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On Demand

From fraud and security threat detection to flagging abnormalities in healthcare imaging data, there are countless business applications for automatic identification of abnormal data.  This process can be challenging, especially when working with large, complex data. This talk explores deep learning approaches (Sequence models, VAEs, GANs) for anomaly detection, when to use them, performance benchmarks and product possibilities.

We’ll cover:

  • Why and when to use deep learning for anomaly detection

  • In-depth coverage of various deep learning models approaches and how they can be applied for anomaly detection

  • Performance benchmark for various deep learning models for a given dataset 

  • Example use cases, product possibilities, and best practices.

We’ll also address common challenges associated with a lack of labeled anomalous samples or the sheer variety of samples in this space by exploring and demonstrating the performance of unsupervised learning approaches.

 

Speakers


Research Engineer, Cloudera Fast Forward Labs

Victor Dibia

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Victor Dibia is a Research Engineer at Cloudera Fast Forward Labs. He spends his time researching and prototyping ideas at the intersection of Machine Learning and Human-Computer Interaction. Victor has delivered talks/workshops at industry conferences such as SXSW, WebSummit, NVIDIA GTC and has published research at academic venues such as AAAI, ACM CHI, and IEEEVis. His research work has been recognized with best paper awards at conferences such as AAAI, and IEEEVis.

Research Engineer, Cloudera Fast Forward Labs

Nisha Muktewar

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Nisha Muktewar is a Research Engineer at Cloudera Fast Forward Labs, where she spends time researching latest ideas in machine learning, builds prototypes that showcase these capabilities when applied to real-world use cases, and advises clients in this space. Prior to joining Cloudera, she worked as a Manager in Deloitte’s Actuarial, Advanced Analytics & Modeling practice leading teams in designing, building, and implementing predictive modeling solutions for pricing, consumer behavior, marketing mix, and customer segmentation use cases for insurance and retail/consumer businesses. She holds a Bachelor of Engineering degree in computer science from University of Pune, India.

Algorithm Engineer and Data Scientist, Anodot

Meir Toledano

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Meir Toledano is an algorithm engineer and data scientist at Anodot, and is instrumental in developing the company’s patented machine learning anomaly detection and forecasting. He began his career working as an aeronautic engineer in Paris. He later pivoted to the financial industries to develop trading algorithms and risk models, and recently the tech industry in Israel’s Start-Up Nation.

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