On-device training in Core ML 3 and why it matters for developersheartbeat.fritz.ai·2 pts·jamesonthecrow·0
Combining artificial intelligence and augmented reality in mobile appsheartbeat.fritz.ai·5 pts·jamesonthecrow·0
Distributing on-device machine learning models with hardware targetingheartbeat.fritz.ai·3 pts·jamesonthecrow·0
Building an iOS app to recognize handwritten digits with Core MLheartbeat.fritz.ai·1 pts·jamesonthecrow·0
Creating an extremely tiny, 17 KB style transfer model with just 11,868 weightsheartbeat.fritz.ai·2 pts·jamesonthecrow·0
Simplifying user experience with Create ML and on-device text classificationheartbeat.fritz.ai·1 pts·jamesonthecrow·0
Streamlining the Reddit app's submission UX with natural language processingheartbeat.fritz.ai·2 pts·jamesonthecrow·0
20 Minute Masterpiece: Training a Style Transfer Model with Colab and Fritzheartbeat.fritz.ai·1 pts·jamesonthecrow·0
Announcing Fritz ML Grants – Get $1000 in cloud credits to build ML powered appsheartbeat.fritz.ai·8 pts·jamesonthecrow·0
Fritz wants to help developers bring machine learning to their mobile appstechcrunch.com·11 pts·jamesonthecrow·0
Why data scientists and ML engineers should start learning Swiftheartbeat.fritz.ai·7 pts·jamesonthecrow·0