Use deep learning, Computer Vision, and machine learning techniques to build an autonomous car with Python
Key Features
- The transition from a beginner to deep learning expert
- Learn through demonstrations as your instructor completes each task with you
- No experience required
Book Description
Self-driving cars have emerged to be one of the most transformative technologies. Fueled by deep learning algorithms, they are rapidly developing and creating new opportunities in the mobility sector. Deep learning jobs command some of the highest salaries in the development world. This is the first and one of the only courses that make practical use of deep learning and applies it to building a self-driving car. You'll learn and master deep learning in this fun and exciting course with top instructor Rayan Slim. Having trained thousands of students, Rayan is a highly rated and experienced instructor who follows a learning-by-doing approach. By the end of the course, you will have built a fully functional self-driving car powered entirely by deep learning. This powerful simulation will impress even the most senior developers and ensure you have hands-on skills in neural networks that you can bring to any project or company.
This course will show you how to do the following:
- Use Computer Vision techniques via OpenCV to identify lane lines for a self-driving car
- Train a perceptron-based neural network to classify between binary classes
- Train convolutional neural networks to identify various traffic signs
- Train deep neural networks to fit complex datasets
- Master Keras, a power neural network library written in Python
- Build and train a fully functional self-driving car
All the code and supporting files for this course are available at https://github.com/PacktPublishing/The-Complete-Self-Driving-Car-Course---Applied-Deep-Learning
What you will learn
- Apply Computer Vision and deep learning techniques to build automotive-related algorithms
- Understand, build, and train convolutional neural networks with Keras.
- Simulate a fully functional self-driving car with convolutional neural networks and Computer Vision
- Train a deep learning model that can identify up to 43 different traffic signs
- Use essential Computer Vision techniques to identify lane lines on a road
- Build and train powerful neural networks with Keras
- Understand neural networks at the most fundamental, perceptron-based level
Who this book is for
This course is for anyone with an interest in deep learning and Computer Vision. Anyone (no matter the skill level) who wants to transition into the field of artificial intelligence, including entrepreneurs with an interest in working on some of the most cutting-edge technologies, will find this course useful.
