It is an open project for students. After some research, my teammates and I decide to work on a house price prediction task, whose target price is either “low”, “medium”, or “high”. Beside using the basic features that are available intuitively, we spend lots of time in visualizing geographical features with given longitude and latitude.
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This assignment is an extended competition from Hw3. I basically need to use some more complicated model to further improve my model performance. For both helpfulness and rating prediction tasks, I decide to use Matrix Factorization (MF) model with Alternating Least Square (ALS) learning approach. Initially, I use only a predicator and biased term for
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In this homework, I need to work on five thousand reviews and train a proper model using those data. With my supervised learning experiment, using one thousand most frequent unigrams and bigrams can effectively reduce the MRE score to 0.289548. Besides that, with Term Frequency – Inverse Document Frequency (tf-idf) features, I can further reduce
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There are two tasks need to be fulfilled in this homework. Firstly, helpfulness prediction is a supervised learning task, where target value is a boolean value. One can tell that the more predictors are used, the better the model performance is. Secondly, rating prediction is another supervised learning task with ratings ranging from one star
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There are three main tasks in this homework. Firstly, I need to train multiple models with different regularization coefficients and report their results. Secondly, results for precision and recall with different thresholds are required. At the end, explained variance with various number of components from Principle Component Analysis is also required. Experiment results and codes
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In this homework, I am asked to perform learning tasks with different sets of features and report their corresponding coefficients and Mean Squared Error (MSE). Experiment results and codes are attached in the report.
In this project, we are interested in one of the hottest topics related to deep learning recently: conditional Generative Adversarial Network (cGAN), and try to add some stochasticity of it. Since it is a very recent work, we spend much time in collecting related information and trying to fully understand those papers. Later, we take
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In this final homework, we are going to use recurrent network to learn and create a song from scratch. There are some hyper parameters that we have turned for a while, including the temperature of the deep learning model, number of neurons in hidden layer, dropout rate, and different optimizers. For model training strategy, one
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In this homework, we are given some pre-trained deep learning models, such as VGG16, to work on other image classification tasks, including Caltech256 and Urban Tribes. To prevent train the whole hidden neurons and links in VGG16 again, we have a surgery on VGG16: replace the last neuron layer with brand new neurons and train
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For the individual written part, I am asked to derive the back-propagation update formulas for multiple single variables and as a matrix. For the team programming part, we are required to implement a neural network in Python from scratch and work on a learning problem to verify our derived update formulas. Experiment results and codes
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