Create new deep networks for image classification and regression tasks by defining the network architecture and training the network from scratch. You can also use transfer learning to take advantage of the knowledge provided by a pretrained network to learn new patterns in new data. Fine-tuning a pretrained image classification network with transfer learning is typically much faster and easier than training from scratch. Using pretrained deep networks enables you to quickly learn new tasks without defining and training a new network, having millions of images, or having a powerful GPU.
After defining the network architecture, you must define training parameters using the trainingOptions function. You can then train the network using trainNetwork. Use the trained network to predict class labels or numeric responses.
You can train a convolutional neural network on a CPU, a GPU, multiple CPUs or GPUs, or in parallel on a cluster or in the cloud. Training on a GPU or in parallel requires Parallel Computing Toolbox™. Using a GPU requires a CUDA® enabled NVIDIA® GPU with compute capability 3.0 or higher. Specify the execution environment using the trainingOptions function.
This example shows how to classify images from a webcam in real time using the pretrained deep convolutional neural network GoogLeNet.
Interactively fine-tune a pretrained deep learning network to learn a new image classification task.
This example shows how to use transfer learning to retrain a convolutional neural network to classify a new set of images.
This example shows how to extract learned image features from a pretrained convolutional neural network, and use those features to train an image classifier.
This example shows how to fine-tune a pretrained AlexNet convolutional neural network to perform classification on a new collection of images.
This example shows how to create and train a simple convolutional neural network for deep learning classification.
Interactively build and edit deep learning networks.
This example shows how to fit a regression model using convolutional neural networks to predict the angles of rotation of handwritten digits.
Recreate a network created or edited in Deep Network Designer by generating MATLAB code.
This example shows how to create a deep learning neural network with residual connections and train it on CIFAR-10 data.
Learn how to resize images for training, prediction and classification, and how to preprocess images using data augmentation, transformations, and specialized datastores.
Read and preprocess volumetric image and label data for 3-D deep learning.
Learn how to use datastores in deep learning applications.
This example shows how to convert a trained classification network into a regression network.
Learn how to improve the accuracy of deep learning networks.
Classify images from a webcam in real time using the pretrained deep convolutional neural network GoogLeNet.
Use transfer learning to retrain a convolutional neural network to classify a new set of images.
Create a deep learning neural network with residual connections and train it on CIFAR-10 data. Residual connections are a popular element in convolutional neural network architectures. Using residual connections improves gradient flow through the network and enables training of deeper networks.
Prepare a datastore for training an image-to-image regression network using the transform and combine functions of ImageDatastore.