These examples include using pre-trained models for [image
These examples include using pre-trained models for [image
classification](label_image) and [object detection](object_detection),
classification](label_image) and [object detection](object_detection),
and driving the [training](training) of a pre-defined model - all using the
and driving the [training](training) of a pre-defined model - all using the
TensorFlow Java APIs.
TensorFlow Java API.
The TensorFlow Java API does not have feature parity with the Python API.
The TensorFlow Java API does not have feature parity with the Python API.
The Java APIs are most suitable for inference using pre-trained models
The Java API is most suitable for inference using pre-trained models
and for training pre-defined models from a single Java process.
and for training pre-defined models from a single Java process.
However, Python will be the most convenient language for defining the
Python will be the most convenient language for defining the
numerical computation of a model.
numerical computation of a model.
-[Slides](https://docs.google.com/presentation/d/e/2PACX-1vQ6DzxNTBrJo7K5P8t5_rBRGnyJoPUPBVOJR4ooHCwi4TlBFnIriFmI719rDNpcQzojqsV58aUqmBBx/pub?start=false&loop=false&delayms=3000) from January 2018.
-[Slides](https://docs.google.com/presentation/d/e/2PACX-1vQ6DzxNTBrJo7K5P8t5_rBRGnyJoPUPBVOJR4ooHCwi4TlBFnIriFmI719rDNpcQzojqsV58aUqmBBx/pub?start=false&loop=false&delayms=3000) from January 2018.
