Projects sharing features with Pytorch Qrnn

DrQA A pytorch implementation of the ACL 2017 paper Reading Wikipedia to Answer Open-Domain Questions (DrQA).

😇A pyTorch implementation of the DeepMoji model: state-of-the-art deep learning model for analyzing sentiment, emotion, sarcasm etc

Training RNNs as Fast as CNNs (https://arxiv.org/abs/1709.02755)

Speech Recognition using DeepSpeech2.

The first public PyTorch implementation of Skip-Thought Vectors

Training RNNs as Fast as CNNs (https://arxiv.org/abs/1709.02755)

This project is a PyTorch sentiment analysis tutorial and a deep learning implementation for analyzing text. It provides a natural language processing sequence classification pipeline designed to clean text data and train neural networks to categorize sequences of words. The implementation focuses on adapting pretrained language models for specific text classification tasks using custom datasets. It includes a process for fine-tuning large-scale language models and implementing recurrent networks and transformers for emotional tone detection. The project covers the broader surface of text se

Tree LSTM implementation in PyTorch

PyTorch implementation of "Efficient Neural Architecture Search via Parameters Sharing"

Pytorch implementations of various Deep NLP models in cs-224n(Stanford Univ)

Deal or No Deal? End-to-End Learning for Negotiation Dialogues

This project is a library of pretrained computer vision architectures and backbones for image classification and feature extraction. It serves as a comprehensive model zoo and collection of standardized image encoders, including ResNet, Vision Transformers, and EfficientNet, for use in visual analysis and as backbones for object detection and image segmentation. The library provides a framework for distributed training and evaluation of image models using advanced data augmentation and optimization scripts. It includes a dedicated toolset for converting trained PyTorch vision models into the

LSTM and QRNN Language Model Toolkit for PyTorch

This project is a PyTorch library for building and training Kolmogorov-Arnold Networks. It implements a neural network architecture that replaces fixed activation functions with learnable spline-based functions on edges, serving as a tool for interpretable machine learning. The implementation utilizes reformulated matrix operations to reduce memory overhead and increase computation speed. It employs L1 regularization to sparsify network weights, which improves the transparency of the model's internal logic and decisions. The framework covers a range of capabilities including grid-based funct

This project is a collection of deep learning research implementations and a reproduction kit designed to translate theoretical AI papers into working code. It provides a library of neural network architectures and reference implementations for reproducing seminal research concepts through interactive notebooks. The repository distinguishes itself through the implementation of AI theory and scaling laws, covering complexity dynamics, information theory, and the simulation of universal AI agents. It also includes a benchmarking suite for synthetic reasoning, allowing for the evaluation of mode

This project is a collection of implementation guides, recipes, and developer resources for building applications with Llama models. It serves as a comprehensive kit for developing autonomous agents, establishing retrieval-augmented generation systems, and executing model fine-tuning. The resource provides specific patterns for multimodal workflows that process text, images, and audio. It includes specialized guidance on adapting pre-trained model weights for targeted tasks and implementing tool-calling orchestration to connect models with external APIs and functions. The codebase covers a b