Deep Learning Toolbox
R2026bDeep Learning Toolbox™ provides functions, apps, and Simulink® blocks for designing, training, and simulating deep neural networks. You can visualize and interpret predictions, verify network properties, and compress networks with pruning, projection, or quantization. You can also generate C/C++, CUDA®, and HDL code for trained networks (with MATLAB® Coder™, GPU Coder™, or Deep Learning HDL Toolbox™).
The toolbox provides interfaces to other AI frameworks, enabling inference in MATLAB and Simulink and allowing the import of models from PyTorch®, TensorFlow™, Keras™, and ONNX™ into MATLAB.
The Deep Network Designer app lets you design, import, edit, and analyze networks. The Time Series Modeler app lets you train and compare models for time series prediction without writing code.
Get Started
Learn the basics of Deep Learning Toolbox
Deep Learning with Simulink
Extend deep learning workflows using Simulink
Preprocess Data for Deep Neural Networks
Manage and preprocess data for deep learning
Import and Build Deep Neural Networks
Build networks using command-line functions or interactively using the Deep Network Designer app
Train Deep Neural Networks
Train networks using built-in training functions or custom training loops
Visualize and Verify Deep Neural Networks
Visualize network behavior, explain predictions, and verify robustness
Generate Code and Deploy Deep Neural Networks
Generate C/C++, CUDA, or HDL code and export or deploy deep learning networks
Explore deep learning workflows with computer vision, image processing, automated driving, signals, audio, text analytics, and computational finance
