What Is Deep Learning?

Deep learning is a branch of machine learning that teaches computers to do what comes naturally to humans: learn from experience. Deep learning uses neural networks to learn useful representations of features directly from data. Neural networks combine multiple nonlinear processing layers, using simple elements operating in parallel and inspired by biological nervous systems. Deep learning models can achieve state-of-the-art accuracy in object classification, sometimes exceeding human-level performance.

Deep Learning Toolbox™ provides simple MATLAB^® commands for creating and interconnecting the layers of a deep neural network. Examples and pretrained networks make it easy to use MATLAB for deep learning, even without knowledge of advanced computer vision algorithms or neural networks.

For a free hands-on introduction to practical deep learning methods, see Deep Learning Onramp. To quickly get started deep learning, see Try Deep Learning in 10 Lines of MATLAB Code.

Start Deep Learning Faster Using Transfer Learning

Transfer learning is commonly used in deep learning applications. You can take a pretrained network and use it as a starting point to learn a new task. Fine-tuning a network with transfer learning is much faster and easier than training from scratch. You can quickly make the network learn a new task using a smaller number of training images. The advantage of transfer learning is that the pretrained network has already learned a rich set of features that can be applied to a wide range of other similar tasks. For an interactive example, see Transfer Learning with Deep Network Designer. For a programmatic example, see Train Deep Learning Network to Classify New Images.

To choose whether to use a pretrained network or create a new deep network, consider the scenarios in this table.

To explore a selection of pretrained networks, use Deep Network Designer.

Deep Learning Workflows

To learn more about deep learning application areas, see Applications.

Deep Learning Apps

Process data, visualize and train networks, track experiments, and quantize networks interactively using apps.

You can process your data before training using apps to label ground truth data. For more information on choosing a labeling app, see Choose an App to Label Ground Truth Data.

Train Classifiers Using Features Extracted from Pretrained Networks

Feature extraction allows you to use the power of pretrained networks without investing time and effort into training. Feature extraction can be the fastest way to use deep learning. You extract learned features from a pretrained network, and use those features to train a classifier, for example, a support vector machine (SVM — requires Statistics and Machine Learning Toolbox™). For example, if an SVM trained using alexnet can achieve >90% accuracy on your training and validation set, then fine-tuning with transfer learning might not be worth the effort to gain some extra accuracy. If you perform fine-tuning on a small dataset, then you also risk overfitting. If the SVM cannot achieve good enough accuracy for your application, then fine-tuning is worth the effort to seek higher accuracy.

For an example, see Extract Image Features Using Pretrained Network.

Deep Learning with Big Data on CPUs, GPUs, in Parallel, and on the Cloud

Training deep networks is computationally intensive and can take many hours of computing time; however, neural networks are inherently parallel algorithms. You can use Parallel Computing Toolbox™ to take advantage of this parallelism by running in parallel using high-performance GPUs and computer clusters. To learn more about deep learning in parallel, in the cloud, or using a GPU, see Scale Up Deep Learning in Parallel, on GPUs, and in the Cloud.

Datastores in MATLAB^® are a convenient way of working with and representing collections of data that are too large to fit in memory at one time. To learn more about deep learning with large data sets, see Deep Learning with Big Data.

Deep Learning Using Simulink

Implement deep learning functionality in Simulink^® models by using blocks from the Deep Neural Networks block library, included in the Deep Learning Toolbox™, or by using the Deep Learning Object Detector block from the Analysis & Enhancement block library included in the Computer Vision Toolbox™.

For more information, see Deep Learning with Simulink.

Deep Learning Interpretability

Deep learning networks are often described as "black boxes" because the reason that a network makes a certain decision is not always obvious. You can use interpretability techniques to translate network behavior into output that a person can interpret. This interpretable output can then answer questions about the predictions of a network.

The Deep Learning Toolbox provides several deep learning visualization methods to help you investigate and understand network behavior. For example, gradCAM, occlusionSensitivity, and imageLIME. For more information, see Deep Learning Visualization Methods.

Deep Learning Customization

You can train and customize a deep learning model in various ways. For example, you can build a network using built-in layers or define custom layers. You can then train your network using the built-in training functions trainnet and trainNetwork, or define a deep learning model as a function and use a custom training loop. For help deciding which method to use, consult the following table.

For more information, see Train Deep Learning Model in MATLAB.

Deep Learning Import and Export

You can import neural networks and layer graphs from TensorFlow™ 2, TensorFlow-Keras, PyTorch^®, and the ONNX™ (Open Neural Network Exchange) model format. You can also export Deep Learning Toolbox neural networks and layer graphs to TensorFlow 2 and the ONNX model format.

The importNetworkFromTensorFlow, importNetworkFromPyTorch, and importNetworkFromONNX functions create automatically generated custom layers when you import a model with TensorFlow layers, PyTorch layers, or ONNX operators that the functions cannot convert to built-in MATLAB layers. The functions save the automatically generated custom layers to a package in the current folder. For more information, see Autogenerated Custom Layers.

The exportNetworkToTensorFlow function saves a Deep Learning Toolbox neural network or layer graph as a TensorFlow model in a Python package. For more information on how to load the exported model and save it in a standard TensorFlow format, see Load Exported TensorFlow Model and Save Exported TensorFlow Model in Standard Format.

By using ONNX as an intermediate format, you can interoperate with other deep learning frameworks that support ONNX model export or import.