Deep Learning with GPUs in Anaconda Enterprise
AI is a hot topic right now. While a lot of the conversation surrounding advanced AI techniques such as deep learning and machine learning can be chalked up to hype, the underlying tools have been proven to provide real value. Even better, the tools aren’t as hard to use as you might think. As Keras author Francois Chollet said in Deep Learning with Python: “Don’t believe the short-term hype, but do believe in the long-term vision.”
In our webinar Accelerating Deep Learning with GPUs, now available on-demand, Anaconda Director of Community Innovation Stan Seibert breaks through the hype and shares some insights on what AI really is. He then provides practical examples showcasing the myriad ways businesses are leveraging AI today, and how you can get started using Anaconda Enterprise, the AI/ML enablement platform from Anaconda.
At the end of this webinar, you’ll be comfortable explaining your company’s AI strategy at the next board meeting and training your own deep learning models on your laptop.
What You’ll Learn:
- How deep learning really works and why it’s so popular right now
- The simplest way to get started with deep learning with just a few lines of code
- Three critical GPU needs for enterprise data science teams
- The most important questions to ask to ensure successful deep learning deployment
- Specific use cases where you can apply deep learning today
Whether you’re an experienced deep learning practitioner or just getting started, Stan’s talk will be both fun and informative. You’ll hear essential deep learning background, exciting use cases, and real-world examples of training and deploying deep learning models in enterprise environments.
Stan leads the Community Innovation team at Anaconda, where his work focuses on high-performance GPU computing and designing data analysis, simulation, and processing pipelines. He is a longtime advocate of the use of Python and GPU computing for research. Prior to joining Anaconda, Stan served as Chief Data Scientist at Mobi, where he worked on vehicle fleet tracking and route planning.
Stan received a PhD in experimental high energy physics from the University of Texas at Austin and performed research at Los Alamos National Laboratory, University of Pennsylvania, and the Sudbury Neutrino Observatory.
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