What's New in Deep Learning

In R2017a, MATLAB® makes it easier and faster for engineers and scientists to learn and apply deep learning to computer vision problems. Configure and train models, visualize their structure, leverage pretrained models for transfer learning, and take advantage of GPU acceleration.

Training and Visualization for Convolutional Neural Networks

You can train deep learning models by using a large set of labeled data and deep neural network architectures that contain many layers. Visualization tools help you to understand the structure of the model and see the features learned.

Pre-Trained Models

Pre-trained models can be used for transfer learning, which is the process of fine-tuning a model to work on a new data set.

GPU Acceleration

GPUs are highly efficient on parallel algorithms such as deep learning. You can achieve higher levels of parallelism by using multiple GPUs or by using GPUs and processors together.

Handling Large Sets of Images

To handle large sets of images, you can create an ImageDatastore, which allows you to read and process multiple image files as a single entity.

Object Detection Frameworks

Object detection and recognition are used to locate, identify, and categorize objects in images and video. You can detect or recognize an object in an image by training an object classifier using deep learning.