Repository for storing code for my MS in Data Science course CS672 Deep Learning at Pace University.
Course description: This course introduces the students to Machine Learning and Deep Learning Technologies, Data Analytics at scale, and Data-driven Science systems in order to extract insights data from in various forms. These scientific processes will include various phases and techniques such as Data Preparation, Model Building, and Prediction, Clustering, Association, Regression (Linear and Logistic), Classification, Decision Trees, Textual Data Analysis and Data Presentation. The course is designed to make you proficient in training and evaluating Deep Learning based models. The basic concepts will be covered with examples which can be tried Python by using PyCharm and/or Jupyter Notebooks (aka IPython Notebooks). These miniaturized examples of real-world problems are designed in such way that the student will gain a clear understanding and get firm foundation of the methods covered in the course. In addition, the course gives an introduction to TensorFlow Deep Learning platform, Keras, the Python Deep Learning API, and PyTorch Deep Learning Framework.
The aim of this project was to perform an EDA on the UCI Wine dataset and build a deep learning model with Tensorflow and Keras to classify the different classes of wine.
The aim of this project was to perform an EDA on the UCI Breast Cancer dataset and build a deep learning model with PyTorch to classify tumors as either benign or malignant.
TBD.