Aly El Gamal, Asst. Professor Electrical & Computer Engineering
To provide focused training on deep learning algorithms. The students should be able to acquire a principled understanding of the various techniques that have a proven successful record in solving important engineering problems. Further, hands-on experimental training will be provided through the course projects.
- Undergraduate level knowledge of linear algebra and probability (ECE302 and MA265)
- Note: Purdue has short courses in these topics available as refreshers
Students will learn:
- How to make design choices regarding the construction of deep learning algorithms.
- The history and justification for state-of-the-art deep learning algorithms.
- How to implement, optimize and tune state-of-the-art deep neural network architectures.
- The security aspects of state-of-the-art deep learning algorithms.
- Open research problems in deep learning and proposed approaches in the literature to tackle them.
- Deep Feedforward Networks
- Regularization for Deep Learning
- Optimization for Training Deep Models
- Convolutional Neural Networks
- Recurrent and Recursive Neural Networks
For further information and to register, please email email@example.com.