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Course Outline
Introduction
Setting up the Development Environment
Creating a Project
Configuring the Simulator
Preparing the Data Sets
Overview of Python Deep Learning Libraries
Applying Computer Vision Techniques to Track Lanes
Training Perceptron-Based Neural Networks to Detect Other Vehicles
Implementing Convolutional Neural Networks to Predict Steering Angle and Speed
Training a Deep Learning Model to Classify Traffic Signs
Using Polynomial Regression to Improve Predictive Accuracy
Testing the Self Driving Car
Troubleshooting
Summary and Conclusion
Requirements
- Python programming experience.
Audience
- Developers
Testimonials
Clarity
WesCEF
Having some previous computer vision experience I found the second day covering feature extraction and CNNs most beneficial.
WesCEF
The second day going through feature extraction was great fun. trainer was very knowledgeable and engaging.
WesCEF
Apart from the content, I loved Abhi's flexibility to tweak the training based on our feedback
WesCEF
Trainer was very knowlegable and very open to feedback on what pace to go through the content and the topics we covered. I gained alot from the training and feel like I now have a good grasp of image manipulation and some techniques for building a good training set for an image classification problem.
Anthea King - WesCEF
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