Evaluating Model Explainability Methods with Sara Hooker - TWiML Talk #189
In this, the first episode of the Deep Learning Indaba series, we’re joined by Sara Hooker, AI Resident at Google Brain. I spoke with Sara in the run-up to the Indaba about her work on interpretability in deep neural networks. We discuss what interpretability means and nuances like the distinction between interpreting model decisions vs model function. We also talk about the relationship between Google Brain and the rest of the Google AI landscape and the significance of the Google AI Lab in Accra, Ghana.
10 Okt 20181h 3min
Graph Analytic Systems with Zachary Hanif - TWiML Talk #188
In this, the final episode of our Strata Data Conference series, we’re joined by Zachary Hanif, Director of Machine Learning at Capital One’s Center for Machine Learning. We start our discussion with a look at the role of graph analytics in the ML toolkit, including some important application areas for graph-based systems. Zach gives us an overview of the different ways to implement graph analytics, including what he calls graphical processing engines which excel at handling large datasets, & much m
8 Okt 201854min
Diversification in Recommender Systems with Ahsan Ashraf - TWiML Talk #187
In this episode of our Strata Data conference series, we’re joined by Ahsan Ashraf, data scientist at Pinterest. We discuss his presentation, “Diversification in recommender systems: Using topical variety to increase user satisfaction,” covering the experiments his team ran to explore the impact of diversification in user’s boards, the methodology his team used to incorporate variety into the Pinterest recommendation system and much more! The show notes can be found at https://twimlai.com/talk/18
4 Okt 201844min
The Fastai v1 Deep Learning Framework with Jeremy Howard - TWiML Talk #186
In today's episode we're presenting a special conversation with Jeremy Howard, founder and researcher at Fast.ai. This episode is being released today in conjunction with the company’s announcement of version 1.0 of their fastai library at the inaugural Pytorch Devcon in San Francisco. In our conversation, we dive into the new library, exploring why it’s important and what’s changed, the unique way in which it was developed, what it means for the future of the fast.ai courses, and much more!
2 Okt 20181h 11min
Federated ML for Edge Applications with Justin Norman - TWiML Talk #185
In this episode we’re joined by Justin Norman, Director of Research and Data Science Services at Cloudera Fast Forward Labs. In my chat with Justin we start with an update on the company before diving into a look at some of recent and upcoming research projects. Specifically, we discuss their recent report on Multi-Task Learning and their upcoming research into Federated Machine Learning for AI at the edge. For the complete show notes, visit https://twimlai.com/talk/185.
27 Sep 201847min
Exploring Dark Energy & Star Formation w/ ML with Viviana Acquaviva - TWiML Talk #184
In today’s episode of our Strata Data series, we’re joined by Viviana Acquaviva, Associate Professor at City Tech, the New York City College of Technology. In our conversation, we discuss an ongoing project she’s a part of called the “Hobby-Eberly Telescope Dark Energy eXperiment,” her motivation for undertaking this project, how she gets her data, the models she uses, and how she evaluates their performance. The complete show notes can be found at https://twimlai.com/talk/184.
26 Sep 201840min
Document Vectors in the Wild with James Dreiss - TWiML Talk #183
In this episode of our Strata Data series we’re joined by James Dreiss, Senior Data Scientist at international news syndicate Reuters. James and I sat down to discuss his talk from the conference “Document vectors in the wild, building a content recommendation system,” in which he details how Reuters implemented document vectors to recommend content to users of their new “infinite scroll” page layout.
24 Sep 201840min
Applied Machine Learning for Publishers with Naveed Ahmad - TWiML Talk #182
In today’s episode we’re joined by Naveed Ahmad, Senior Director of data engineering and machine learning at Hearst Newspapers. In our conversation, we discuss into the role of ML at Hearst, including their motivations for implementing it and some of their early projects, the challenges of data acquisition within a large organization, and the benefits they enjoy from using Google’s BigQuery as their data warehouse. For the complete show notes for this episode, visit https://twimlai.com/talk/182.
20 Sep 201839min
