Welcome to the Advanced Deep Learning Course! This page provides an overview of the course content and objectives. If you are interested in expanding your knowledge on this topic, be sure to check out our Introduction to Deep Learning course.

Course Outline

  • Introduction to Deep Learning (已完成)
  • Neural Networks
    • Types of Neural Networks
    • Backpropagation
  • Convolutional Neural Networks (CNNs)
    • CNN Architecture
    • Applications in Image Recognition
  • Recurrent Neural Networks (RNNs)
    • RNN Architecture
    • Applications in Time Series Analysis
  • Generative Adversarial Networks (GANs)
    • GAN Architecture
    • Applications in Image Generation
  • Advanced Topics
    • Transfer Learning
    • Autoencoders

Learning Objectives

  • Understand the basic concepts of deep learning.
  • Learn how to build and train various types of neural networks.
  • Apply deep learning techniques to real-world problems.
  • Explore advanced topics in deep learning.

Course Materials

  • Textbook: "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
  • Lecture Notes: Available on the course website.
  • Tutorials: Hands-on exercises to practice what you've learned.

Deep Learning

By the end of this course, you will have a solid understanding of advanced deep learning concepts and be able to apply them to solve complex problems. We look forward to seeing you in class!