The Data Exchange with Ben Lorica

The Data Exchange with Ben Lorica

Por: Ben Lorica
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/].
326 episodios disponibles

Latest episodes of the podcast The Data Exchange with Ben Lorica

Mostrando página 15 de 17

Making deep learning accessible 22/10/2020
Building and deploying knowledge graphs 15/10/2020
Financial Time Series Forecasting with Deep Learning 08/10/2020
A programming language for scientific machine learning and differentiable programming 01/10/2020
Using machine learning to modernize medical triage and monitoring systems 24/09/2020
Connecting Reinforcement Learning to Simulation Software 17/09/2020
Using machine learning to detect shifts in government policy 10/09/2020
What is AI Assurance? 03/09/2020
Best practices for building conversational AI applications 27/08/2020
Tools for scaling machine learning 20/08/2020
From Python beginner to seasoned software engineer 13/08/2020
Assessing Models and Simulations of Epidemic Infectious Diseases 06/08/2020
Improving the hiring pipeline for software engineers 30/07/2020
How to build state-of-the-art chatbots 23/07/2020
Democratizing machine learning 16/07/2020
How graph technologies are being used to solve complex business problems 09/07/2020
Machines for unlocking the deluge of COVID-19 papers, articles, and conversations 02/07/2020
Designing machine learning models for both consumer and industrial applications 25/06/2020
Building open source developer tools for language applications 18/06/2020
Viewing machine learning and data science applications as sociotechnical systems 11/06/2020
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