What makes a good training dataset according to Andrej Karpathy?

Andrej Karpathy highlights the importance of having a good data engine to perfect the training sets for neural networks. According to him, the process involves continuously refining the training sets to increase their quality by monitoring how a deployed neural network performs and identifying scenarios where it struggles. These challenging scenarios, often rare, need to be captured, reconstructed accurately, and then added back into the training dataset to improve it further.

Karpathy emphasizes that a good training dataset needs to be large, diverse, and clean. The diversity should cover a wide range of scenarios to prepare the neural network for different, potential real-world situations. He also stresses the necessity of ensuring that the dataset is free from errors (clean) to prevent learning incorrect patterns. The key to this process is a strong execution by an engineering team that understands the underlying methods and can execute them capably 1.