Today, we are announcing the v1 launch of the Nuron.World API. NuronLabs was founded with the goal of enabling truly safe & reliable real-world neural networks in production applications and this milestone brings us one step closer to achieving that goal. A well designed API is essential because it allows…

Deep learning research done wrong can be extremely expensive in terms of computation and time. In addition, it could also hurt the environment. A poorly structured deep learning project might involve:

  1. Write some code.
  2. Start training.
  3. Wait for something to fail.
  4. Fix and repeat.

This process slows down progress and…

Have you wondered why self-driving car projections keep getting pushed back from 2018 to 2019 to ‘way in the future’ to ‘may never happen’? While self-driving cars prototypes today make for good demos, they are not safe enough to deploy in the real world without substantial human intervention.

One of…

Although the Garter hype cycle believes that we’ve reached peak Deep Learning in 2019, I believe Deep Learning is in its early stages and there are numerous extremely valuable opportunities that lie ahead. Here are some of the opportunities I see for the next decade.

1. Real-world deep learning

The key shift that will…

Deep learning has seen a rapid increase in popularity over the last 5 years.

Given that its a field in the rapid stages of its development, it has brought about an influx of researchers and institutes. …

A restaurant analogy

This article is the start of a series that will provide some background for the problems NuronLabs provides solutions to.

Precision-Recall is an important metric that measures performance on a few tough visual perception problems such as Object Detection, and Instance/Semantic Segmentation. …


Building a neural layer for reliable real-world deep learning

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