Deep Learning Python

AIQC: Framework for Reproducible and Rapid Deep Learning

AIQC simplifies data preparation and parameter tuning for batches of deep learning models without an expensive cloud backend. It empowers researchers by reducing the programming and data science know-how required to integrate machine learning into their research. It makes machine learning less of a black box by reproducibly recording experiments in a file-based database that […]

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Covid Pandemic Python

Fighting COVID with Python

Daan de Bruin’s presentation at PyData Eindhoven 2021 shows how Python can be used in the fight against COVID.

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Machine Learning Python

Introduction to Machine Learning with Time Series

Time series are ubiquitous in real-world applications, but often add considerable complications to data science workflows. What’s more, most available machine learning toolboxes (e.g. scikit-learn) are limited to the tabular setting, and cannot easily be applied to time series data. In this tutorial, you’ll learn how to apply common machine learning techniques to time series […]

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AI Natural Language Processing Python

Transfer Learning – Entering a New Era in NLP

Malte Pietsch delivers this keynote on “Transfer Learning – Entering a new era in NLP” at PyData Warsaw 2019 Transfer learning has been changing the NLP landscape tremendously since the release of BERT one year ago. Transformers of all kinds have emerged, dominate most research leaderboards and have made their way into industrial applications. In […]

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Data Science Developer

Maintainable Code in Data Science

In this PyData London talk,  Kevin Lemagnen covers something that I’ve long wondered about: the maintainability of code created in data science projects. Notebooks are great, they allow to explore your data and prototype models quickly. But they make it hard to follow good software practices. In this tutorial, we will go through a case […]

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Deep Learning Python

PyTorch 1.0: Now and in the Future

Adam Paszke speaks at PyData Warsaw 2018 about PyTorch, one of the main tools used for machine learning research. It’s been developed in beta mode for over 2 years, but this October, a release candidate for 1.0 version has been finally released. In this talk, Adam briefly introduces the library, and then move on to […]

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Using Python in Weather Forecasting

Stephan Siemen shows you how to make your own weather forecasts in Python.

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Data Science Python

Predictive Customer Lifetime Value Models in Python

Implementing and Training Predictive Customer Lifetime Value Models in Python are covered in this talk by Jean-Rene Gauthier and  Ben Van Dyke. Customer lifetime value models (CLVs) are powerful predictive models that allow analysts and data scientists to forecast how much customers are worth to a business. CLV models provide crucial inputs to inform marketing […]

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Pandas Python

Introduction to Pandas

Daniel Chen presents an introduction to Pandas at the PyData Carolinas conference last year.

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10 Things You Really Should Know About Jupyter Notebooks

Jupyter notebooks are great. They are interactive, customizable and can be made to beautifully illustrate data. Unfortunately only a small fraction of data scientists takes the full advantage of the possibilities that they bring. In this talk, Jakub Czakon shows you some of the coolest notebook features that will impress your peers, dazzle your clients […]

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