![]() Clustering algorithms: linear or kernel k-means, Chinese Whispers, and Newman clustering.An online kernelized centroid estimator/novelty detector and offline support vector one-class classification.Relevance vector machines for classification and regression.Reduced-rank methods for large-scale classification and regression.Monte-Carlo (every-visit, and first-visit).LSTM network layer implemented - needs more testing.feed-forward or simple recurrent layers can be mixed in one network.multiple layers, several non-linearity models.General-Purpose Machine Learning Libraries It uses generator part of MNIST GAN as Core ML model. Ĭore ML currently doesn't support training models, but still, you can replace model by downloading a new one from a server in runtime. There are many curated lists of pre-trained neural networks in Core ML format:,. Third-party converters to CoreML format are also available for some models from: It is highly optimized to Apple hardware.Ĭurrently CoreML is compatible (partially) with the following machine learning packages via coremltools python package: Core ML is an Apple framework to run inference on device.It contains converters from some popular machine learning libraries to the Apple format. Some of the resources are awesome, some are great, some are fun, and some can serve as an inspiration. The order doesn't reflect my personal preferences or anything else. Resources are sorted alphabetically or randomly. ![]() Also, I included links to some relevant web APIs, blog posts, videos and learning materials. This list consists mainly of libraries written in Objective-C, Swift, C, C++, JavaScript and some other languages that can be easily ported to iOS. Most of the de-facto standard tools in AI-related domains are written in iOS-unfriendly languages (Python/Java/R/Matlab) so finding something appropriate for your iOS application may be a challenging task. Speech Recognition (TTS) and Generation (STT). ![]() Curated list of resources for iOS developers in following topics: ![]()
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