Deep learning is an incredibly powerful technology for understanding messy data from the real world—and the TensorFlow machine learning library is the ideal way to harness that power.
Traditionally, deep learning has been associated with data centers and giant clusters of high-powered GPU machines. However, it can be very expensive and time-consuming to send all of the data a device has access to across a network connection. Running on mobile makes it possible to deliver very interactive applications in a way that’s not possible when you have to wait for a network round trip.
- Use cases including speech, image, and object recognition, translation, and text classification
- Common patterns for integrating a deep-learning model into your application
- Several examples for running TensorFlow on Android, iOS, and Raspberry Pi
- Techniques for testing your deep-learning model inside your application
- Methods to help you prepare your solution for mobile deployment
- Optimizing your model for latency, RAM usage, model file size, and binary size