Learn TensorFlow in 30days!
- Optimized for on-device machine learning, by addressing key constraints:
- latency (there's no round-trip to a server),
- privacy (no personal data leaves the device),
- size (reduced model and binary size)
- power consumption (efficient inference and a lack of network connections).
- Multiple platform support, covering Android and iOS devices, embedded Linux, and microcontrollers.
Key Point: The TensorFlow Lite binary is ~1MB when all 125+ supported operators are linked (for 32-bit ARM builds), and less than 300KB when using only the operators needed for supporting the common image classification models InceptionV3 and MobileNet.
- Diverse language support, which includes Java, Swift, Objective-C, C++, and Python.
- installed anaconda and added tf packages
- tried matmul ops in tf 2.0
- learned difference between eager execution vs lazy