Learning

Get to grips with pandas and scikit-learn: a first contact with data science using python

Hidden Markov Models for Chord Recognition - Intuition and Applications
Caio Miyashiro
Algorithms, Data Science, Machine Learning, Statistics

Come check out Caio's workshop on music+programming+stats on PyData

How strong is my opponent? Using Bayesian methods for skill assessment
Darina Goldin
Algorithms

Introduction to the ranking algorithms Elo, Glicko2, and Trueskill.

How to write tests that need a lot of data?
Sander Kooijmans
Algorithms, Code-Review

In this talk Sander explains how to write tests that need a lot of data using code of a warehouse management system as example.

Leveraging the advantages of Bayesian Methods to build a data science product using PyMC3
Korbinian Kuusisto
Algorithms, Business & Start-Ups, Data Science, Machine Learning, Science, Statistics

How can one leverage the power of Bayesian methods to build a successful data science product?

Loss Function Theory 101
David Wölfle
Artificial Intelligence, Algorithms, Deep Learning, Data Science, Machine Learning, Statistics

This talk covers the theoretical background behind two common loss functions, mean squared error and cross entropy, including why they are used for machine learning at all, and what limitations you should keep in mind.

Practical DevOps for the busy data scientist
Dr. Tania Allard
Algorithms, Big Data, Data Science, DevOps, Machine Learning

Devops for the busy data scientist: learn how to leverage these practices to improve your workflows

Python-Powered OSINT! Modernising Open Source Intelligence for Investigating Disinformation
Chiin-Rui Tan, Dare Imam-Lawal
Artificial Intelligence, Algorithms, Data Science, Machine Learning, Web, Data Mining / Scraping, Use Cases

Socio-Technical Python for OSINT! The old state discipline of gathering intelligence from open sources is today critical for investigating Disinformation but has lacked modernisation. A former UK Gov Head of DataSci presents a maturity model for updating legacy OSINT with Python!

Quantum computing with Python
James Wootton
Algorithms, Infrastructure, Microcontrollers, Science, APIs

Every Python user can play with one of the world's most advanced technologies: quantum computers. This session will tell you how you can and why you should.

Should I stay or should I go? Optimal exercise decisions using the Longstaff-Schwartz algorithm
Benedikt Rudolph
Algorithms, Business & Start-Ups, Data Science, Science, Statistics

Learn about a simple least-squares approach to evaluate financial exercise options and make optimal exercise decisions.

Take control of your hearing: Accessible methods to build a smart noise filter
Peggy Sylopp, Aislyn Rose
Artificial Intelligence, Algorithms, Computer Vision, Deep Learning, Data Science, Machine Learning, Science

Control what you hear with deep learning and open audio databases. The developer and manager of \\NoIze//, a project supported by Prototype Fund, share what’s helped them build an open source smart, low-computational noise filter in Python.

Time Series Anomaly Detection for Bottling Machine Maintenance
Andrea Spichtinger
Algorithms, Data Science, Machine Learning

Anomaly detection in time series data from mechanical motors in bottling machines, set productive on an AWS edge device. #AnomalyDetection #UnsupervisedML #AWS

Why you don’t see many real-world applications of Reinforcement Learning.
Yurii Tolochko
Artificial Intelligence, Algorithms, Deep Learning, Machine Learning, Statistics

Why doesn’t RL show the same success as (un)supervised learning? Inherent difficulties facing RL and avenues for future work

Write your Own Decorators
Mike Müller
Algorithms

Learn how to write useful decorators in a hands-on tutorial.

Your Name Is Invalid!
Miroslav Šedivý
Algorithms, Community, Natural Language Processing, Web, Data Mining / Scraping, Use Cases

If your code tells me “Your Name Is Invalid!”, then your code is probably invalid. Names of people cannot be invalid.

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