rwkv-lm
RWKV (pronounced RwaKuv) is an RNN with great LLM performance, which can also be directly trained like a GPT transformer (parallelizable). We are at RWKV-7 "Goose". So it's combining the best of RNN and transformer - great performance, linear time, constant space (no kv-cache), fast training, infinite ctx_len, and free sentence embedding.
https://github.com/timeseriesai/tsai
Time series Timeseries Deep Learning Machine Learning Python Pytorch fastai | State-of-the-art Deep Learning library for Time Series and Sequences in Pytorch / fastai
https://github.com/blinkdl/chatrwkv
ChatRWKV is like ChatGPT but powered by RWKV (100% RNN) language model, and open source.
ReservoirComputing
Reservoir computing utilities for scientific machine learning (SciML)
rnnreactivation
Code for "Sufficient conditions for offline reactivation in recurrent neural networks" (ICLR 2024)
load_forecasting
Forecasting electric power load of Delhi using ARIMA, RNN, LSTM, and GRU models
https://github.com/ari-dasci/s-tsfe-dl
Time Series Feature Extraction using Deep Learning
https://github.com/rindow/rindow-neuralnetworks
Neural networks library for machine learning on PHP
https://github.com/ahmedshahriar/deeplearning.ai-tensorflow-developer-professional-certificate
My notes and assignments on DeepLearning.AI TensorFlow Developer Professional Certificate
https://github.com/cedrickchee/awd-lstm-lm
LSTM and QRNN Language Model Toolkit for PyTorch (adapted to fast.ai version)
https://github.com/cedrickchee/rnnoise-nodejs
Node.js bindings to Xiph's RNNoise denoising C library
https://github.com/cn-tu/adversarial-recurrent-ids
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").
rwkv-depth-recurrence
A implementation of RWKV models with depth recurrence (crosslayer-param sharing)
https://github.com/atharvapathak/twitter_sentiment_analysis_project
Twitter sentiment analysis is the process of analyzing tweets posted on the Twitter platform to determine the overall sentiment expressed within them. It involves using natural language processing (NLP) and machine learning techniques to classify tweets.
https://github.com/alcantarar/recurrent_grf_prediction
Repository supporting "Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: A recurrent neural network solution"