Course Identification

• Experience in programming in Python
• Basic knowledge of machine learning

This is a hands on course that will cover many aspects of deep neural networks, including data preprocessing and normalization, feature generation, auto-encoding, network architectures, recurrent neural networks, activation functions, network training and testing, and visualization of the training process. The course will be taught through a single prediction task that all students will work on in an attempt to improve prediction accuracy, while integrating all of the above aspects of neural networks.

Students will gain considerable hands on experience in working with and optimizing deep neural networks within a large-scale dataset and a complex prediction task. Upon finishing the course they are expected to be versed in many different aspects of deep neural networks, from data processing through network architectures and training and network optimization procedures