| Code | Course offering | AIST4010 | |
| Title | Long Course Title | Foundation of Applied Deep Learning | 應用深度學習基礎 |
| Overview | Long Description | This course covers how to use deep learning techniques to resolve real-life computational problems, handling different kinds of data. We start the course by introducing the problem-solving paradigm with deep learning: data preparation, building the model, training the model, model evaluation, and hyper-parameter searching. Then, we fill in the details in the paradigm. Regarding the deep learning models, we will go from the simplest linear regression model, towards the relatively complicated models. To handle various data types, that is, the structured data, images, text, sequences, signals, and graphs, in our daily life, we would cover CNN/ResNet, RNN/LSTM, Attention, and GNN models. In addition to the above paradigms, we will also cover the commonly used techniques to handle overfitting. We would briefly go through the generative models, VAE, and GAN, at the end of this course. | 本科將詳細介紹如何使用深度學習去處理並解決實際生活中遇到的各種數據類型。本科開始將首先介紹用深度學習去解決問題的流程和框架：數據預處理、構建模型、訓練模型、評估模型及超參數搜索。然後詳細介紹這個流程中的細節。深度模型部分，將從最簡單的線性模型開始介紹並逐漸增加模型的複雜度。為了處理不同的數據類型，即結構化數據、圖像、文本、序列、信號和網絡，本科將介紹CNN/ResNet, RNN/LSTM, Attention和GNN模型。除了上述流程，本科還會詳細介紹如何處理深度學習中的過擬合問題。最後，本科將簡單介紹生成模型：VAE和GAN。 |