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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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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Going Beyond Medical Image Segmentation. The Road Towards Clinical Insights
Adam Klimont and Tomasz Bartczak present “Beyond Medical Image Segmentation. The Road Towards Cinical Insights” at the PyData London 2022 conference. Recent progress in deep learning for medical imaging has led to impressive results. Among them is a fully automatic human organ segmentation from Computed Tomography (CT) scans. Organ segmentation can be the end goal […]
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How Improved Text Readability Online Helps the Visually Impaired
Asya Frumkin presents “Can You Read This? (Or: How I Improved Text Readability on the Web for the Visually Impaired)” from the PyData London 2022: This talk will describe how Asya Frumkin used deep learning to identify texts on background images that are illegible for people with vision impairments. Frumkin explains the challenges ncountered when […]
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PyData Chicago: AI Autonomy: Pre- and Post-Deployment Continual Learning
Here’s a great talk from PyData Chicago on AI Autonomy.
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