Date: April 17, 2024 (Wednesday)
Time: 5:30pm – 7:30 pm
Venue: Digital Learning Studio, Tam Wing Fan Innovation Wing One, HKU
Do you want to know how ChatGPT works?
Interested in how Google Translate operates?
Many modern technologies, such as ChatGPT, Google Translate, AutoCorrect, and email filtering, are all built on top of sequence models! ᕦ(ò_óˇ)ᕤ
In this workshop series, we will introduce you to the theory and applications of sequence models. By learning how to use powerful machine learning models, you will eventually be able to create your own language models. (=´∀｀)人(´∀｀=)
In this first workshop, two basic sequence models: RNNs and LSTMs will be covered and you will have the skills to do language modelling, prediction problems, automated translations, and much more! (p_-)
Date: April 17, 2024 (Wednesday)
Time: 5:30pm- 17:00pm
Venue: Digital Learning Studio, Tam Wing Fan Innovation Wing One, HKU
Class size: A limited quota of 18 will be offered on first-come-first-served basis. Prior knowledge on Python programming is a MUST.
Eligibility: Priority to members of Innovation Wing! All HKU students are welcome to join.
Instructor: AI student research assistants of Innovation Wing
| Session | Duration | Details |
|---|---|---|
|
Introduction and Recap of Machine Learning Basics |
10 minutes |
1. Workshop Overview and Expectations
|
|
Introduction to Sequence Models |
3 minutes |
1. Introduction to common sequence models 2. Differences between the models 3. Applications of sequence models |
|
General Overview of Recurrent Neural Networks (RNNs) |
12 minutes |
1. Introduction to RNNs 2. General applications of RNNs 3. Limitations of RNNs |
|
Coding: Character-based RNNs |
35 minutes |
1. Implementation of a basic RNN using Python 2. Preparation of dataset for training |
|
Break |
5 minutes |
|
|
Coding Practice (using Character-based RNNs) |
15 minutes |
1. Workshop attendees prepare and use a custom dataset on the model |
|
Introduction to Long Short-Term Memory (LSTMs) |
10 minutes |
1. Introduction to LSTMs (an improved version of RNNs with longer memory) |
|
Coding: LSTMs |
25 minutes |
1. Implementation of an LSTM for sentiment analysis on Python 2. Usage of predefined functions in libraries like Datasets and PyTorch |
|
Wrap-up and Q&A |
5 minutes |
1. Workshop wrap-up 2. Q&A Session |
