You already know you need to learn about deep learning.
From mobile apps at the fingertips of billions to burly enterprise applications powering the world’s biggest businesses to a new generation of cutting-edge research tools, deep learning has become ubiquitous.
That makes spending an hour or so to get oriented a more worthwhile investment than ever.
This is where Will Ramey, senior director and global head of developer programs at NVIDIA, and the host of a GTC Digital session called “Deep Learning Demystified,” comes in.
Ramey’s a technologist with a knack for making the fundamental concepts of deep learning, and its history, accessible to people whose expertise lies in other fields. And that’s just the sort of people who can benefit from putting it to work now.
Ramey spoke with AI Podcast host Noah Kravitz about how NVIDIA developed into an AI company thanks to the invention of the parallel computing platform CUDA, as well as the broader state of deep learning today.
Key Points From This Episode:
- Ramey provided an overview of the activities available to developers at GTC Digital, including seven full-day workshops and 15 short courses by the NVIDIA Deep Learning Institute, which has trained over 200,000 people worldwide.
- He was the very first guest on the NVIDIA AI Podcast. He joined our first episode — the most streamed to date — three years ago to talk about the history of the AI boom and the fundamentals of deep learning.
- Ramey explains how deep learning is used to improve self-driving cars. Rather than spending decades collecting real-world driving data, deep learning allows for simulations that can be run in parallel, accelerating training time.
Learn more about the developer community that Ramey supports.
Tweetables:
“Deep neural networks, they’re much larger, they’re deeper — meaning they have lots more layers — and require a lot more data to train. Being able to do that work in a practical amount of time requires something like a GPU parallel accelerator.” — Will Ramey [9:31]
“[Deep learning] has made this technology available to many, many more people with much less domain-specific subject matter expertise.” — Will Ramey [15:29]
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