Deep Recurrent Neural Networks (RNNs) for Time-Series Prediction
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Updated
Oct 24, 2017 - Python
Deep Recurrent Neural Networks (RNNs) for Time-Series Prediction
CS224n : Natural Language Processing with Deep Learning Assignments, Winter 2017, Stanford University.
Example project of LSTM in Keras for google stocks prediction.
Explores language production using recurrent neural networks and distributed semantic representations.
Handwriting Synthesis and Prediction - PyTorch Implementation
Sentiment analysis and prediction project based on a Recurrent Neural Network Model (RNN) that can read in some text and make a prediction about the sentiment of that text.
A simple series of programs to train gated recurrent neural networks with PyTorch and generate text based on them.
A lightweight but powerful library to build token indices for NLP tasks, compatible with major Deep Learning frameworks like PyTorch and Tensorflow.
e3d-lstm; Eidetic 3D LSTM A Model for Video Prediction and Beyond
pretrained transformer and embeddings language models
The repository contains the codes for the generation of enhancer sequences using RNN and embedding concepts.
embeddings language models
Speech recognition project on Keyword Spotting (KWS) using Recurrent Neural Networks (RNNs)
[ACM Computing Surveys'23] Implementations or refactor of some temporal link prediction/dynamic link prediction methods and summary of related open resources for survey paper "Temporal Link Prediction: A Unified Framework, Taxonomy, and Review" which has been accepted by ACM Computing Surveys.
Implementation of a Hierarchical Mamba as described in the paper: "Hierarchical State Space Models for Continuous Sequence-to-Sequence Modeling"
Implementation of a modular, high-performance, and simplistic mamba for high-speed applications
Implementation of Griffin from the paper: "Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models"
Integrating Mamba/SSMs with Transformer for Enhanced Long Context and High-Quality Sequence Modeling
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