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Artifacts and Code Distribution for web publications, machine learning, and deep learning applications.

Computer Vision

• Dogs vs Cats Image Classification (Keras):Given an image of a dog or cat, determine which animal is it using Keras Framework and CNN Architecture

NLP

• Classify News Articles (gensim/sklearn): Original Publication Using gensim, create a word2vec model based on news articles. Using the same model, create a logistic regression model that inputs the word vectors and returns a category or type of news articles.

Audio

• Dogs vs Cats Audio Classification (pytorch): Original Publication Using librosa to generate spectrograms to be fed into CNN model, determine if the audio file is a cat or dog sound.

GAN

• GAN - Dog Images (pytorch): Using the Deep Convolutional Generative Adversarial Network (DCGAN) proposed by Radford, Metz and Chintala, create a GAN model that creates new dog images from existing images.

Classification

• Predict Customer Churn (XGboost): Original Publication Use XGboost algorithm to predict customer churn based on customer demographic and behavior.
• Iris Classification (Keras): Original Publication Using Toy Iris Dataset, classify which Iris flower based on the width and length of stem and petals by using an Artificial Neural Network.
• Iris Classification (XGboost): Train a model using xgboost framework, save the model artifact in s3, and load for inferencing as an endpoint
• Breast Cancer Detection (sklearn): Original Publication Using breast cancer measurements, classify which patient has breast cancer using sklearn framework and AWS SageMaker.
• Stellar Classification (sklearn): Original Publication Using the Gradient-Boosting Classifier algorithm from the sklearn framework, predict the stellar classification based on mreasurements.
• Wine Classification (sklearn): Develop a pipeline to operationalize a machine learning workload. Using toy wine dataset, predict the type of wine among 3 classes.

Regression

• Possum Age Regression (Keras): Original Publication Using possum measurements, predict the age of a possum using Keras Deep Learning framework.
• Iris SepalLength Regresion (SageMaker): Using Iris measurements, predict the size of SepalLengthCm using SepalWidthCm, PetalLengthCm, and PetalWidthCm. Utilizes AWS built-in algorithm, LinearLearner.
• Laptop Prices Regression (pytorch): Using laptop measurements, predict the price of a laptop using pytorch framework.
• Linear Regression (tensorflow): Using synethic data, create a regression model to showcase tensorflow/keras framework.

Clustering

• k-means customer segmentation (sklearn):Original Publication Perform customer segmentation for market analysis using demographic data

Amazon Web Services (AWS)

• AWS Kinesis Anomaly Detection (Kinesis): Original Publication Create a Kinesis Data Stream to receive events from an emitter. Report anomalies based on SQL query and AWS Random Cut Forest Algorithm.
• Breast Cancer Detection (SageMaker): Original Publication Using breast cancer measurements, classify which patient has breast cancer using sklearn framework and AWS SageMaker.
• Iris SepalLength Regresion (SageMaker): Using Iris measurements, predict the size of SepalLengthCm using SepalWidthCm, PetalLengthCm, and PetalWidthCm. Utilizes AWS built-in algorithm, LinearLearner.
• Deploy a model Artifact (SageMaker): Train a model using xgboost framework, save the model artifact in s3, and load for inferencing as an endpoint

MLOps

• Classification Pipelines (sklearn): Original Publication Develop a pipeline to operationalize a machine learning workload. Using toy wine dataset, predict the type of wine among 3 classes.