The Data Exchange with Ben Lorica
Podcast autorstwa Ben Lorica - Czwartki
281 Odcinki
-
Why You Need a Modern Metadata Platform
Opublikowany: 11.11.2021 -
Making Large Language Models Smarter
Opublikowany: 4.11.2021 -
AI Begins With Data Quality
Opublikowany: 28.10.2021 -
Modernizing Data Integration
Opublikowany: 21.10.2021 -
Deploying Machine Learning Models Safely and Systematically
Opublikowany: 14.10.2021 -
Large-scale machine learning and AI on multi-modal data
Opublikowany: 7.10.2021 -
Machine Learning in Astronomy and Physics
Opublikowany: 30.09.2021 -
The Unreasonable Effectiveness of Multiple Dispatch
Opublikowany: 23.09.2021 -
How To Lead In Data Science
Opublikowany: 16.09.2021 -
Why interest in graph databases and graph analytics are growing
Opublikowany: 9.09.2021 -
The State of Data Journalism
Opublikowany: 2.09.2021 -
Auditing machine learning models for discrimination, bias, and other risks
Opublikowany: 26.08.2021 -
An oscilloscope for deep learning
Opublikowany: 19.08.2021 -
What’s new in data engineering
Opublikowany: 12.08.2021 -
The evolution of the data science role and of data science tools
Opublikowany: 5.08.2021 -
Data Augmentation in Natural Language Processing
Opublikowany: 29.07.2021 -
Storage Technologies for a Multi-cloud World
Opublikowany: 22.07.2021 -
Building a next-generation dataflow orchestration and automation system
Opublikowany: 15.07.2021 -
Building a flexible, intuitive, and fast forecasting library
Opublikowany: 8.07.2021 -
Neural Models for Tabular Data
Opublikowany: 1.07.2021
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/].