Deep Learning with MATLAB

Data preparation

Handling of large data sets (images, signals, text, …)

Partially automated data labeling and synthetic data generation

AI modeling

Create deep learning models from scratch for image and signal data.

Hyperparameters optimization using Bayesian optimization

Tracking and running modeling runs with the Experiment Manager app for rapid, automated iteration

Accelerate training locally or in the cloud by relying on GPUs

Import and export models from Python frameworks such as Keras and PyTorch.

Introducing the extended deep learning framework to customize and train advanced neural networks

Deployment

Automatic generation of CUDA and C++ code for embedded targets.

Deploying your models to production in a variety of formats