jamesonthecrow
- Karma
- 91
- Created
- December 11, 2017 (8y ago)
- Submissions
- 0
- Building an on-device face mask detector (heartbeat.fritz.ai)
- Deep learning has a size problem (heartbeat.fritz.ai)
- On-device training in Core ML 3 and why it matters for developers (heartbeat.fritz.ai)
- Combining artificial intelligence and augmented reality in mobile apps (heartbeat.fritz.ai)
- Synthetic data: your data moat is shallower than you think (heartbeat.fritz.ai)
- Synthetic Data: A bridge over the data moat (heartbeat.fritz.ai)
- Distributing on-device machine learning models with hardware targeting (heartbeat.fritz.ai)
- Building an iOS app to recognize handwritten digits with Core ML (heartbeat.fritz.ai)
- Best of Machine Learning in 2018: Reddit Edition (heartbeat.fritz.ai)
- Creating an extremely tiny, 17 KB style transfer model with just 11,868 weights (heartbeat.fritz.ai)
- Simplifying user experience with Create ML and on-device text classification (heartbeat.fritz.ai)
- Streamlining the Reddit app's submission UX with natural language processing (heartbeat.fritz.ai)
- 20 Minute Masterpiece: Training a Style Transfer Model with Colab and Fritz (heartbeat.fritz.ai)
- Announcing Fritz ML Grants – Get $1000 in cloud credits to build ML powered apps (heartbeat.fritz.ai)
- The Lifecycle of Mobile Machine Learning Models (heartbeat.fritz.ai)
- Why data scientists and ML engineers should start learning Swift (heartbeat.fritz.ai)
- TensorFlow Dev Summit 2018 – Just the mobile bits (heartbeat.fritz.ai)
- Benchmarking Core ML – Estimating model runtimes on iOS (heartbeat.fritz.ai)
- Benchmarking Core ML – Estimating model runtimes on iOS (heartbeat.fritz.ai)
- Core ML Simplified with Lumina (heartbeat.fritz.ai)
- Mobile Machine Learning 101: Glossary (heartbeat.fritz.ai)