Deep Learning with Domain Randomization
Learn how to train any robot to recognize an object and pinpoint its 3D location with only an RGB camera and a lot of training with Keras.
Welcome to this micro course! This course is intended for the people that want to learn about deep learning using Keras.
In this case, we use a very interesting approach to learning which is Environment Randomization This method exploits the versatility of environment generation in simulations to train a robot in a way that the resulting model is very robust, no matter the lighting conditions. It also makes the transition from simulated learning to reality much smoother and fast. Learn through hands-on experience how to train a robot for 3D object recognition using random environments.
Keras will be the cornerstone of this system and you will learn all the necessary skills to generate training data, convert it to a database, train a MobileNetV2 model, retrain it and make predictions with it.
The final project is the training of a garbage picking robot, from training data generation to the final garbage detection and picking program. Dive into the fantastic world of DeepLearning with Keras right now!
What You Will Learn
- How to use Keras in a basic way
- How to train a deep neural network using a Gazebo Simulation
- How to work with ROS+Gazebo+Keras in tandem.
- How the Random Environment generation works in Gazebo.
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