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Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BER
QuillGPT is an implementation of the GPT decoder block based on the architecture from Attention is All You Need paper by Vaswani et. al. in PyTorch. Additionally, this repository contains two pre-trained models — Shakespearean GPT and Harpoon GPT, a Streamlit Playground, Containerized FastAPI Microservice, training - inference scripts & notebooks.
The repository contains the notebook as well as python implementations of laguange models, from basic naive implementations to Transformers on a simple name dataset. more notebooks and files will be added regularly
This repository contains the collection of explorative notebooks pure in python and in the language that we, humans can read. Have tried to compile all lectures from the Andrej Karpathy's 💎 playlist on Neural Networks - which we will end up with building GPT.
A miniGPT inspired from the original NanoGPT released by OpenAI. This is a notebook to walk through the decoder part of the transformer architecture with details outlined.
This repository contains various models for text summarization tasks. Each model has a separate directory containing the implementation code, pretrained weights, and a Jupyter notebook for testing the model on sample input texts. Feel free to use these models for your own text summarization tasks or to experiment with them further.
This repo contains my implementaions of notebooks in TensorFlow (not in Trax which is used in the course) of Natural Language Processing Specialization: Course4 (NLP with Attention) by deeplearning.ai on Coursera
Part of M.Sc. Computer Science project. Demo Jupyter notebook/Python scripts to download a pretrained language model from huggingface and finetune it according to own topic domain and needs.