http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
1 online resource (1 video file, approximately 3 hr., 50 min.)
This three-part series of half-day online events gives attendees an overarching perspective of key topics in data and AI today, including deep learning, data analytics, and natural language processing. Each of these areas is pushing the boundaries of what's possible with more computing power, data, and innovative algorithms. As you'll see in this instance, the world of neural networks is constantly changing, with new strategies and techniques for supervised, semisupervised, and unsupervised learning being developed and refined every day. These sessions will explore how using applied neural networks can help inform and improve your computer vision, natural language processing, audio recognition, and other machine learning and AI applications. About the presenters: Katy Warr is the author of Strengthening Deep Neural Networks: Making AI Less Susceptible to Adversarial Trickery. She's the head of AI at Roke Manor Research, one of the UK's longest established engineering research specialists in AI, cybersecurity, data science, and communications, and has over 20 years' experience developing software for middleware solutions, specializing in policies and security. Anthony Reina is a medical doctor with extensive experience in AI, neurophysiology, telemedicine, and data science. His biggest claim to fame is spending 12 years as a stay-at-home dad to his two sons while his wife served as a psychiatrist in the US Navy. His current work involves privacy-preserving distributed training for 3D convolutional neural networks in medical imaging (which is much easier than raising two teenage boys). Chris Van Pelt is a cofounder of Weights and Biases, an experiment tracking platform for deep learning. For the past 10 years, Chris has dedicated his career to optimizing ML workflows and teaching ML practitioners, making machine learning more accessible to all. He founded Figure Eight/CrowdFlower in 2009 and has also worked as a studio artist, computer scientist, and web engineer. He studied both art and computer science at Hope College. Hanlin Tang is senior director of Intel's AI Lab-an AI research and engineering group that conducts both foundational and applied ML research, builds several ML open source libraries in reinforcement learning and natural language processing, and delivers algorithms-hardware codesign. He previously led teams in computer vision and federal AI programs at Intel. He joined Intel through its acquisition of the deep learning startup Ne...
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