Deep Learning Toolbox
Design, train, and analyze deep learning networks
Have questions? Contact sales.
Have questions? Contact sales.
Deep Learning Toolbox™ provides a framework for designing and implementing deep neural networks with algorithms, pretrained models, and apps. You can use convolutional neural networks (ConvNets, CNNs) and long short-term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. You can build network architectures such as generative adversarial networks (GANs) and Siamese networks using automatic differentiation, custom training loops, and shared weights. With the Deep Network Designer app, you can design, analyze, and train networks graphically. The Experiment Manager app helps you manage multiple deep learning experiments, keep track of training parameters, analyze results, and compare code from different experiments. You can visualize layer activations and graphically monitor training progress.
You can import networks and layer graphs from TensorFlow™ 2, TensorFlow-Keras, PyTorch®, the ONNX™ (Open Neural Network Exchange) model format, and Caffe. You can also export Deep Learning Toolbox networks and layer graphs to TensorFlow 2 and the ONNX model format. The toolbox supports transfer learning with DarkNet-53, ResNet-50, NASNet, SqueezeNet and many other pretrained models.
You can speed up training on a single- or multiple-GPU workstation (with Parallel Computing Toolbox™), or scale up to clusters and clouds, including NVIDIA® GPU Cloud and Amazon EC2® GPU instances (with MATLAB Parallel Server™).
Train deep learning models for classification, regression, and feature learning applications for automated driving, signal and audio processing, wireless communications, image processing, and more.
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.
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.
Speed up deep learning training using GPUs, cloud acceleration, and distributed computing.
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™.
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.
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.
Your school may already provide access to MATLAB, Simulink, and add-on products through a campus-wide license.