Jeff Hinton dismissed gradient descent entirely before winning the Nobel Prize for it. His fear was local minima - valleys in the loss landscape where the model gets stuck forever. For a 1.2 billion parameter LLM like Llama, the landscape has more dimensions than atoms in the

Name: Jeff Hinton dismissed gradient descent entirely before winning the Nobel Prize for it. His fear was local minima - valleys in the loss landscape where the model gets stuck forever. For a 1.2 billion parameter LLM like Llama, the landscape has more dimensions than atoms in the
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Description: Jeff Hinton dismissed gradient descent entirely before winning the Nobel Prize for it. His fear was local minima - valleys in the loss landscape where the model gets stuck forever. For a 1.2 billion parameter LLM like Llama, the landscape has more dimensions than atoms in the

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Jeff Hinton dismissed gradient descent entirely before winning the Nobel Prize for it. His fear was local minima - valleys in the loss landscape where the model gets stuck forever. For a 1.2 billion parameter LLM like Llama, the landscape has more dimensions than atoms in the

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