stradaa/ecs189g-deep-learning-project

ECS189G-Deep Learning

ECS189G is a Deep Learning upper division course we covered important topics such as:

• basic math, optimization, machine learning background knowledge
• neural network basics, error backpropagation algorithm
• auto-encoder model for data encoding and re-construction
• convolutional neural network (CNN) for computer vision
• recurrent neural network (RNN) for natural language processing
• graph neural network (GNN) for network embeddings
• graph-bert and GResNet

There were individual assignments and a group-level programming project (this project)

• Stage 1: Group formation and programming environment setup. (0%)
• Stage 2: First trial with PyTorch and build up a Multi-Layer Perceptron model to classify the instances in a provided dataset. (10%) https://docs.google.com/document/d/1xjyygpgekKi8BO9eUQqEYtdkE4ZGJWCp1JQutSmRaFw/edit?usp=sharing
• Stage 3: Object recognition from images with convolutional neural network model. (10%) https://docs.google.com/document/d/1nHUS4pkn8qVrbl28674RqYw6IB8qu5Mjn-V_TUEWqtE/edit?usp=sharing
• Stage 4: Text classification and generation with recurrent neural network models. (10%) https://docs.google.com/document/d/1Je5nkTF0nnyeTxb9uioU3LCYznZdbszN4Gjfdos1my0/edit?usp=sharing
• Stage 5: Network embedding and node classification with graph neural network models. (10%) https://docs.google.com/document/d/14BZSKBDUAZYrpaQgEhEsO2MBfe1Ey1oqa02WsmNk17A/edit?usp=sharing