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UBDA USEr 3

A. HPC Service

Python examples for ML/DL

Category

Example

Techniques

Readme for HPC 

JupyterNoteBook

Deep Learning

Deep learning using cotton images

Deep learning, tensorflow, sequential, CNN, binary labels

 Readme

Notebook

Deep learning using ball images

Deep learning, tensorflow,sequential, CNN, multi-labels

 Readme

Notebook

Deep learning using chest xray images

TensorFlow, binary labels, prediction (neural network)

 Readme  

Deep learning using traffic sign images

TensorFlow, multi-labels, prediction (neural network)

 Readme  

Classification

Classification analysis of student information

Linear Regression, Decision Trees, Logistic Regression, Random Forest

Neural Networks, Support Vector Machines

 Readme Notebook

Classification analysis for flower images

Logistic Regression, Linear Discriminant Analysis, K-Nearest Neighbors, Decision Trees,

Random Forests, Gaussian Naive Bayes and Support Vector Machine

 Readme Notebook

Clustering

Clustering analysis using K-means

k-means

 Readme Notebook

Text Analysis

Text analysis using nltk for sentiment prediction

CountVectorizer, Tfidf, Sentiment Prediction (naive_bayes)

 Readme Notebook

Convex Optimization

Convex optimization using CVXPPY for portfolio optimization

Portfolio optimization, Linear Programming

 Readme Notebook

Convex optimization using CVXOPT for floor planning

Floor planning, Linear Programming

 Readme 

Notebook

Demo Video

Basic operation in ubdaplatform (Student / Staff)

B. Virtual Machine Service

C. JupyterHub Service

RAPIDS Examples

JupyterNotebook:

  1. BlazingSQL2
  2. cuDF1
  3. cuML2
  4. cuPy5
  5. cuSignal4

Demo Video

Basic operation in ubdalab (Student / Staff)