Machine Learning and Me
My name is Xiangyu, and I am a machine learning enthusiast. I started studying machine learning since 2016, and started to seriously engage in this topic since 2018.
Please visit my LinkedIn Page for contact information.
Here are some of the projects I have done. Please click on the links to go to detailed project pages.
- Image Captioning: a project that uses RNN and CNN models to generate novel captions for an image.
- Unlike traditional image captioning techinques, we use a model where the RNN and CNN are concatenated together to form a language model. The output of this language model is fed into another netword which generates the next word in the caption.
- Word Embedding: GloVe embedding is used for word representation
- Transfer Learning: VGG16 is used to generate high-level image features and is concatenated with RNN output
- Caption Generation: Beam Search is used to generate high probability outputs
- Metric: BLEU and ROUGE scores are used to measure performance
- Automatic Colorization: a project that uses CNN and autoencoders to colorize a gray-scale image.
- We use an autoencoder to colorize a black-and-white phote. The novel aspect is that high level features of the image is extracted from a previously trained deep CNN and injected into the input of the decoder.
- Color Spaces: CIE LAB color space is chosen for its better approximation of human vision
- Transfer Learning: VGG16 is used to extract high level features which are concatenated with mid-level features extracted by the encoder
- Model Merging: VGG16 and encoder is merged before feeding into decoder
- Tweet Classification using BERT: a project that uses the BERT word representation to identify useful tweets and sort them into different categories during a natural disaster or a mass casualty event.
- Pytorch Model: Use bidirectional LSTM stack on top of a BERT representation to create the classification model
- Baseline: A bidirectional LSTM using GloVE Twitter embedding as input and a fine-tuned BERT sequence classification model serves as baselines for comparison
- Training: Model is trained with various "Patience" and learning rate decay schedules.
- Metric: Model is compared using Matthews Correlation Coefficient
- Emotion Detection: a tutorial on using transfer learning to detect facial emotions in an image.
- We illustrate the use of transfer learning to use previously trained CNN on new applications.
- Feature Engineering: a tutorial introducing feature engineering and data exploration
- We illustrate the most basic and the most important part of machine learning, data exploration and feature engineering.