There are 10 repositories under rnn-lstm topic.
Collection of must read papers for Data Science, or Machine Learning / Deep Learning Engineer
Implementation of Logistic Regression, MLP, CNN, RNN & LSTM from scratch in python. Training of deep learning models for image classification, object detection, and sequence processing (including transformers implementation) in TensorFlow.
RNN architectures trained with Backpropagation and Reservoir Computing (RC) methods for forecasting high-dimensional chaotic dynamical systems.
This project provides implementations with Keras/Tensorflow of some deep learning algorithms for Multivariate Time Series Forecasting: Transformers, Recurrent neural networks (LSTM and GRU), Convolutional neural networks, Multi-layer perceptron
Time Series Analysis of Air Pollutants(PM2.5) using LSTM model
This project is about performing Speaker diarization for Hindi Language.
Deep Learning notes and practical implementation with Tensorflow and keras. Text Analytics and practical application implementation with NLTK, Spacy and Gensim.
Monorepo for trading algorithms
Contact: Alexander Hartl, Maximilian Bachl, Fares Meghdouri. Explainability methods and Adversarial Robustness metrics for RNNs for Intrusion Detection Systems. Also contains code for "SparseIDS: Learning Packet Sampling with Reinforcement Learning" (branch "rl").
Fake News Detection Using Recurrent Neural Networks (RNNs) & Long Short Term Memory (LSTM).
Natural Language Processing for Multiclass Classification: A repository containing NLP techniques for multiclass classification of text data.
Weather forecasting using recurrent neural network
In this project we will be building a model capable of generating notes and chords after learning from the dataset of songs we provide to our recurrent neural network and create songs. Before we start, let us recall few of the basic concepts and terminologies we will be using in this project and add to that knowledge the concepts required to successfully train our model to be an excellent composer.
Predicting stock market prices using RNN with LSTM
Deep Learning, Attention, Transformers, BERT, GPT-2, GTP-3
This repository contains the notebooks used in my project "Air quality analysis and forecasting"
Coherent / Meaningful lyrics generation using RNNs , with a dataset created by web-scraping the GENIUS website using its API.
This is my first deep learning project in which I implemented a LSTM model on abc notation music data.
Predicting Sick Patient Volume in a Pediatric Outpatient Setting using Time Series Analysis [Presented at MLHC 2019]
This project contains the file of my undergraduate Final Year Project. This project aims to expose cyberbullying in Twitter by using Machine Learning to classify whether the tweet is suspicious or not. A deployment has been created using streamlit.
Forecasting exchange rates by using commodities prices
Time series analysis on NIFTY-50 index stock data. The project visualises trends in stock data and explores various time-series analysis models such as ARIMA, ARMA and deep learning models such as LTSM and RNN to predict stock prices with the highest accuracy.
Tesla stock price forecasting with LSTM using Pytorch 📈
Deep learning models for classifying ECG time series
✨Using RNN and LSTM generate poetry by Keras, and translate poetry. (通过Keras实现基于RNN与LSTM的藏头诗自动生成模型, 并将生成的古诗翻译成英文.)
English to Telugu Neural Machine Translation using Encoder-Decoder
"Exploring the Dynamics of Stock Price Prediction: Harnessing the power of LSTM neural networks, this project demonstrates the application of deep learning techniques to forecast Apple's stock prices using historical data from Yahoo Finance."
Generate caption on images using CNN Encoder- LSTM Decoder structure
RNN using LSTM layers in coded in Python using Keras to predict Google open stock prices.
This is just a simple RNN text generation model that generates new scripts of Friends TV Show.
Fake and True news Classification using ML and RNN
🚀 Leveraging advanced RNN with LSTM for efficient, real-time anomaly detection in IoT networks, optimized for performance in resource-constrained environments.