![]() ![]() See how to use MATLAB, a simple webcam, and a deep neural network to identify objects in your surroundings.ĭeep Learning with MATLAB: Transfer Learning in 10 Lines of MATLAB Code Learn how to use transfer learning in MATLAB to re-train deep learning networks created by experts for your own data or task. You can check the modified architecture for errors in connections and property assignments using a network analyzer.ĭeep Learning with MATLAB: Deep Learning in 11 Lines of MATLAB Code It demonstrates the ease with which you can use the tool to modify the last few layers in the imported network as opposed to modifying the layers in the command line. This video shows how to use the app in a transfer learning workflow. Interactively Modify a Deep Learning Network for Transfer Learningĭeep Network Designer is a point-and-click tool for creating or modifying deep neural networks. You will learn to use deep learning techniques in MATLAB for image recognition. This free, two-hour deep learning tutorial provides an interactive introduction to practical deep learning methods. 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 ® ResNet-50, NASNet, SqueezeNet and many other pretrained models. The toolbox supports transfer learning with DarkNet-53, You can also exportĭeep Learning Toolbox networks and layer graphs to TensorFlow 2 and the ONNX model format. You can import networks and layer graphs from TensorFlow™ 2, TensorFlow-Keras, and PyTorch ®, the ONNX™ (Open Neural Network Exchange) model format, and Caffe. You can visualize layer activations and graphically ![]() Multiple deep learning experiments, keep track of training parameters, analyze results, andĬompare code from different experiments. The Experiment Manager app helps you manage With the Deep Network Designer app, you canĭesign, analyze, and train networks graphically. Generative adversarial networks (GANs) and Siamese networks using automatic differentiation,Ĭustom training loops, and shared weights. You can build network architectures such as Regression on image, time-series, and text data. (ConvNets, CNNs) and long short-term memory (LSTM) networks to perform classification and You can use convolutional neural networks Enroll in our free 4-course series on Coursera to build your skills in data science and apply them to real-world examples. ![]() Design, train, and analyze deep learning networksĭeep Learning Toolbox™ provides a framework for designing and implementing deep neural networks withĪlgorithms, pretrained models, and apps. ![]()
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