Deep Learning for Time Series Data (O'Reilly Artificial Intelligence Conference) скачать в хорошем качестве

Deep Learning for Time Series Data (O'Reilly Artificial Intelligence Conference)

Arun Kejariwal and Ira Cohen, both thought leaders in the deep learning space, share a novel two-step approach for building more reliable prediction models by integrating anomalies in them. They then walk you through marrying correlation analysis with anomaly detection, discuss how the topics are intertwined, and detail the challenges you may encounter based on production data. Present at the 2019 O'Reilly Artificial Intelligence Conference. Kejariwal is an R&D leader and data enthusiast who has previously served in management roles at Twitter, Netflix, Yahoo! and Machine Zone. He has also authored numerous publications statistical and machine learning, time series analysis, data-driven mobile marketing, software development, and hardware design. He is a self-described advocate of open source. Ira Cohen is co-founder and chief data scientist of Anodot, the Autonomous Analytics company (www.anodot.com). He specializes in machine learning, statistical modeling and video analysis; classification; semi-supervised learning; Bayesian Network classifiers; and adaptive temporal modeling. He is an expert in the application and development of statistical machine learning to solve business challenges. The O'Reilly AI Conference is where cutting-edge science meets new business implementation. It's a deep dive into emerging AI techniques and technologies with a focus on how to use it in real-world implementations. You'll dissect case studies, delve into the latest research, learn how to implement AI in your projects, share emerging best practices in intelligence engineering and applications, uncover AI's limitations and untapped opportunities and anticipate how AI will change the business landscape. More at https://conferences.oreilly.com/artif....