Are you new to deep learning and want to learn how to use it in your work? An Application Engineer from the MathWorks will be on campus to demonstrate new MATLAB features that simplify this task.
Deep learning can achieve state-of-the-art accuracy in many humanlike tasks such as naming objects in a scene or recognizing optimal paths in an environment.
The main tasks are to assemble large data sets, create a neural network, to train, visualize, and evaluate different models, using specialized hardware – often requiring unique programming knowledge. These tasks are frequently even more challenging because of the complex theory behind them.
In this seminar, we’ll demonstrate new MATLAB features that simplify these tasks and eliminate low-level programming. In doing so, we’ll decipher practical knowledge of the domain of deep learning. We’ll build and train neural networks that recognize handwriting, classify food in a scene, classify signals, and figure out the drivable area in a city environment.
Along the way, you’ll see MATLAB features that make it easy to:
- Manage large sets of images
- Create, analyze, and visualize networks and gain insight into the black box nature of deep networks
- Build networks from scratch with a drag-and-drop interface
- Perform classification tasks on images and signals, and pixel-level semantic segmentation on images
- Import training data sets from networks such as GoogLeNet and ResNet
- Import models from TensorFlow Keras, Caffe, and the ONNX Model format
- Speed up network training with parallel computing on a cluster
- Automate manual effort required to label ground truth
- Automatically generate source code for embedded targets
This event was first published on April 18, 2019 and last updated on April 24, 2019.