The Data Exchange with Ben Lorica
A podcast by Ben Lorica - Thursdays
Categories:
251 Episodes
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End-to-end deep learning models for speech applications
Published: 26/11/2020 -
Securing machine learning applications
Published: 19/11/2020 -
Testing Natural Language Models
Published: 12/11/2020 -
Detecting Fake News
Published: 5/11/2020 -
The Computational Limits of Deep Learning
Published: 29/10/2020 -
Making deep learning accessible
Published: 22/10/2020 -
Building and deploying knowledge graphs
Published: 15/10/2020 -
Financial Time Series Forecasting with Deep Learning
Published: 8/10/2020 -
A programming language for scientific machine learning and differentiable programming
Published: 1/10/2020 -
Using machine learning to modernize medical triage and monitoring systems
Published: 24/09/2020 -
Connecting Reinforcement Learning to Simulation Software
Published: 17/09/2020 -
Using machine learning to detect shifts in government policy
Published: 10/09/2020 -
What is AI Assurance?
Published: 3/09/2020 -
Best practices for building conversational AI applications
Published: 27/08/2020 -
Tools for scaling machine learning
Published: 20/08/2020 -
From Python beginner to seasoned software engineer
Published: 13/08/2020 -
Assessing Models and Simulations of Epidemic Infectious Diseases
Published: 6/08/2020 -
Improving the hiring pipeline for software engineers
Published: 30/07/2020 -
How to build state-of-the-art chatbots
Published: 23/07/2020 -
Democratizing machine learning
Published: 16/07/2020
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/].