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Jeremy Howard

1 appearance ·8 ideas explored

Across 1 conversation, Jeremy Howard ranges across VC-backed startups, transfer learning, AI in medicine. Fast.ai offers free, practical deep learning courses that emphasize accessibility and minimal BS. Jeremy Howard argues that most deep learning research is a waste of time, advocating for practical problem-solving instead.

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Howard highlights that MLIR could revolutionize GPU programming by simplifying tensor computations, a crucial step for deep learning optimization.
Jeremy Howard: fast.ai Deep Learning Courses and Research
Jeremy Howard's critique of large datasets like ImageNet challenges the prevailing belief that bigger datasets are necessary for significant AI advancements.
Jeremy Howard: fast.ai Deep Learning Courses and Research
The concept of superconvergence, allowing networks to train faster and generalize better, is not widely recognized in academia, despite its potential impact.
Jeremy Howard: fast.ai Deep Learning Courses and Research
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Reading list

What they pointed you toward

books

Deep Learning
by Ian Goodfellow

papers

Notation as a Tool for Thought
by Ken Iverson
Super Convergence
by Leslie Smith

others

APL
by Ken Iverson
Halide
by Unknown
MLIR
by Chris Lattner
DawnBench
by Stanford
DeOldify
by Jason Antich
Swift for TensorFlow
by Google
Fast.ai
by Jeremy Howard
SuperMemo
by Pyotr Wozniak
Ebbinghaus's research on memory
by Hermann Ebbinghaus
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