- [Instructor] And now we need to fill our Buffer … that we're going to send along to our model. … So what we're going to do is create a new CIImage, … and we'll call it coreImage … and that's going to be equal to new CIImage … and we'll initialize that with our scaledImage. … And then we'll begin an ImageContext. … And in the ImageContext we'll specify the size … so it will be a new CoreGraphics size … and we'll use that 299, 299 again. … And then we'll say CIContext, … context is equal to CIContext FromContext. … And we're going to use UIGraphics, GetCurrentContext, and null. … Scroll that up a little bit … and then we'll say context Render and we're going … to Render our coreImage and put it into the buffer. … And then we can end our ImageContext. … And now we can get our result. … So, var result equals our classifier which is an instance … of our model. … GetPrediction and we'll pass in our buffer … and out is an NSError called error. … And finally we'll set a returnValue equal … to the result ClassLabel. …
Author
Released
3/21/2019- Defining machine learning
- Training a machine learning model
- Comparing machine learning frameworks
- Using IBM Watson for mobile machine learning
- Using Azure Machine Learning for speech and image recognition
- Training Core ML models
- Comparing client-side and server-side models
Skill Level Beginner
Duration
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Introduction
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What you should know1m 1s
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Using the exercise files2m 36s
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1. Introduction to Machine Learning
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What is machine learning?2m 27s
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Required concepts2m 53s
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Training a model2m 14s
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ML frameworks2m 56s
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2. Server Models: IBM Watson
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Overview of Watson2m 1s
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Visual Recognition: Set up1m 41s
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Create a custom model3m 37s
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Install client SDK package3m 45s
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Run the client app3m 27s
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3. Server Models: Azure Machine Learning
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Custom Vision: Set up4m 25s
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Install client SDK package2m 27s
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Client tie to LUIS5m 16s
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Run the clent app2m 12s
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4. Client Models: Core ML
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Core ML overview2m 10s
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Run the client app1m 45s
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5. Understanding the Offerings
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Conclusion
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Next steps1m 40s
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Video: Client tie to Visual Recognition: Converting model