Deep Learning Toolbox



Deep Learning Toolbox

Design, train, analyze, and simulate deep learning networks

Deep Learning for Engineers

Create and use explainable, robust, and scalable deep learning models for automated visual inspection, reduced order modeling, wireless communications, computer vision, and other applications.

Deep Learning in Simulink

Use deep learning with Simulink to test the integration of deep learning models into larger systems. Simulate models based on MATLAB or Python to assess model behavior and system performance.

Integration with PyTorch and TensorFlow

Exchange deep learning models with Python-based deep learning frameworks. Import PyTorch, TensorFlow, and ONNX models, and export networks to TensorFlow and ONNX with a single line of code. Co-execute Python-based models in MATLAB and Simulink.

Code Generation and Deployment

Automatically generate optimized C/C++ code (with MATLAB Coder) and CUDA code (with GPU Coder) for deployment to CPUs and GPUs. Generate synthesizable Verilog® and VHDL® code (with Deep Learning HDL Toolbox) for deployment to FPGAs and SoCs.

Explainability and Verification

Visualize training progress and activations of deep neural networks. Use Grad-CAM, D-RISE, and LIME to explain network results. Verify the robustness and reliability of deep neural networks.

Network Design and Training

Use deep learning algorithms to create CNNs, LSTMs, GANs, and transformers, or perform transfer learning with pretrained models. Automatically label, process, and augment image, video, and signal data for network training.

Low-Code Apps

Accelerate network design, analysis, and transfer learning for built-in and Python-based models by using the Deep Network Designer app. Tune and compare multiple models using the Experiment Manager app.

Deep Learning Compression

Compress your deep learning network with quantization, projection, or pruning to reduce its memory footprint and increase inference performance. Assess inference performance and accuracy using the Deep Network Quantizer app.

Scaling Up Deep Learning

Speed up deep learning training using GPUs, cloud acceleration, and distributed computing. Train multiple networks in parallel and offload deep learning computations to run in the background.

​“This was the first time we were simulating sensors with neural networks on one of our powertrain ECUs. Without MATLAB and Simulink, we would have to use a tedious manual coding process that was very slow and error-prone.”

Katja Deuschl, AI developer at Mercedes-Benz

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