Chapter
Benchmarking Deep Learning Algorithms with Limited Training Data
1:00:57 - 1:06:06 (05:08)
This podcast episode discusses benchmarking deep learning algorithms with limited training data, including the inverted MNIST problem and the errors made by the network, which are similar to those made by humans in a similar scenario.
Clips
The speaker discusses the relevance of benchmark tests for deep learning algorithms, including the unique challenges of the capture problem in computer vision and the similarities in errors made by deep learning networks and humans in similar situations.
1:00:57 - 1:04:57 (03:59)
Deep Learning
Summary
The speaker discusses the relevance of benchmark tests for deep learning algorithms, including the unique challenges of the capture problem in computer vision and the similarities in errors made by deep learning networks and humans in similar situations.
ChapterBenchmarking Deep Learning Algorithms with Limited Training Data
Episode#115 – Dileep George: Brain-Inspired AI
PodcastLex Fridman Podcast
The MNIST dataset is an interesting space for studying the fundamentals of learning algorithms.
1:04:57 - 1:06:06 (01:09)
Machine Learning
Summary
The MNIST dataset is an interesting space for studying the fundamentals of learning algorithms. The dataset requires tens or hundreds of examples to reach 95% accuracy, and it is still highly relevant in the pursuit of AGI.