Deep Learning

Deep Learning In this chapter, we will learn about deep learning from the ground up using Professor HUNG-YI LEE's 2023 Machine Learning course materials (opens in a new tab) and frontier research papers.

We will cover topics such as convolutional networks, recurrent networks, and their applications in different fields. We will also discuss state-of-the-art research, such as LLM (Large Language Model) alignment and security.

You are expected to have basic knowledge of Python, calculus, and probability.

Note: This section is maintained by An-Che Liang (namwoam@gmail.com). Feel free to contact me with suggestions.

Some useful resources on this topic:

1. Paper with code (opens in a new tab) - highlights trending machine learning research and the code to implement it.
2. Huggingface (opens in a new tab) - a platform that allows users to share machine learning models and datasets.
3. arxiv (opens in a new tab) - a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics and computer science.
4. NeurIPS (opens in a new tab), ICLR (opens in a new tab), ICML (opens in a new tab), CVPR (opens in a new tab), EMNLP (opens in a new tab) - top research conference in the field of machine learning, computer learning and natural language processing.