by Ben Lorica | Released September 2017 | ISBN: 9781491996393

In this video, O'Reilly's Chief Data Scientist, Ben Lorica highlights some recent research initiatives and trends in data from both the AI community and the big data/data science world. Topics include: the emergence of deep learning as a general-purpose machine learning technique; strategies for overcoming the main bottlenecks in running successful AI/machine learning projects (i.e., lack of training data and deploying/monitoring models in production); the transition from offline to continuous learning (including reinforcement learning); and the emerging software and hardware infrastructure for AI and machine learning. This is a must-view for every data scientist, data architect/engineer, data/business analyst, and manager or CxO who wants to stay current in the rapidly evolving world of big data, data science, and AI.Survey and understand the latest trends in deep learningDiscover new open source tools bringing computer vision and text recognition to wide audiencesLearn about the emergence of the machine learning engineerExplore opportunities to do machine learning for start-upsHear about the recent trends in real-time, streaming, and reinforcement learningBecome familiar with the hardware infrastructure for AI and machine learningLearn how labeled data, generative models, and weak supervision overcome the main bottlenecks in running successful AI/machine learning projectsBen Lorica is the Chief Data Scientist at O'Reilly Media, Inc. and is the Program Director of both the Strata Data Conference and the O'Reilly Artificial Intelligence Conference. He has applied business intelligence, data mining, machine learning and statistical analysis in a variety of settings, including direct marketing, consumer and market research, targeted advertising, text mining, and financial engineering. His background includes stints with an investment management company, internet startups, and financial services. Show and hide more

1. Introduction 00:01:27
2. Trends in Deep Learning 00:04:10
3. The Machine Learning Engineer 00:03:12
4. Machine Learning and Start-ups 00:02:19
5. Labeled Data, Generative Models, Weak Supervision 00:07:35
6. Real Time, Streaming, and Reinforcement Learning 00:08:03
7. Hardware Infrastructure for Machine Learning, Deep Learning and AI 00:06:20
8. Wrap Up 00:01:42
Show and hide more