With this speech, we will go through practical examples such as classifying human activity or naming objects in a scene, to highlight the potential of Machine/Deep Learning with MATLAB. Specifically, we will explore the fundamentals workflow steps, i.e.:
- Access, explore, structure and visualize large sets of data/images
- Perform Machine Learning tasks
- Clustering for unsupervised learning
- Feature selection and feature transformation
- Train, validate and tune classification models with the Classification Learner App
- Perform Deep Learning tasks
- Import training data sets from available neural networks
- Import, use and manipulate pre-trained models from other frameworks, like TensorFlow, Keras and Caffe
- Create, analyze, and visualize networks and gain insight into the black box nature of deep networks
- Perform classification tasks