What Is Deep Learning?

Deep learningis a branch of machine learning that teaches computers to do what comes naturally to humans: learn from experience. Machine learning algorithms use computational methods to “learn” information directly from data without relying on a predetermined equation as a model. Deep learning is especially suited for image recognition, which is important for solving problems such as facial recognition, motion detection, and many advanced driver assistance technologies such as autonomous driving, lane detection, pedestrian detection, and autonomous parking.

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, seeDeep Learning Onramp.

To learn more about deep learning application areas, including automated driving, seeDeep Learning Applications.

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

For more information, seeChoose Network Architecture.

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.

You train models using a large set of labeled data and neural network architectures that contain many layers, usually including some convolutional layers. Training these models is computationally intensive and you can usually accelerate training by using a high performance GPU. This diagram shows how convolutional neural networks combine layers that automatically learn features from many images to classify new images.

许多深度学习应用程序使用图像文件,nd sometimes millions of image files. To access many image files for deep learning efficiently, MATLAB provides theimageDatastorefunction. Use this function to:

Try Deep Learning in 10 Lines ofMATLABCode

This example shows how to use deep learning to identify objects on a live webcam using only 10 lines of MATLAB code. Try the example to see how simple it is to get started with deep learning in MATLAB.

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 example, if you take a network trained on thousands or millions of images, you can retrain it for new object detection using only hundreds of images. You can effectively fine-tune a pretrained network with much smaller data sets than the original training data. If you have a very large dataset, then transfer learning might not be faster than training a new network.

Transfer learning enables you to:

For an interactive example, seeTransfer Learning with Deep Network Designer.

一个编程的例子,看到Train Deep Learning Network to Classify New Images.

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 usingalexnetcan 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, seeExtract Image Features Using Pretrained Network.

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

Neural networks are inherently parallel algorithms. You can take advantage of this parallelism by using Parallel Computing Toolbox™ to distribute training across multicore CPUs, graphical processing units (GPUs), and clusters of computers with multiple CPUs and GPUs.

Training deep networks is extremely computationally intensive and you can usually accelerate training by using a high performance GPU. If you do not have a suitable GPU, you can train on one or more CPU cores instead. You can train a convolutional neural network on a single GPU or CPU, or on multiple GPUs or CPU cores, or in parallel on a cluster. Using GPU or parallel options requires Parallel Computing Toolbox.

You do not need multiple computers to solve problems using data sets too large to fit in memory. You can use theimageDatastorefunction to work with batches of data without needing a cluster of machines. However, if you have a cluster available, it can be helpful to take your code to the data repository rather than moving large amounts of data around.

To learn more about deep learning with large data sets, seeDeep Learning with Big Data.