Description:

This course explores research being done in machine learning and deep learning. Topics covered in its 12 videos include neural networks and deep neural networks. First, learners examine how to prevent neural networks from overfitting. You will explore research on multilabel learning algorithms, multilabel classification, and multiple-output classifications, which are variants of the standard classification problem. Then examine deep learning algorithms, and how the depth of neural networks is key to performance since deeper neural networks have been shown to be more adept at automatic feature extraction. Next, learners will examine research involved in transferable features in deep neural networks, and research associated with large-scale video classification. You will review research related to common objects in context, and generative adversarial networks. You will learn about research on facial alignment, and ensemble of regression trees, and about deep features for scene recognition. Finally, you will look at research proposing Extreme Learning Machine (ELM), which is used for regression and multiclass classification.

Target Audience:

Duration: 00:42