Learn basic Python programming and master practical skills;
Learn basic machine learning and deep learning concepts, and get familiar with deep learning frameworks such as PyTorch;
Learn the basic theory of the graph neural network, understand the graph neural network frameworks like PyG, DGL, or GammaGL;
Implement a graph neural network model on OpenHGNN and GammaGL using related tools.
Interested students can go to our homepage to learn about more graph learning works.
The first week:
Choose an IDEA that is easy to use, recommend Pycharm, VSCode, etc.
Master Python basic project management, project debugging, etc.
Focus on learning the first part of the Python3 tutorial in the catalog, and those who are interested in the second part of the Python3 advanced tutorial can learn about it.
The most important content in the first part: string, list, dict, tuple, conditional control, loop statement, module, object-oriented.
Understand why "everything in Python is an object".
Gradually learn how to bypass the wall (necessary to connect to platforms such as github).
Complete a Python Basic exercise.
If you need related videos, you can search directly on the video website such as YouTube, .etc.
The second week:
Learn how to use conda to manage the Python environment.
You only need to master the simplest environment creation, the management of installing packages, and the installing channel.
Learn to use Juypter Notebook to facilitate code writing.
Learn commonly used libraries for data analysis: numpy, pandas, matplotlib.
It can be learned in combination with Zhihu topics.
Complete a Data processing exercise.
The third week:
Combined with the above courses, you can learn and master the concepts of machine learning and deep learning such as gradient descent, backpropagation, and deep learning fast. You just need to understand and get familiar with related concepts at this stage.
Learn the sklearn library and its related usage.
Complete a Linear regression exercise.
The fourth week:
The video is only for recommendation. It is strongly recommended to follow the Tutorials and Docs of the PyTorch official website to learn. It is necessary to get familiar with the concept of tensor in PyTorch, use PyTorch to build a neural network, and use PyTorch to build a training process.
Complete a PyTorch exercise.
The first week
Learn the basic concepts and the theory of graph neural networks.
Follow the examples, start writing code, and pay attention to identify the library differences between DGL and PyG (Graph-centric vs Tensor-centric).
Complete an Algorithm library Combat.
The second week
Answer the following questions:
- How does the formula mention in GraphConv correspond to the message passing paradigm in the document (Indicate which part of the formula each symbol in the message passing paradigm corresponds to).
- For GATConv and SAGEConv, please make the same corresponding analysis.
- Which line should the formula concat mentioned in SAGEConv be in the source code, and analyze the similarities and differences between the two.
The third week
The fourth week
Understand the back-end framework used by each algorithm library.
Implement a minimal model using the algorithm library.
Led by the responsible students, realize the model and achieve the performance of the paper.
Find out which part of the algorithm libraries are unreasonable. We welcome your valuable suggestion.
If you are interested in this program, please email email@example.com.