Rendering is a complex process. Its differentiation cannot be uniquely defined; thus, a straightforward integration into neural networks is impossible.

Differentiable rendering (DR) constitutes a family of techniques that tackle such integration for end-to-end optimization by obtaining useful gradients of the rendering process.

Nvidia and Aalto University introduce a modular primitive to provide high-performance primitive operations for rasterization-based differentiable rendering. The proposed modular primitive uses highly optimized hardware graphics pipelines to deliver better performance than previous differentiable rendering systems.

In this deeplizard episode, learn how to prepare and process our own custom data set of sign language digits, which will be used to train our fine-tuned MobileNet model in a future episode.


  • 00:00 Welcome to DEEPLIZARD – Go to for learning resources
  • 00:40 Obtain the Data
  • 01:30 Organize the Data
  • 09:42 Process the Data
  • 13:11 Collective Intelligence and the DEEPLIZARD HIVEMIND