7 Tips for Dealing With Small Data

7 Tips for Dealing With Small Data

How can we prototype and validate machine learning ideas without the most essential raw material? How can we efficiently obtain and create value with data when resources are sparse?

At my workplace, we produce a lot of functional prototypes for our clients. Because of this, I often need to make Small Data go a long way. In this article, I’ll share 7 tips to improve your results when prototyping with small datasets.

Is your algorithm confident enough? How to measure uncertainty in neural networks

When machine learning techniques are used in “mission critical” applications, the acceptable margin of error becomes significantly lower.

Imagine that your model is driving a car, assisting a doctor or even just interacting directly with an (perhaps easily annoyed) end user. In these cases, you’ll want to ensure that you can be confident in the predictions your model makes before acting on them.