An AI assistant for football analytics – Petar Veličković (Google DeepMind)
This video from PyData shows that even sportscasters need to fear the AI.
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Jan van der Vegt takes us on a walk through the isolation forest
This video is from PyData. Anomaly detection can help with fraud detection, predictive maintenance and cyber security cases amongst others. Next to this it can help on a meta level for other machine learning projects by detecting outliers during training or inference. One of the approaches to anomaly detection is called Isolation Forests. In this […]
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New Features You Need To Know In Python 3.12
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In this video, ArjanCodes not only dive into the thrilling new features and improvements coming our way in Python 3.12 but also discusses some elements that will be removed in the upcoming release.
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Thomas Bierhance: Polars – make the switch to lightning-fast dataframes
In this talk from PyData, get a report on our experiences switching from Pandas to Polars in a real-world ML project. Polars is a new high-performance dataframe library for Python based on Apache Arrow and written in Rust. We will compare the performance of polars with the popular pandas library, and show how polars can […]
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Discovering Inspirational Insights in Motivational Sports Speeches Using Data Science
PyData presents this talk on Discovering Inspirational Insights in Motivational Sports Speeches Using Data Science. nspirational sports speeches have motivated and reinvigorated folks for years. Whether you’re a developer or an athlete, they’ve withstood the journey because even the smartest, the bravest, and the most resilient need some encouragement on occasion. During our time together, […]
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Grover’s Quantum Search for Data Science and Why should we Care
Tigran Sedrakyan on Grover’s Quantum Search for Data Science and Why should we Care Among the most prominent achievements of the quantum computing field is an algorithm known as Grover’s quantum search. This talk focuses on Grover’s algorithm and its applications to machine learning routines. Prior knowledge required is a basic understanding of linear algebra and […]
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A Subset-Based Strategy for Faster AutoML
Teddy Lazebnik, CTO@DataClue recently presented this talk at PyData Tel Aviv. Automated machine AutoML learning frameworks have become important tools in the data scientists’ arsenal. However, when the dataset is large, the overall AutoML running times become increasingly high. In this lecture, we present AutoML optimization strategy that tackles the data size, rather than configuration […]
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Why you Should Care about Data-Centric AI
From PyData London 2022 Marysia Winkels presents: “Models Schm-odels: Why You Should Care About Data-Centric AI” Data Centric AI is the term coined by AI pioneer Andrew Ng for the movement that argues we shift our focus towards iterating on our data instead of models to improve machine learning predictions. But isn’t this what we […]
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Audio Neural Networks without Ground Truth: Avoid Humans in the Loop
From the PyData London 2022 conference Orian Sharoni speaks about Audio Neural Networks without Ground Truth: How to Avoid Humans in the Loop at all Costs. Manual listening tests are great but they’re time consuming, mission specific and expensive. We all want good quality automated testing measurements to better our algorithms but can we truly get […]
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