Intuition Machine - Issue #43

We’ve now been running computer vision models in production at EyeEm for more than three years - on literally billions of images. As an engineer involved in building the infrastructure behind it from scratch, I both enjoyed and suffered the many technical challenges this task raised. This journey has also taught me a lot about managing processes and relationships with different teams, tasks of an especially challenging nature in a dynamic startup environment.

We believe in fiction as an important tool for imagining future relationships to technology. 

Same stats, different graphs: generating datasets with varied appearance and identical statistics through simulated annealing.

This is a pre-release product. Purchasers are entitled to the final version. The table of contents may be re-organized several times prior to release. I challenge you to find a field as interesting and exciting as Deep Learning. 

Prerequisites: This article assumes you are familiar with the basics of Python, TensorFlow, and Jupyter notebooks. 

Survival of the fittest.

I got the chance to read this paper on Distributional Bellman published by DeepMind in July. 

An engineer who’s learning about using convolutional neural networks for image classification just asked me an interesting question; how does a model know how to recognize objects in different positions in an image?