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