Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch
邱锡鹏《神经网络与深度学习》（蒲公英书）理论书 v2 与通识版
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.
all kinds of text classification models and more with deep learning
A concise but complete full-attention transformer with a set of promising experimental features from various papers
Implementation / replication of DALL-E, OpenAI's Text to Image Transformer, in Pytorch
An ultimately comprehensive paper list of Vision Transformer/Attention, including papers, codes, and related websites
A collection of important graph embedding, classification and representation learning papers with implementations.
A TensorFlow Implementation of the Transformer: Attention Is All You Need
RuVector is a High Performance, Real-Time, Self-Learning Ai, Vector GNN, Memory DB built in Rust.
Graph Attention Networks (https://arxiv.org/abs/1710.10903)
Automatic Speech Recognition (ASR), Speaker Verification, Speech Synthesis, Text-to-Speech (TTS), Language Modelling, Singing Voice Synthesis (SVS), Voice Conversion (VC)
Pytorch implementation of the Graph Attention Network model by Veličković et. al (2017, https://arxiv.org/abs/1710.10903)
Show, Attend, and Tell | a PyTorch Tutorial to Image Captioning
Multilingual Automatic Speech Recognition with word-level timestamps and confidence
Keras Attention Layer (Luong and Bahdanau scores).
My implementation of the original GAT paper (Veličković et al.). I've additionally included the playground.py file for visualizing the Cora dataset, GAT embeddings, an attention mechanism, and entropy histograms. I've supported both Cora (transductive) and PPI (inductive) examples!
Build a modern LLM from scratch. Every line commented. Explained like we are five.
Reformer, the efficient Transformer, in Pytorch