This repository is used to collect papers and code in the field of AI.
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Updated
Jun 24, 2024
This repository is used to collect papers and code in the field of AI.
toyGPT - A Hands-On Project in Building a Basic GPT Model
Basic Gesture Recognition Using mmWave Sensor - TI AWR1642
A numpy implementation of the Transformer model in "Attention is All You Need"
Official PyTorch implementation of the Vectorized Conditional Neural Field.
An ultimately comprehensive paper list of Vision Transformer/Attention, including papers, codes, and related websites
Implementation of the LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens Paper
LinkOrgs: An R package for linking linking records on organizations using half a billion open-collaborated records from LinkedIn
Welcome to quote our published papers, and the codes have been uploaded.
Yet Another Transformer Implementation
An introduction to attention mechanisms and the vision transformer
Educational code for understanding attention mechanisms. You will build a good intuition to K, Q, and V, key in modern Transformer architectures.
In this repository, I have explained the working of the Transformer architecture, provided the code for building it from scratch, and demonstrated how to train it.
A novel implementation of fusing ViT with Mamba into a fast, agile, and high performance Multi-Modal Model. Powered by Zeta, the simplest AI framework ever.
Code for CRATE (Coding RAte reduction TransformEr).
Sequence-to-sequence framework with a focus on Neural Machine Translation based on PyTorch
Seq2SeqSharp is a tensor based fast & flexible deep neural network framework written by .NET (C#). It has many highlighted features, such as automatic differentiation, different network types (Transformer, LSTM, BiLSTM and so on), multi-GPUs supported, cross-platforms (Windows, Linux, x86, x64, ARM), multimodal model for text and images and so on.
Developing Natural Language Processing tools to enhance Learning Analytics. Creating an automated dashboard that diagnoses strengths and weaknesses from educational data.
Sentiment analysis on the IMDB dataset using Bag of Words models (Unigram, Bigram, Trigram, Bigram with TF-IDF) and Sequence to Sequence models (one-hot vectors, word embeddings, pretrained embeddings like GloVe, and transformers with positional embeddings).
Extractive Nepali Question Answering System | Browser Extension & Web Application
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