Deep Learning - Graduate Course (Teaching Assistant)

Served as Teaching Assistant for Prof. Stan Z. Li’s Deep Learning course during Ph.D. studies. This graduate-level course covered fundamental and advanced topics in deep learning, including neural network architectures, optimization methods, convolutional neural networks, recurrent neural networks, attention mechanisms, and generative models.

Responsibilities

• Lecture Support: Assisted in preparing course materials, slides, and coding demonstrations
• Lab Sessions: Led hands-on programming tutorials using PyTorch and TensorFlow
• Student Mentoring: Provided one-on-one guidance for course projects and homework assignments
• Grading: Evaluated assignments, exams, and final projects
• Office Hours: Held weekly office hours to answer student questions on theoretical concepts and implementation details

Topics Covered

• Fundamentals of neural networks and backpropagation
• Optimization algorithms (SGD, Adam, learning rate scheduling)
• Convolutional Neural Networks (CNNs) for computer vision
• Recurrent Neural Networks (RNNs) and LSTMs for sequence modeling
• Attention mechanisms and Transformer architectures
• Generative models (VAEs, GANs, Diffusion Models)
• Transfer learning and fine-tuning
• Applications in computer vision, NLP, and scientific computing

Student Projects

Supervised student projects on diverse applications including medical image analysis, natural language processing, and scientific data visualization. Many projects resulted in publications and competitive achievements in data science competitions.