More academies to come


My mission for Zero to Singularity is to make Deep Learning accessible, understandable and applicable for everyone.


The strategy for 2020 is to create task specific academies that help people understand the details and impact of a specific part of deep learning, and learn how to apply it during an educational workshop.


Below are some of the current ideas:

Deep Dives

I want to create one/two day trainings for a specific topic within Deep Learning. The first of these trainings is the “Deep Learning Day - Vision” which teaches you how to train and deploy world-class networks. A second day would be beneficial to go through the nitty gritty detail.

I am considering creating similar academies for the following applications of deep learning:

  • Computer vision (day 1 already available)
  • Object Detection
  • Natural Language Processing
  • Structured Data
  • Mobile
  • Edge
  • Recommendation Engine

Advanced topics

Reinforcement Learning and Generative Adversarial networks are two of the most fascinating subjects to date.

Reinforcement Learning

RL teaches computers to understand the world around them and learns to act upon that understanding, such as playing computer games (Atari, Go, …) or power autonomous cars (ongoing research).

Generative Adverserial Networks

GANs are currently one of the most exciting ideas in Deep Learning, where two neural networks combat each other to learn how to create realistic samples, some examples:

I am considering creating a two day course for each, so people understand what they are, and how they can start to apply them.

AI Cloud providers

More and more cloud providers and startups are offering tooling to train and deploy AI solutions in the cloud. I am considering creating cloud specific trainings that discusses:

  • Hardware
  • Software
  • Services
  • Applications

For the most well-known cloud providers.


At the moment these are ideas I am playing with, but would love to make them reality.

If you have some ideas, interest or general feedback, I would love to hear from you: