Learn how to apply machine learning to robotic applications through this course developed in collaboration with the Interactive Robotics Lab at Arizona State University. Beginning with understanding simple neural networks to exploring long short-term memory (LSTM) and reinforcement learning, these modules provide the foundations for using deep learning algorithms in many robotics workloads.
This course provides you with practical knowledge of the following skills:
Apply supervised learning for obstacle detection
Derive backpropagation and use dropout and normalization to train your model
Use reinforcement learning to let a robot learn from simulations
Build many types of deep learning systems using PyTorch*
The course is structured around four weeks of lectures and exercises. Each week requires three hours to complete.
Get a beginner’s overview of supervised learning for robotics applications. Topics include:
Use backpropagation to train a simple neural network and identify overfitting
Build a neural network using PyTorch for an obstacle avoidance system