Noohshiny

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 prediction. LSTM will be used as a generator, and CNN as a discriminator. In addition, Natural Language Processing(NLP) will also be used in this project to analyze the influence of News on stock prices.

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

DeepLearningExamples

Deep Learning Examples

financial-forecasting-challenge-gresearch

My approaches to Financial Forecasting Challenge by G-Research

LLMs-from-scratch

Implementing a ChatGPT-like LLM in PyTorch from scratch, step by step

LSTM-Time-Series-Analysis

Using LSTM network for time series forecasting

sagemaker-distributed-training-workshop

Hands-on workshop for distributed training and hosting on SageMaker

TCN

Sequence modeling benchmarks and temporal convolutional networks

Noohshiny/financial-forecasting-challenge-gresearch

My approaches to Financial Forecasting Challenge by G-Research

Noohshiny/LLMs-from-scratch

Implementing a ChatGPT-like LLM in PyTorch from scratch, step by step

Noohshiny/LSTM-Time-Series-Analysis

Using LSTM network for time series forecasting

Noohshiny/awesome-rl

Reinforcement learning resources curated

Noohshiny/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

Noohshiny/Clustering-of-Customers-Based-on-their-purchasing-behaviour

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

Noohshiny/DeepCTR

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

Noohshiny/DeepLearningExamples

Deep Learning Examples

Noohshiny/hclustvar

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

Noohshiny/hierarchical-clustering-using-r-and-python

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

Noohshiny/implicit

Fast Python Collaborative Filtering for Implicit Feedback Datasets

Noohshiny/sagemaker-distributed-training-workshop

Hands-on workshop for distributed training and hosting on SageMaker

Noohshiny/TCN

Sequence modeling benchmarks and temporal convolutional networks

Noohshiny/FAANG-Type-Interview-Questions

Repository of Interview Questions

Noohshiny/fresh

Fresh shiny themes

Noohshiny/gluonts-hierarchical-ICML-2021

Noohshiny/K-prototypes

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

Noohshiny/LeetCode

Solving LeetCode Problems in Python!:)

Noohshiny/lstm-load-forecasting

Electricity load forecasting with LSTM (Recurrent Neural Network)

Noohshiny/Machine-Learning-for-Solar-Energy-Prediction

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

Noohshiny/mixed-data-clustering

Application of information entropy for mixed data clustering

Noohshiny/models

Models and examples built with TensorFlow

Noohshiny/neural-collaborative-filtering

pytorch version of neural collaborative filtering

Noohshiny/nnet-ts

Neural network architecture for time series forecasting.

Noohshiny/Plots-in-Draw

Noohshiny/prophet_forecasting

forecasting examples using Facebook Prophet

Noohshiny/pyspc

Statistical Process Control Charts Library for Humans

Noohshiny/shap-shapley

Noohshiny/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

Noohshiny/Stock_Price_Predication

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