Deep Learning Projects

Course ECS189G Winter Quarter 2022 at UC Davis. I learned in depth about 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

Purpose of each stages was as follows:
Stage 1: Installation and Gihub Setup.
Stage 2: Build up a Multi-Layer Perceptron model to classify instances in provided dataset.
Stage 3: Object recognition from images with a convolutional neuron network model.
Stage 4: Text classification and generation with recurrent neural network models.
Stage 5: Network embedding and node classification with graph neural network models.

Python was used exclusively with extensive use of packages such as PyTorch, TensorFlow, Pandas, and Numpy. Among others.

To view this project on Github and learn more about the data and purpose of each of the stages, click on the icon below to be directed to the source code.