The Data Exchange with Ben Lorica
Un pódcast de Ben Lorica - Jueves
Categorías:
256 Episodo
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Large-scale machine learning and AI on multi-modal data
Publicado: 7/10/2021 -
Machine Learning in Astronomy and Physics
Publicado: 30/9/2021 -
The Unreasonable Effectiveness of Multiple Dispatch
Publicado: 23/9/2021 -
How To Lead In Data Science
Publicado: 16/9/2021 -
Why interest in graph databases and graph analytics are growing
Publicado: 9/9/2021 -
The State of Data Journalism
Publicado: 2/9/2021 -
Auditing machine learning models for discrimination, bias, and other risks
Publicado: 26/8/2021 -
An oscilloscope for deep learning
Publicado: 19/8/2021 -
What’s new in data engineering
Publicado: 12/8/2021 -
The evolution of the data science role and of data science tools
Publicado: 5/8/2021 -
Data Augmentation in Natural Language Processing
Publicado: 29/7/2021 -
Storage Technologies for a Multi-cloud World
Publicado: 22/7/2021 -
Building a next-generation dataflow orchestration and automation system
Publicado: 15/7/2021 -
Building a flexible, intuitive, and fast forecasting library
Publicado: 8/7/2021 -
Neural Models for Tabular Data
Publicado: 1/7/2021 -
Training and Sharing Large Language Models
Publicado: 24/6/2021 -
Questioning the Efficacy of Neural Recommendation Systems
Publicado: 17/6/2021 -
Automation in Data Management and Data Labeling
Publicado: 10/6/2021 -
Reinforcement Learning For the Win
Publicado: 3/6/2021 -
How Companies Are Investing in AI Risk and Liability Minimization
Publicado: 27/5/2021
A series of informal conversations with thought leaders, researchers, practitioners, and writers on a wide range of topics in technology, science, and of course big data, data science, artificial intelligence, and related applications. Anchored by Ben Lorica (@BigData), the Data Exchange also features a roundup of the most important stories from the worlds of data, machine learning and AI. Detailed show notes for each episode can be found on https://thedataexchange.media/ The Data Exchange podcast is a production of Gradient Flow [https://gradientflow.com/].