Join MathWorks engineers for complimentary technical seminars to develop your MATLAB programming skills. Learn optimal workflows and features for increasing your productivity. Two sessions will be offered to address the needs of new and existing users, including:
Data Analysis with MATLAB 11:00 AM – 12:30 PM
In this session, you will learn how MATLAB can be used to visualize and analyze data, perform numerical computations, and develop algorithms. Through live demonstrations and examples, you will see how MATLAB can help you become more effective in your work. You will also learn about some of the newer features in MATLAB in recent years.
- Accessing data from many sources (files, other software, hardware, etc.)
- Using interactive tools for iterative exploration, design, and problem solving
- Automating and capturing your work in easy-to-write scripts and programs
- Sharing your results with others by automatically creating reports
Demystifying Deep Learning 1:00 – 3:00 PM
Are you new to deep learning and want to learn how to use it in your work? 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 the 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, and figure out the drivable area in a city environment.
- Manage extremely large sets of images
- Visualize networks and gain insight into the black box nature of deep networks
- Perform classification and pixel-level semantic segmentation on images
- Import training data sets from networks such as GoogLeNet and ResNet
- Import and use pre-trained models from TensorFlow and Caffe
- Speed up network training with parallel computing on a cluster
- Automate manual effort required to label ground truth
- Automatically convert a model to CUDA to run on GPUs