Book Deep Learning Experimentation Measurement 
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. When a measure becomes a target, it ceases to be a good measure ( Goodhart’s Law ).
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.
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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.
Domino Data Lab
MAY 8, 2019
Visualizations are vital in data science work, with the caveat that the information that they convey may be 4-5 layers of abstraction away from the actual business process being measured. measure the subjects’ ability to trust the models’ results. Information can get quite distorted after being abstracted that many times.
Occam's Razor
OCTOBER 12, 2017
The tiny downside of this is that our parents likely never had to invest as much in constant education, experimentation and self-driven investment in core skills. Ask them what they worry about, ask them what they are solving for, ask them how they measure success, ask them what are two things on the horizon that they are excited about.
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