Learn to build a neutral network that can drive using PyTorch in Python
Have you ever wondered how self-driving cars navigate the roads effortlessly? Imagine sitting in a robo-taxi and witnessing the magic of artificial intelligence at work. Recently, I had the opportunity to experience this firsthand in San Francisco.
As the autonomous vehicle smoothly maneuvered through the streets, I was intrigued by the technology behind it. As a Data Scientist, I was determined to understand how neural networks can be used to predict driving behavior. Let me take you on a journey through building a simple neural network that can pave the way for autonomous vehicles.
To begin, let’s delve into the mechanics of how software and hardware components seamlessly integrate to create a self-driving vehicle. A car operates on a horizontal plane and can move in four directions. Hence, it is equipped with sensors to detect proximity to objects in front, behind, and on the sides.
Imagine the freedom of travel without worrying about the skill or mood of a human driver. This is the future that awaits us, driven by cutting-edge technology. Are you ready to embark on this journey to build a neural network that can revolutionize the way we commute?