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Core MLagueña

DZone's Guide to

Core MLagueña

If you haven't yet taken the time to dive deeper into CoreML, see some of the capabilities it's bringing to machine learning for iOS.

· Mobile Zone
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Checked out the WW17 Roundup yet? OK then, let’s start digging into this new stuff a little deeper. And we’ll start with the one with the most buzz around the web,

Introducing Core ML: "Machine learning opens up opportunities for creating new and engaging experiences. Core ML is a new framework which you can use to easily integrate machine learning models into your app. See how Xcode and Core ML can help you make your app more intelligent with just a few lines of code."

Vision Framework: Building on Core ML: "Vision is a new, powerful, and easy-to-use framework that provides solutions to computer vision challenges through a consistent interface. Understand how to use the Vision API to detect faces, compute facial landmarks, track objects, and more. Learn how to take things even further by providing custom machine learning models for Vision tasks using CoreML."

By “more intelligent” what do we mean exactly here? Why, check out iOS 11: Machine Learning for everyone:

"The API is pretty simple. The only things you can do are:

  1. loading a trained model
  2. making predictions
  3. profit!!!

This may sound limited but in practice loading a model and making predictions is usually all you’d want to do in your app anyway…"

Yep, probably. Some people are very excited about that approach:

Apple Introduces Core ML:

"When was the last time you opened up a PDF file and edited the design of the document directly?

You don’t.

PDF is not about making a document. PDF is about being able to easily view a document.

With Core ML, Apple has managed to achieve an equivalent of PDF for machine learning. With their .mlmodel format, the company is not venturing into the business of training models (at least not yet). Instead, they have rolled out a meticulously crafted red carpet for models that are already trained. It’s a carpet that deploys across their entire lineup of hardware.

As a business strategy, it’s shrewd. As a technical achievement, it’s stunning. It moves complex machine learning technology within reach of the average developer…"

Well, speaking as that Average Developer here, this sure sounds like a great way to dip a toe into $CURRENT_BUZZWORD without, y’know, having to actually work at it. Great stuff!

Here’s some more reactions worth reading:

Here are some models to try it out with, or you can convert your own built with XGBoost, Caffe, LibSVM, scikit-learn, and Keras:

  • Places205-GoogLeNet CoreML (Detects the scene of an image from 205 categories such as an airport terminal, bedroom, forest, coast, and more).
  • ResNet50 CoreML (Detects the dominant objects present in an image from a set of 1000 categories such as trees, animals, food, vehicles, people, and more).
  • Inception v3 CoreML (Detects the dominant objects present in an image from a set of 1000 categories such as trees, animals, food, vehicles, people, and more).
  • VGG16 CoreML (Detects the dominant objects present in an image from a set of 1000 categories such as trees, animals, food, vehicles, people, and more).

And some samples and tutorials:

Also note NSLinguisticTagger that’s part of the new ML family here too.

For further updates we miss, check out awesome-core-ml and Machine Learning for iOS!

UPDATES:

YOLO: Core ML versus MPSNNGraph

Why Core ML will not work for your app (most likely)

Analysts agree that a mix of emulators/simulators and real devices are necessary to optimize your mobile app testing - learn more in this white paper, brought to you in partnership with Sauce Labs.

Topics:
mobile ,mobile app development ,ios ,machine learning ,coreml

Published at DZone with permission of Alex Curylo, DZone MVB. See the original article here.

Opinions expressed by DZone contributors are their own.

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