Deep Learning Essentials

BECM33DPL

The course teaches deep learning methods on known robotic problems, such as semantic segmentation or reactive motion control. The overall goal is timeless, universal knowledge rather than listing all known deep learning architectures. Students are assumed to have working prior knowledge of mathematics (gradient, jacobian, hessian, gradient descent, Taylor polynomial) and machine learning (Bayes risk minimization, linear classifier). The labs are divided into two parts; in the first one, the students will solve elementary deep ML tasks from scratch (including the reimplementation of autograd backpropagation), and in the second one, students will build on existing templates in order to solve complex tasks including RL, vision transformers and generative networks

Details

EXPLORE THE BLOCKS AND COURSES

STILL SOME QUESTIONS?

Contact us at minor@prg.ai and we will get back to you shortly.

Also, don’t forget to follow us on LinkedIn.