Gabriel Ha, MathWorks
Deep Learning Toolbox™ (formerly Neural Network 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 advanced network architectures such as generative adversarial networks (GANs) and Siamese networks using custom training loops, shared weights, and automatic differentiation. Apps and plots help you visualize activations, edit and analyze network architectures, and monitor training progress.
You can exchange models with TensorFlow™ and PyTorch through the ONNX™ format and import models from TensorFlow-Keras and Caffe. The toolbox supports transfer learning with a library of pretrained models (including NASNet, SqueezeNet, Inception-v3, and ResNet-101).
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™).
Deploy your network onto platforms such as Intel® CPUs or their Arm-Mali® GPUs, NVIDIA GPUs, and ARM® processors using coder products to auto generate code.
Deep Learning Toolbox provides algorithms and tools for creating, training, and analyzing deep networks. You can use deep learning with CNNs for image classification, and deep learning with LSTM networks for time-series and sequence data. Deep Learning Toolbox comes with numerous pre-built examples you can leverage, including classifying moving objects in a scene and detecting facial features with regression. You can also build advanced network architectures like GANs and Siamese networks using custom training loops, shared weights, and automatic differentiation.
Deep Network Designer makes it simple to create and modify deep networks. The drag-and-drop interface allows you to visualize the layers and connections and add learnable layer parameters. After creating a network, you can quickly check the architecture for errors. Finally, export your network to the workspace for training, or generate its corresponding MATLAB code so your colleagues can easily reproduce and refine your work.
You can create network architectures from scratch or by utilizing transfer learning with pretrained networks like ResNet and Inception. Deep Learning Toolbox supports interoperability with other frameworks including TensorFlow, PyTorch, and MXNet .You can also import networks and network architectures from TensorFlow Keras and Caffe. And since Deep Learning Toolbox supports the ONNX model format, you can import models, leverage MATLAB for tasks like visualizing and optimizing your network, then export your model for use in other deep learning frameworks.
You can speed up training on a single- or multiple-GPU workstation or scale to clusters and clouds, including NVIDIA GPU Cloud and Amazon EC2® GPU.
Deep Learning Toolbox can be used in conjunction with code generation tools, enabling you to deploy deep learning algorithms to targets like NVIDIA GPUs, and Intel and ARM processors. This auto-generated code provides a significant performance boost in inference applications.
For more information about Deep Learning Toolbox, please check out the Deep Learning Toolbox Product page, and don’t hesitate to contact us with any questions.