PerceptiLabs

Don’t Take Activation Functions for Granted

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In this video, Martin Isaksson, the CEO and co-founder of PerceptiLabs, explains the importance of the activation function in determining a machine learning model’s performance. For more information, visit https://www.perceptilabs.com Read our blog: https://blog.perceptilabs.com Leave your questions and feedback: https://forum.perceptilabs.com Join our friendly community: https://perceptilabs-com.slack.com/join/shared_invite/zt-mxohtpkm-lQI_0nT~tBeUd2RtZIfe4g#/shared-invite/email

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Instant Models: Detecting Anomalies in Magnetic Tiles

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In this video, Robert Lundberg, CTO and Co-founder of PerceptiLabs, builds a +80% accuracy model that detects anomalies in magnetic tiles in under 5 minutes! Try out PerceptiLabs (free for developers!): https://www.perceptilabs.com Dataset used in the video: https://www.kaggle.com/alex000kim/magnetic-tile-surface-defects/version/1 Read our blog: https://blog.perceptilabs.com Leave your questions and feedback: https://forum.perceptilabs.com Join our friendly community: https://perceptilabs-com.slack.com/join/shared_invite/zt-mxohtpkm-lQI_0nT~tBeUd2RtZIfe4g#/shared-invite/email

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PerceptiLabs Demonstration of Fast, Visual, and Explainable Machine Learning Modeling

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Robert Lundberg, Chief Technology Officer and Co-founder of PerceptiLabs, demonstrates the company’s latest edge AI and vision technologies and products at the 2021 Embedded Vision Summit. Specifically, Lundberg demonstrates fast, visual, and explainable machine learning modeling. Lundberg uses image classification and transfer learning to train a model to classify brain tumors in magnetic resonance imaging

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Use Case: Air Conditioner Piston Check

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This blog post was originally published at PerceptiLabs’ website. It is reprinted here with the permission of PerceptiLabs. With the growing use of computer vision in Industrial IoT (IIoT) and Industry 4.0 to analyze and detect defects, we were inspired to build an image recognition model that can classify images of AC pistons. Materials used

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Use Case: Defect Detection in Metal Surfaces

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This blog post was originally published at PerceptiLabs’ website. It is reprinted here with the permission of PerceptiLabs. With the rise of Industrial IoT (IIoT) and Industry 4.0, ML is playing an ever-increasing role in enabling automation. With this in mind, we built an ML model that can classify defects on metal materials using image

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Case Study: Genetic Screening Using CNNs in PerceptiLabs

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This blog post was originally published at PerceptiLabs’ website. It is reprinted here with the permission of PerceptiLabs. One group of scientists leveraging ML is the Buchser lab at Washington University in Saint Louis School of Medicine in the Department of Genetics. There, Dr. Buchser and his team are working in functional genomics, High-Throughput Screening,

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Here you’ll find a wealth of practical technical insights and expert advice to help you bring AI and visual intelligence into your products without flying blind.

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