Working with TensorFlow Lite on Android with C++ /
"Other Videos in This Category Deep Learning with PyTorch Deep Learning with PyTorch Luca Pietro Giovanni Antiga; Thomas Viehmann; Eli Stevens Federated Learning Federated Learning Qiang Yang; Yang Liu; Yong Cheng; Yan Kang; Tianjian Chen; Han Yu Compatibility Modeling Compatibility Modeling Xu...
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| Format: | Video |
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| Language: | English |
| Published: |
[Place of publication not identified] :
O'Reilly Media,
2020.
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| Subjects: | |
| Online Access: | CONNECT CONNECT |
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| 520 | |a "Other Videos in This Category Deep Learning with PyTorch Deep Learning with PyTorch Luca Pietro Giovanni Antiga; Thomas Viehmann; Eli Stevens Federated Learning Federated Learning Qiang Yang; Yang Liu; Yong Cheng; Yan Kang; Tianjian Chen; Han Yu Compatibility Modeling Compatibility Modeling Xuemeng Song; Liqiang Nie; Yinglong Wang; Gary Marchionini Representation and Understanding Representation and Understanding Allan Collins; Daniel G Bobrow Uncertainty in Artificial Intelligence Uncertainty in Artificial Intelligence MKP There are many cases where developers on mobile write lower-level C++ code for their Android applications using the Android NDK, OpenCV and other technologies. Joe Bowser (Adobe) explores how to use TF Lite's C++ API on Android with existing code so the code can interact directly with TF Lite without having to make a round trip through Java Native Interface (JNI) and the Android subsystem, allowing for cleaner, more portable code so that can even be used in iOS or other platforms. You'll also discover common pitfalls when working with TFLite as a C++ library, using TFLite with OpenCV and/or Halide on Android, as well as some techniques to do integration testing to allow your tests to work in a CI/CD environment."--Resource description page | ||
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