Modeling Guides

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