Using inductive bias as a guide for effective machine learning prototyping

What makes working on new machine learning (ML) use cases so exciting, and at times so frustrating, is ML’s lack of hard and fast rules. A few aspects of the model development process can be codified; for example, data should always be separated into strictly disjoint training and test sets to ensure that model performance isn’t attributable to overfitting. But…

--

--

--

Thoughts from the Engineering team at Flatiron Health. We're building technology that enables cancer researchers and care providers to learn from the experience of every patient.

Recommended from Medium

Language Model as Few-Shot Learner for Task-Oriented Dialogue Systems

A Useful Method for Shadow Detection

MLOps : The technology for tomorrow

When does a problem need a Machine Learning solution?

Torch — Logistic Regression on Iris dataset

What is K-Nearest Neighbor(KNN) ?

RTX 2080Ti Vs GTX 1080Ti: FastAI Mixed Precision training & comparisons on CIFAR-100

Optimizing Feature generation

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Alexander Rich

Alexander Rich

Data Insights Engineer at Flatiron Health and cognitive psychology PhD

More from Medium

Dithering in Recommendation Systems

The Linked List Data Structure

AEP: Data Lake vs. Profile

164. Maximum Gap — Explanation