Data Science and Beyond

DECEMBER 23, 2018

In my opinion it’s more exciting and relevant to everyday life than more hyped data science areas like deep learning. However, I’ve found it hard to apply what I’ve learned about causal inference to my work. Hence, the book is full of practical examples.

Deep Learning 103

Deep Learning Experimentation Data-driven Data Science 103

O'Reilly on Data

JULY 28, 2020

Without clarity in metrics, it’s impossible to do meaningful experimentation. Ethics and Data Science is a short book that helps developers think through data problems, and includes a checklist that team members should revisit throughout the process. Ongoing monitoring of critical metrics is yet another form of experimentation.

Marketing 362

Marketing Experimentation Metrics Testing 362

CIO Business Intelligence

JUNE 12, 2023

There are sales calls and workshops, and some book meetings right into the calendar. Of course, he says, it’s interesting to try something experimental, but investing requires greater commitment to the business case. But maybe the next step for salespeople will be to learn it too.” It’s lots of phone calls,” says Svensson. “I

Sales 52

Sales Machine Learning Experimentation Deep Learning 52

Domino Data Lab

AUGUST 22, 2019

Many thanks to Addison-Wesley Professional for providing the permissions to excerpt “Natural Language Processing” from the book, Deep Learning Illustrated by Krohn , Beyleveld , and Bassens. The excerpt covers how to create word vectors and utilize them as an input into a deep learning model.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

O'Reilly on Data

OCTOBER 19, 2021

ML apps need to be developed through cycles of experimentation: due to the constant exposure to data, we don’t learn the behavior of ML apps through logical reasoning but through empirical observation. Not only is data larger, but models—deep learning models in particular—are much larger than before.

IT 346

IT Testing Experimentation Software 346

Smart Data Collective

DECEMBER 24, 2020

Students who want to throw their books away may want to hold off because machine learning cannot provide a high grade every single time. Machine learning remains highly experimental and much of its functionality remains in the development stages. This makes it incapable of producing something from nothing. Conclusion.

Machine Learning 96

Machine Learning Experimentation Software Technology 96

Domino Data Lab

MAY 8, 2019

For example, in the case of more recent deep learning work, a complete explanation might be possible: it might also entail an incomprehensible number of parameters. They also require advanced skills in statistics, experimental design, causal inference, and so on – more than most data science teams will have.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105