Deep Learning Toolbox
Deep Learning Toolbox provides functions, apps, and Simulink blocks for designing, implementing, and simulating deep neural networks. The toolbox provides a framework to create and use many types of networks, such as convolutional neural networks (CNNs) and transformers. You can visualize and interpret network predictions, verify network properties, and compress networks with quantization, projection, or pruning.
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.
