Modeling GuidesΒΆ

The following documents describe the various modeling features provided by the MLTK.
For complete tutorials, please see the Tutorials.

Name Description
Model Profiler Provides information about how efficiently a model may run on an embedded target
Model Profiler Utility Standalone model profiler executable with webpage interface
Model Visualizer View an ML model in an interactive webpage
Model Specification Specify everything needed to create, train, and evaluate a machine learning model
Model Training Train a machine learning model using the MLTK and Google Tensorflow
Model Training via SSH Train a machine learning model on a cloud server via SSH
Model Training Monitor with Tensorboard Monitor/profile the training of a model using Tensorboard
Model Evaluation Evaluate the accuracy of a trained model
Model Quantization Reduce memory usage by compressing model weights
Model Parameters Embed custom parameters into the generated model file
Model Summary Generate a text summary of a model
Model Archive Store model information in a distributable archive
Model Search Path Configure how models are found by the MLTK commands and APIs