Neural Networks and Deep Learning
Instructor: Hua Meng and Yu Wang
Course Description:
This course covers fundamental concepts of machine learning, feedforward neural networks, convolutional neural networks, recurrent neural networks, network optimization and regularization, as well as attention mechanisms and external memory. Designed for mathematics majors, the course emphasizes both theoretical foundations and practical applications.
Main Topics
- Fundamental concepts of machine learning
- Feedforward neural networks
- Convolutional neural networks (CNN) and their applications
- Recurrent neural networks (RNN) and their variants
- Network optimization and regularization
- Attention mechanisms and external memory
- Transformer
- Applications of deep learning in image, speech, and natural language processing
References
- Xipeng Qiu. Neural Networks and Deep Learning. China Machine Press, 2020. (in Chinese)
Assessment
- Homework and experiments
- Final project or exam
If you have any questions, please contact: [email protected]