Tag: Machine Learning

Highlights from the Maryland Data Science Conference: Deep Learning on Imagery and Text

Niels Kasch, cofounder of Miner & Kasch, an AI and Data Science consulting firm, provides insight from a deep learning session that occurred at the Maryland...

Themes and Conferences per Pacoid, Episode 7

Paco Nathan covers recent research on data infrastructure as well as adoption of machine learning and AI in the enterprise. Introduction Welcome back to our monthly...

On Collaboration Between Data Science, Product, and Engineering Teams

Eugene Mandel, Head of Product at Superconductive Health, recently dropped by Domino HQ to candidly discuss cross-team collaboration within data science. Mandel’s previous leadership roles within...

Machine Learning Projects: Challenges and Best Practices

Lukas Biewald is the founder of Weights & Biases. He was previously the founder of Figure Eight (formerly CrowdFlower). This blog post provides insights into why...

Model Interpretability with TCAV (Testing with Concept Activation Vectors)

This Domino Data Science Field Note provides very distilled insights and excerpts from Been Kim’s recent MLConf 2018 talk and research about Testing with Concept Activation Vectors...

SHAP and LIME Python Libraries: Part 2 – Using SHAP and LIME

This blog post provides insights on how to use the SHAP and LIME Python libraries in practice and how to interpret their output, helping readers prepare...

Themes and Conferences per Pacoid, Episode 4

Paco Nathan's latest column covers themes that include data privacy, machine ethics, and yes, Don Quixote. Introduction Welcome back to our monthly series about data science....

SHAP and LIME Python Libraries: Part 1 – Great Explainers, with Pros and Cons to Both

This blog post provides a brief technical introduction to the SHAP and LIME Python libraries, followed by code and output to highlight a few pros and...

Themes and Conferences per Pacoid, Episode 3

Paco Nathan‘s column covers themes that include open source, "intelligence is a team sport", and "implications of massive latent hardware". Introduction Welcome to our monthly series...

Justified Algorithmic Forgiveness?

Last week, Paco Nathan referenced Julia Angwin’s recent Strata keynote that covered algorithmic bias. This Domino Data Science Field Note dives a bit deeper into some...

Themes and Conferences per Pacoid, Episode 2

Paco Nathan's column covers themes of data science for accountability, reinforcement learning challenges assumptions, as well as surprises within AI and Economics. Introduction Welcome back to...

Trust in LIME: Yes, No, Maybe So? 

In this Domino Data Science Field Note, we briefly discuss an algorithm and framework for generating explanations, LIME (Local Interpretable Model-Agnostic Explanations), that may help data...

Benchmarking NVIDIA CUDA 9 and Amazon EC2 P3 Instances Using Fashion MNIST

In this post, Josh Poduska, Chief Data Scientist at Domino Data Lab, writes about benchmarking NVIDIA CUDA 9 and Amazon EC2 P3 Instances Using Fashion MNIST....

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