Chapter
The Power of Recurrent Neural Networks
13:24 - 22:25 (09:01)
Recurrent neural networks (RNNs) have been surpassed by transformers, but they still have incredible potential. The recurrence in RNNs may be able to capture the timing phenomena important for neuronal firing in the brain.
Clips
The recurrence in recurrent neural networks may capture the same phenomena as the timing that's important for neuron firings in the brain, and it's possible to build large knowledge bases within neural networks in the future.
13:24 - 16:52 (03:28)
Summary
The recurrence in recurrent neural networks may capture the same phenomena as the timing that's important for neuron firings in the brain, and it's possible to build large knowledge bases within neural networks in the future.
ChapterThe Power of Recurrent Neural Networks
Episode#94 – Ilya Sutskever: Deep Learning
PodcastLex Fridman Podcast
Deep learning was initially underestimated but it has proven to be successful when the right mixture of data and computation is applied.
16:52 - 20:45 (03:52)
Summary
Deep learning was initially underestimated but it has proven to be successful when the right mixture of data and computation is applied. The speaker discusses the differences between vision, language, and reinforcement learning.
ChapterThe Power of Recurrent Neural Networks
Episode#94 – Ilya Sutskever: Deep Learning
PodcastLex Fridman Podcast
The advancements made in deep learning and vision have led to improvements in NLP and reinforcement learning applications, eliminating the need for different architectures for each individual problem domain.
20:45 - 22:25 (01:40)
Summary
The advancements made in deep learning and vision have led to improvements in NLP and reinforcement learning applications, eliminating the need for different architectures for each individual problem domain.