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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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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Python vs Julia
Python and Julia are both common and powerful language that may seem alike, but there are definitely differences you should consider. In this video Martin Keen, Master Inventor, provides an overview of Python and Julia, showcasing their strengths so you can make an informed decision on the best for your next project. [Video is from […]
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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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JuliaCon 2022 Live Session Day 1 (Open Remarks, Erin LeDell, Julia Computing, etc.)
Live stream from day 1 of JuliaCon 2022.
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Statistics in Julia
Statistics is a domain where some early stage development of packages, and some early applications, have come about in Julia. From Ajay Shah at JuliaCon 2022 We think of this mini-symposium as a combination of (a) Report on many interesting recent developments in this field and (b) Offer a birds eye view to the people […]
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Introduction to Julia
This workshop is geared towards anyone who wants to start using Julia.
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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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