rnn-lstm

Collection of must read papers for Data Science, or Machine Learning / Deep Learning Engineer

RNN architectures trained with Backpropagation and Reservoir Computing (RC) methods for forecasting high-dimensional chaotic dynamical systems.

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.

Cuda implementation of Extended Long Short Term Memory (xLSTM) with C++ and PyTorch ports

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.

Natural Language Processing for Multiclass Classification: A repository containing NLP techniques for multiclass classification of text data.

Monorepo for trading algorithms

Weather forecasting using recurrent neural network

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").

This repository contains the notebooks used in my project "Air quality analysis and forecasting"

Fake News Detection Using Recurrent Neural Networks (RNNs) & Long Short Term Memory (LSTM).

Beginner Friendly CheatCodes

🚀 Leveraging advanced RNN with LSTM for efficient, real-time anomaly detection in IoT networks, optimized for performance in resource-constrained environments.

Deep learning models for classifying ECG time series

Predicting stock market prices using RNN with LSTM

An AI model capable of generating music after learning on an extensive dataset of songs provided to the recurrent neural network.

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.