In this episode of our TrainAI series, I sit down with Anima Anandkumar, Bren Professor at Caltech and Principal Scientist with Amazon Web Services.
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Anima joined me to discuss the research coming out of her “Tensorlab” at CalTech. In our conversation, we review the application of tensor operations to machine learning and discuss how an example problem–document categorization–might be approached using 3 dimensional tensors to discover topics and relationships between topics. We touch on multidimensionality, expectation maximization, and Amazon products Sagemaker and Comprehend. Anima also goes into how to tensorize neural networks and apply our understanding of tensor algebra to do perform better architecture searches.
Happy Birthday 2018!!
A few weeks ago, at the TWIML AI Summit, I spoke with a listener who shared some interesting ways that his business, a world-leading energy company, has directly benefited from what he’s learned on the podcast. It’s been incredibly exciting to hear stories like this from listeners. To celebrate our second anniversary, We’d really like to hear from you about ways that the podcast has helped you at work or in school or transitioning between the two, how it’s helped you find or connect to resources you’ve found valuable, or educated you about something new.
You can submit your written comments to twimlai.com/2av, or call us at (636) 735-3658, and leave a voicemail (leave us your name/email, we’ll edit it out!) with your story!
Interested in the Fast.AI Deep Learning course?
I’ll be working through the Fast AI Practical Deep Learning for Coders course starting in June and in turn, I’m organizing a study and support group via the Meetup. This is a great course and Fast.ai co-founder Jeremy Howard encouraged our group on twitter noting that groups that take the course together have a higher success rate, so let’s do this!
Three simple steps to join:
1. Sign up for the Meetup, noting fast.ai in the “What you hope to learn” box
2. Using the email invitation you’ll receive to join our Slack group, and
3. Once you’re there joining the #fast_ai channel.
Thanks to our sponsor!
I’d like to send a shoutout to our friends over at Figure Eight for their continued support of the show, and their sponsorship of this week’s series which all took place at Train AI. Figure Eight is the essential Human-in-the-Loop AI platform for data science and machine learning teams. The Figure Eight software platform trains, tests, and tunes machine learning models to make AI work in the real world. Learn more at www.figure-eight.com.
Mentioned in the Interview
- Flatland: A Romance of Many Dimensions
- Amazon Sagemaker
- Amazon Comprehend
- Paper: Tensor Decompositions for Learning Latent Variable Models
- Latent Dirichlet Allocation (LDA)
- TrainAI 2018 Series Page
- Join us in celebrating our 2nd Birthday!
- TWIML Presents: Series page
- TWIML Events Page
- TWIML Meetup
- TWIML Newsletter
“More On That Later” by Lee Rosevere licensed under CC By 4.0