ROS Deep Learning with TensorFlow 101 Python
The first step with ROS, Deep Learning, Tensor Flow, and Image Recognition
DeepLearning… A topic that we hear a lot about and that promises a lot. Deep learning is the technology behind intelligent drone flight, self-driving cars, robots recognizing a huge number of objects, people tracking video feeds, etc. But how can it be used?
In this course, you will focus on learning the essentials for doing image recognition with Deep Learning. You won’t learn each and every nook and cranny of deep learning, but the few elements you learn you will be able to put into use and apply in real life, integrated with ROS.
- Use the Google TensorFlow Image Recognition DB to recognize hundreds of different objects with ROS
- Generate your own TensorFlow Inference graph to make it learn a custom object.
- Use TensorBoard Web visualizer to monitor how the learning process is going.
Simulation robots used in this course
Mira Robot, GarbageCollector Robot.
What projects will you be doing?
Integrate ROS + TensorFlow
Create your own ROS package that recognizes images with TensorFlow
Train your model
Train your own TensorFlow Image Recognition Model
Detect with your new retrained model in ROS
Garbage Collecting Robot Challenge
Image recognition/ learning with TensorFlow
What you will learn
Unit 1: Introduction
- TensorFlow Image: Introduction to the Course
- Practical demo
Unit 2: Use existing TensorFlow Model
TensorFlow Image Create your own ROS Package that recognizes images with TensorFlow.
1 hr. 30 min.
Unit 3: Inspect a TensorFlow Model
TensorFlow Image Launch TensorBoard and inspect a TensorFlow Model.
1 hr. 30 min.
Unit 4: Part1: Train your own TensorFlow
TensorFlow Image Train your own TensorFlow Image Recognition model Part1.
Unit 5: Part2: Use your trained TensorFlow
TensorFlow Image Train your own TensorFlow Image Recognition model Part2.
Unit 6: Garbage Collecting Robot
TensorFlow Image Garbage Collecting Robot.
Unit 7: Final Recommendations
TensorFlow Image FinalRecommendations.
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