Deep Learning Toolbox


Deep Learning Toolbox

Design, train, and analyze deep learning networks

Deep Learning Applications

Train deep learning models for classification, regression, and feature learning applications for automated driving, signal and audio processing, wireless communications, image processing, and more.

Network Design and Model Management

Speed up the development of deep learning models using low-code apps. Create, train, analyze, and debug a network using Deep Network Designer app. Tune and compare multiple models using Experiment Manager app.

Pretrained Models

Access popular models with a single line of code in MATLAB. Use PyTorch™ via ONNX and TensorFlow™ to import any model into MATLAB.


Visualize training progress and activations of the learned features in a deep learning network. Use Grad-CAM, Occlusion Mapping, and LIME to explain deep learning model results.


Label, process, and augment data for network training. Automate data labeling with built-in algorithms.

Training Acceleration

Speed up deep learning training using GPUs, cloud acceleration, and distributed computing.

Code Generation

Automatically generate optimized CUDA® code with GPU Coder™, and generate C and C++ code with MATLAB Coder™ to deploy deep learning networks to NVIDIA GPUs and various processors. Prototype and implement deep learning networks on FPGAs and SoCs using Deep Learning HDL Toolbox™.

Simulation with Simulink

Simulate deep learning networks with control, signal processing, and sensor fusion components to assess the impact of your deep learning model on system-level performance.

Deep Learning Compression

Quantize and prune your deep learning network to reduce memory usage and increase inference performance. Analyze and visualize the tradeoff between increased performance and inference accuracy using the Deep Network Quantizer app.

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