noohshiny Goto Github PK

awesome-rl

Reinforcement learning resources curated

awesome_imputation

Awesome Deep Learning Resources for Time-Series Imputation, including a must-read paper list about using deep learning neural networks to impute incomplete time series containing NaN missing values/data

clustering-of-customers-based-on-their-purchasing-behaviour

Analyzing the content of an E-commerce dataset and cluster the customers based on their purchases.

deepctr

Easy-to-use,Modular and Extendible package of deep-learning based CTR models.

deeplearningexamples

Deep Learning Examples

financial-forecasting-challenge-gresearch

My approaches to Financial Forecasting Challenge by G-Research

fresh

Fresh shiny themes

gluonts-hierarchical-icml-2021

hclustvar

A package for hierarchical clustering of mixed variables: numeric and/or categorical

hierarchical-clustering-using-r-and-python

Tutorial on how to build hierarchical clustering dendrogram using Python and R

implicit

Fast Python Collaborative Filtering for Implicit Feedback Datasets

k-prototypes

K-prototypes is an un-supervised algorithm for doing clustering on data with both numerical data type and categorical data type.

lstm-load-forecasting

Electricity load forecasting with LSTM (Recurrent Neural Network)

lstm-time-series-analysis

Using LSTM network for time series forecasting

machine-learning-for-solar-energy-prediction

Predict the Power Production of a solar panel farm from Weather Measurements using Machine Learning

mixed-data-clustering

Application of information entropy for mixed data clustering

mle

Maximum likelihood estimation of some common but not straightforward distributions

models

Models and examples built with TensorFlow

mth594_machinelearning

The materials for the course MTH 594 Advanced data mining: theory and applications (Dmitry Efimov, American University of Sharjah)

neural-collaborative-filtering

pytorch version of neural collaborative filtering

nnet-ts

Neural network architecture for time series forecasting.

plots-in-draw

prophet_forecasting

forecasting examples using Facebook Prophet

pyspc

Statistical Process Control Charts Library for Humans

repo-2017

Python codes in Machine Learning, NLP, Deep Learning and Reinforcement Learning

sagemaker-distributed-training-workshop

Hands-on workshop for distributed training and hosting on SageMaker

shap-shapley

stock-price-prediction-using-gan

In this project, we will compare two algorithms for stock prediction. First, we will utilize the Long Short Term Memory(LSTM) network to do the Stock Market Prediction. LSTM is a powerful method that is capable of learning order dependence in sequence prediction problems. Furthermore, we will utilize Generative Adversarial Network(GAN) to make the

stock_price_predication

I have implemented Artificial Neural Network (ANN) model to predict the stock prices and compare it with linear regression.

tcn

Sequence modeling benchmarks and temporal convolutional networks