Cryptocurrency Prediction with Artificial Intelligence (Deep Learning via LSTM Neural Networks)

Related tags

Deep Learningdeep-neural-networksdeep-learningneural-networkpredictionartificial-intelligencecryptocurrencylstmneural-networkslstm-modelartificial-neural-networksdeeplearningxrplstm-neural-networksusd
Overview

Cryptocurrency-Prediction-with-Artificial-Intelligence

I developed Cryptocurrency Prediction (Deep Learning with LSTM Neural Networks) software with Artificial Intelligence. I predicted the fall on December 28, 2021 with 98.5% accuracy in the XRP/USDT pair. '0.009179626158151918' MAE Score, '0.0002120391943355104' MSE Score, 98.35% Accuracy Question software has been completed.

The XRP/USDT pair forecast for December 28, 2021 was correctly forecasted based on data from Binance.

Software codes and information are shared with you as open source code free of charge on GitHub and My Personal Web Address.

Happy learning!

Emirhan BULUT

Senior Artificial Intelligence Engineer and Inventor

###The coding language used:

Python 3.9.8

###Libraries Used:

Tensorflow - Keras

NumPy

Matplotlib

Pandas

Scikit-learn (SKLEARN)

Cryptocurrency Prediction with Artificial Intelligence (Deep Learning via LSTM Neural Networks)- Emirhan BULUT

Developer Information:

Name-Surname: Emirhan BULUT

Contact (Email) : [email protected]

LinkedIn : https://www.linkedin.com/in/artificialintelligencebulut/

Kaggle: https://www.kaggle.com/emirhanai

Official Website: https://www.emirhanbulut.com.tr

Owner
Emirhan BULUT
Artificial Intelligence Engineer • Machine Learning and Deep Learning Inventor
Emirhan BULUT
GitHub Repository
Pytorch implement of 'Unmixing based PAN guided fusion network for hyperspectral imagery'

Pgnet There's a improved version compared with the publication in Tgrs with the modification in the deduction of the PDIN block: https://arxiv.org/abs

5 Jul 01, 2022
Learning to trade under the reinforcement learning framework

Trading Using Q-Learning In this project, I will present an adaptive learning model to trade a single stock under the reinforcement learning framework

Uirá Caiado 470 Nov 28, 2022
DA2Lite is an automated model compression toolkit for PyTorch.

DA2Lite (Deep Architecture to Lite) is a toolkit to compress and accelerate deep network models. ⭐ Star us on GitHub — it helps!! Frameworks & Librari

Sinhan Kang 7 Mar 22, 2022
R-package accompanying the paper "Dynamic Factor Model for Functional Time Series: Identification, Estimation, and Prediction"

dffm The goal of dffm is to provide functionality to apply the methods developed in the paper “Dynamic Factor Model for Functional Time Series: Identi

Sven Otto 3 Dec 09, 2022
Space-invaders - Simple Game created using Python & PyGame, as my Beginner Python Project

Space Invaders This is a simple SPACE INVADER game create using PYGAME whihc hav

Gaurav Pandey 2 Jan 08, 2022
This is the unofficial code of Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes. which achieve state-of-the-art trade-off between accuracy and speed on cityscapes and camvid, without using inference acceleration and extra data

Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes Introduction This is the unofficial code of Deep Dual-re

chenjun 113 Dec 23, 2022
Implementation of Uniformer, a simple attention and 3d convolutional net that achieved SOTA in a number of video classification tasks

Uniformer - Pytorch Implementation of Uniformer, a simple attention and 3d convolutional net that achieved SOTA in a number of video classification ta

Phil Wang 90 Nov 24, 2022
A PyTorch Implementation of Single Shot MultiBox Detector

SSD: Single Shot MultiBox Object Detector, in PyTorch A PyTorch implementation of Single Shot MultiBox Detector from the 2016 paper by Wei Liu, Dragom

Max deGroot 4.8k Jan 07, 2023
Fermi Problems: A New Reasoning Challenge for AI

Fermi Problems: A New Reasoning Challenge for AI Fermi Problems are questions whose answer is a number that can only be reasonably estimated as a prec

AI2 15 May 28, 2022
Deep Learning with PyTorch made easy 🚀 !

Deep Learning with PyTorch made easy 🚀 ! Carefree? carefree-learn aims to provide CAREFREE usages for both users and developers. It also provides a c

381 Dec 22, 2022
Code from PropMix, accepted at BMVC'21

PropMix: Hard Sample Filtering and Proportional MixUp for Learning with Noisy Labels This repository is the official implementation of Hard Sample Fil

6 Dec 21, 2022
sssegmentation is a general framework for our research on strongly supervised semantic segmentation.

sssegmentation is a general framework for our research on strongly supervised semantic segmentation.

445 Jan 02, 2023
SCAN: Learning to Classify Images without Labels, incl. SimCLR. [ECCV 2020]

Learning to Classify Images without Labels This repo contains the Pytorch implementation of our paper: SCAN: Learning to Classify Images without Label

Wouter Van Gansbeke 1.1k Dec 30, 2022
[ICML 2021] DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning | 斗地主AI

[ICML 2021] DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning DouZero is a reinforcement learning framework for DouDizhu (斗地主), t

Kwai Inc. 3.1k Jan 04, 2023
Signals-backend - A suite of card games written in Python

Card game A suite of card games written in the Python language. Features coming

1 Feb 15, 2022
3D Human Pose Machines with Self-supervised Learning

3D Human Pose Machines with Self-supervised Learning Keze Wang, Liang Lin, Chenhan Jiang, Chen Qian, and Pengxu Wei, “3D Human Pose Machines with Self

Chenhan Jiang 398 Dec 20, 2022
Code for KDD'20 "Generative Pre-Training of Graph Neural Networks"

GPT-GNN: Generative Pre-Training of Graph Neural Networks GPT-GNN is a pre-training framework to initialize GNNs by generative pre-training. It can be

Ziniu Hu 346 Dec 19, 2022
Image super-resolution (SR) is a fast-moving field with novel architectures attracting the spotlight

Revisiting RCAN: Improved Training for Image Super-Resolution Introduction Image super-resolution (SR) is a fast-moving field with novel architectures

Zudi Lin 76 Dec 01, 2022
Aquarius - Enabling Fast, Scalable, Data-Driven Virtual Network Functions

Aquarius Aquarius - Enabling Fast, Scalable, Data-Driven Virtual Network Functions NOTE: We are currently going through the open-source process requir

Zhiyuan YAO 0 Jun 02, 2022
A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization

MADGRAD Optimization Method A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization pip install madgrad Try it out! A best

Meta Research 774 Dec 31, 2022