The Data Exchange with Ben Lorica
A podcast by Ben Lorica - Thursdays
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Categories:
270 Episodes
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Securing machine learning applications
Published: 11/19/2020 -
Testing Natural Language Models
Published: 11/12/2020 -
Detecting Fake News
Published: 11/5/2020 -
The Computational Limits of Deep Learning
Published: 10/29/2020 -
Making deep learning accessible
Published: 10/22/2020 -
Building and deploying knowledge graphs
Published: 10/15/2020 -
Financial Time Series Forecasting with Deep Learning
Published: 10/8/2020 -
A programming language for scientific machine learning and differentiable programming
Published: 10/1/2020 -
Using machine learning to modernize medical triage and monitoring systems
Published: 9/24/2020 -
Connecting Reinforcement Learning to Simulation Software
Published: 9/17/2020 -
Using machine learning to detect shifts in government policy
Published: 9/10/2020 -
What is AI Assurance?
Published: 9/3/2020 -
Best practices for building conversational AI applications
Published: 8/27/2020 -
Tools for scaling machine learning
Published: 8/20/2020 -
From Python beginner to seasoned software engineer
Published: 8/13/2020 -
Assessing Models and Simulations of Epidemic Infectious Diseases
Published: 8/6/2020 -
Improving the hiring pipeline for software engineers
Published: 7/30/2020 -
How to build state-of-the-art chatbots
Published: 7/23/2020 -
Democratizing machine learning
Published: 7/16/2020 -
How graph technologies are being used to solve complex business problems
Published: 7/9/2020
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/].