O'Reilly on Data

JULY 28, 2020

This tradeoff between impact and development difficulty is particularly relevant for products based on deep learning: breakthroughs often lead to unique, defensible, and highly lucrative products, but investing in products with a high chance of failure is an obvious risk. arbitrary stemming, stop word removal.). Conclusion.

Marketing 362

Marketing Experimentation Metrics Testing 362

O'Reilly on Data

MARCH 31, 2020

Pragmatically, machine learning is the part of AI that “works”: algorithms and techniques that you can implement now in real products. We won’t go into the mathematics or engineering of modern machine learning here. After training, the system can make predictions (or deliver other results) based on data it hasn’t seen before.

Management 332

Management Machine Learning Experimentation Metrics 332

O'Reilly on Data

MARCH 18, 2020

Supervised learning is the most popular ML technique among mature AI adopters, while deep learning is the most popular technique among organizations that are still evaluating AI. Supervised learning is dominant, deep learning continues to rise. AI tools organizations are using.

Enterprise 311

Enterprise Deep Learning Data Governance Risk 311

Domino Data Lab

JULY 22, 2019

He was saying this doesn’t belong just in statistics. He also really informed a lot of the early thinking about data visualization. It involved a lot of interesting work on something new that was data management. To some extent, academia still struggles a lot with how to stick data science into some sort of discipline.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109