Deep Learning Toolbox
The Deep Learning Toolbox offers functions, interactive apps, and Simulink blocks that support the design, implementation, and simulation of deep neural networks. It provides a flexible environment for building and applying various network types, including convolutional neural networks (CNNs) and transformer models. Users can visualize and interpret predictions, assess network behavior, and optimize models through techniques like quantization, projection, and pruning.
Using the Deep Network Designer app, you can interactively create, modify, and evaluate networks, bring in pretrained models, and export designs to Simulink. The toolbox also integrates with external deep learning frameworks, allowing you to import models from PyTorch®, TensorFlow™, and ONNX™ for inference, transfer learning, simulation, and deployment, as well as export models back to TensorFlow and ONNX.
In addition, the toolbox supports automatic generation of C/C++, CUDA®, and HDL code for trained networks.
With the Deep Network Designer app, you can design, edit, and analyze networks interactively, import pretrained models, and export networks to Simulink. The toolbox lets you interoperate with other deep learning frameworks. You can import PyTorch®, TensorFlow™, and ONNX™ models for inference, transfer learning, simulation, and deployment. You can also export models to TensorFlow and ONNX.
You can automatically generate C/C++, CUDA®, and HDL code for trained 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.