In Deep Learning, Is There a Separation Between Research and Research?

In this episode of The Data Exchange, I speak with Paco Nathan, author, teacher, and founder of Derwen.ai, a boutique consulting firm specializing in Data, ML, and AI. Paco consults with companies and speaks before audiences all over the world, and I plan to have him as a frequent guest on this podcast to draw on his observations of diverse organizations.

This particular conversation spanned many topics, including:

• Data Governance: Paco’s talk on the topic
• AutoML: Paco’s talk on the topic
• PyTorch and TensorFlow: posts we discussed - [1], [2]
• Reproducibility and feature selection
• The Streamlit open source project for ML app development, and Grus Law (“if you can think up something crazy and/or dangerous to do with notebooks, people are doing it.”)

I want this to be more than just a podcast. I want to create a community to help people make better decisions. A key part of this is getting you involved. I have ideas on how this community will grow, but as a first step, I want to ask a question related to one of the topics that Paco and I discussed: PyTorch and TensorFlow. I'd love to have you weigh in by filling out the survey form. I'll report on results and key insights in a future episode of this podcast.

[full show notes can be found on the Data Exchange site.]