Chapter
Training Neural Nets for an Autopilot
1:15:02 - 1:20:36 (05:34)
The success of a neural net depends on the quality of the data sets and the architecture of the net. For an autopilot system, the data sets include object detection, lane line markings and traffic lights.
Clips
In this podcast episode, the speakers discuss the process of formulating a task for a multi-headed neural network, such as the example of an autopilot using convolutional neural nets.
1:15:02 - 1:16:37 (01:35)
Summary
In this podcast episode, the speakers discuss the process of formulating a task for a multi-headed neural network, such as the example of an autopilot using convolutional neural nets.
ChapterTraining Neural Nets for an Autopilot
Episode#333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI
PodcastLex Fridman Podcast
The development of neural nets for autonomous vehicles, using eight camera images simultaneously in the prediction process, and examples of a tiny neural net that can predict if there is a traffic light or lane line marking.
1:16:37 - 1:18:40 (02:02)
Summary
The development of neural nets for autonomous vehicles, using eight camera images simultaneously in the prediction process, and examples of a tiny neural net that can predict if there is a traffic light or lane line marking.
ChapterTraining Neural Nets for an Autopilot
Episode#333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI
PodcastLex Fridman Podcast
The process of predicting in 3D requires diverse and accurate datasets, which can be achieved through offline tracking and automatic reconstruction.
1:18:40 - 1:20:36 (01:55)
Summary
The process of predicting in 3D requires diverse and accurate datasets, which can be achieved through offline tracking and automatic reconstruction.