Deep Learning with Images
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 create models for new tasks without defining and training a new network, having millions of images, or having a powerful GPU. You can also create new deep networks for image classification and regression tasks by defining the network architecture and training the network from scratch.
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 supported GPU device (for information
on supported devices, see GPU Computing Requirements (Parallel Computing Toolbox)).
Specify the execution environment using the
- Pretrained Networks for Images
Use pretrained networks to quickly learn new tasks
- Deep Networks for Images
Create deep neural networks and train from scratch
- Deep Network Customization for Images
Customize deep learning training loops and loss functions
- Data Preprocessing for Images
Manage and preprocess data for deep learning