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1. Aerial_Cactus_Detection_Using_CNN Aerial_Cactus_Detection_Using_CNN Public

   A CNN model was built using TensorFlow to predict whether a low-resolution image contains a cactus.

   Python

2. Multiclass_Cuisine_Detection_Using_RNN Multiclass_Cuisine_Detection_Using_RNN Public

   A RNN model was built using TensorFlow to detect a cuisine type from 20 classes based on the ingredients of recipes from the JSON-formatted dataset.

   Python 1

3. Stock_Price_Movement_Prediction_RNN_CNN_FFNN Stock_Price_Movement_Prediction_RNN_CNN_FFNN Public

   The random forest, FFNN, CNN and RNN models are developed to predict the movement of future trading price of Netflix (NFLX) stock using transaction data from the Limit Order Book (LOB).

   Jupyter Notebook 59 18

4. Prediction_of_Patients_Deterioration_in_Cardiac_Wards Prediction_of_Patients_Deterioration_in_Cardiac_Wards Public

   A super learner was built by stacking logistic regression, random forest, and gradient boosting models (XGBoost) to predict whether a patient in the cardiac wards needs to be transferred to ICU.

   Jupyter Notebook 2 4

5. Data_Visualization_for_Features_Comparison_Using_D3.js Data_Visualization_for_Features_Comparison_Using_D3.js Public

   The HTML file is developed using the JavaScript library D3.js to dynamically show and compare the features between different groups of patients

   HTML

6. Digit_Recognizer_With_MNIST_Data_Using_FFNN Digit_Recognizer_With_MNIST_Data_Using_FFNN Public

   A feed forward neural network (FFNN) is built to recognize the gray-scale images of hand-drawn digits from zero through nine using tensorflow.

   Python