log anomaly detection toolkit including DeepLog

Pytorch Implementation of DeepLog.

FloydHub porting of Pytorch time-sequence-prediction example

Four digit SVHN (Street View House Number) sequence prediction with CNN using Keras with TensorFlow backend

Temporal Convolutional Network for Sequence Modelling

An Implementation of the Context Tree Weighting (CTW) Sequence Prediction Algorithm

Simple implementation of Hidden Markov Model for discrete outcomes/observations in Python. It contains implementation of 1. Forward algorithm 2. Viterbi Algorithm and 3. Forward/Backward i.e. Baum-Welch Algorithm.

biLSTM model with the attention mechanism. Example of prediction/inferencing included.

Predict next number in a sequence using a simple ANN. Modularized code with classes for data preparation, neural network architecture, and training.

Rock Paper Scissors using Discrete Markov Chains : The program calculates the probability of the opponent picking one of the three states (R/ P/ S) from choices made by the opponent during the previous games.

Project using Compact Prediction Tree Algorithm. Based on the paper "Compact Prediction Tree : A lossless model for accurate sequence prediction"

Prediction of the binding specificity of transcription factors using support vector regression

Contains code for building a simple lstm model to predict hourly Beijing air quality data.

Opportunistic planning model to generate action sequence predictions for human behavior in everyday activities

Neural Networks for learning with structured data types

🌟 Scott Miner's GitHub portfolio showcasing personal projects, coding skills, and expertise in Software Development/Data Analytics/AI/ML. Get in touch for collaboration!

A Product Sequence Predictor and Recommender Application made as a part of the Machine Learning Lab Course in the curriculum of B. Tech. Data Science & Engineering at Manipal Institute of Technology.

The repository includes implementations of quaternion networks and new QALE loss function, which calculates the error value based on the difference in angles between the result and the expected value. Procedures for performing the training and evaluation of predicting successive elements of a rotation sequence are also provided.