To build an autonomous car using Python, you can use deep learning, Computer Vision and machine learning techniques
About This Video
- The transition from beginner to deep learning expert
- As your instructor completes each task, you will learn through demonstrations
- No experience necessary
Self-driving cars are emerging as one of the most transformative technologies. Deep learning algorithms are driving their rapid development and creating new opportunities for the mobility sector. The highest salaries in the world of development are for deep learning jobs. This course is unique in that it makes practical use of deep learning and applies it towards building a self-driving vehicle. This exciting and fun course will teach you how to master deep learning with Rayan Slim, a top instructor. Rayan Slim is an experienced instructor with a high rating who has trained thousands of students. He follows a learning by doing approach. You will be able to build a fully functioning self-driving car using deep learning by the end of this course. This simulation will impress even senior developers and give you hands-on skills in neural network design that you can use for any project or company.
This course will teach you how to do the following.
- OpenCV uses Computer Vision techniques to identify lane lines in a self-driving vehicle.
- To classify between binary classes, train a neural network based on perceptron.
- Convolutional neural networks can be trained to recognize various traffic signs
- To fit complex datasets, train deep neural networks
- Master Keras is a Python-based library for power neural networks.
- Train and build a fully functional self driving car
Downloading the example code for this course: You can download the example code files for this course on GitHub at the following link: https://github.com/PacktPublishing/The-Complete-Self-Driving-Car-Course---Applied-Deep-Learning. If you require support please email: customercare@packt.com
