Jeremy Howard is the founder of fast.ai, a research institute dedicated to make deep learning more accessible. He is also a Distinguished Research Scientist at the University of San Francisco, a former president of Kaggle as well a top-ranking competitor there, and in general, he's a successful entrepreneur, educator, research, and an inspiring personality in the AI community. This conversation is part of the Artificial Intelligence podcast. INFO:
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EPISODE LINKS:
- Jeremy Twitter:
- fast.ai Twitter:
- fast.ai Web:
- fast.ai Course:
OUTLINE:
0:00 - Introduction
1:18 - First program
3:07 - Favorite programming languages
15:01 - Programming languages for machine learning
23:35 - Fast.ai intro (to be continued later)
24:31 - Ai and deep learning in medicine
32:30 - Privacy
37:55 - Fast.ai
40:42 - Theory vs practice
45:43 - DAWNBench - Stanford deep learning benchmark
56:24 - Fusing multiple audio and image sources
59:01 - Learning rate & deep learning as an experimental science
1:04:32 - Working with data
1:06:16 - Deep learning cloud options
1:09:12 - Deep learning frameworks
1:17:51 - How long does it take to finish fast.ai courses?
1:19:49 - Lessons from teaching deep learning
1:21:34 - Advice for people starting with deep learning
1:27:02 - Startups and entrepreneurship
1:32:21 - Anki and spaced repetition
1:40:06 - Next breakthrough in deep learning
1:41:17 - Job displacement and Andrew Yang
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