A repository containing tutorials for practical NLP using PyTorch

Recommendation Systems This is a workshop on using Machine Learning and Deep Learning Techniques to build Recommendation Systesm Theory: ML & DL Formulation, Prediction vs. Ranking, Similiarity, Biased vs. Unbiased Paradigms: Content-based, Collaborative filtering, Knowledge-based, Hybrid and Ensembles Data: Tabular, Images, Text (Sequences) Models: (Deep) Matrix Factorisation, Auto-Encoders, Wide & Deep, Rank-Learning, Sequence Modelling Methods: Explicit vs. implicit feedback, User-Item matrix, Embeddings, Convolution, Recurrent, Domain Signals: location, time, context, social, Process: Setup, Encode & Embed, Design, Train & Select, Serve & Scale, Measure, Test & Improve Tools: python-data-stack: numpy, pandas, scikit-learn, keras, spacy, implicit, lightfm Notes & Slides Basics: Deep Learning AI Conference 2019: WhiteBoard Notes | In-Class Notebooks Notebooks Movies - Movielens 01-Acquire 02-Augment 03-Refine 04-Transform 05-Evaluation 06-Model-Baseline 07-Feature-extractor 08-Model-Matrix-Factorization 09-Model-Matrix-Factorization-with-Bias 10-Model-MF-NNMF 11-Model-Deep-Matrix-Factorization 12-Model-Neural-Collaborative-Filtering 13-Model-Implicit-Matrix-Factorization 14-Features-Image 15-Features-NLP Ecommerce - YooChoose 01-Data-Preparation 02-Models News - Hackernews Product - Groceries Python Libraries Deep Recommender Libraries Tensorrec - Built on Tensorflow Spotlight - Built on PyTorch TFranking - Built on TensorFlow (Learning to Rank) Matrix Factorisation Based Libraries Implicit - Implicit Matrix Factorisation QMF - Implicit Matrix Factorisation Lightfm - For Hybrid Recommedations Surprise - Scikit-learn type api for traditional alogrithms Similarity Search Libraries Annoy - Approximate Nearest Neighbour NMSLib - kNN methods FAISS - Similarity search and clustering Learning Resources Reference Slides Deep Learning in RecSys by Balázs Hidasi Lessons from Industry RecSys by Xavier Amatriain Architecting Recommendation Systems by James Kirk Recommendation Systems Overview by Raimon and Basilico Benchmarks MovieLens Benchmarks for Traditional Setup Microsoft Tutorial on Recommendation System at KDD 2019 Algorithms & Approaches Collaborative Filtering for Implicit Feedback Datasets Bayesian Personalised Ranking for Implicit Data Logistic Matrix Factorisation Neural Network Matrix Factorisation Neural Collaborative Filtering Variational Autoencoders for Collaborative Filtering Evaluations Evaluating Recommendation Systems

A more than 1000X faster paralleled LSTM-CRF implementation modified from the slower version in the Pytorch official tutorial (URL:https://pytorch.org/tutorials/beginner/nlp/advanced_tutorial.html).

pytorch를 사용하여 텍스트 전처리부터 RAG, 에이전트, LLM 파인튜닝을 정리한 Deep Learning NLP 저장소입니다.

A short tutorial for Torchtext, the NLP-specific add-on for Pytorch.

Fully batched seq2seq example based on practical-pytorch, and more extra features.

A collection of deep learning projects implemented in Python, focusing on neural networks, computer vision, and NLP using frameworks like TensorFlow and PyTorch. Includes practical examples, datasets, and tutorials for building and evaluating advanced machine learning models.

Materials for O'Reilly DL 4 NLP tutorial (NYC, June 2017)

torchtext-tutorial (text classification)

Materials for O'Reilly DL 4 NLP tutorial (SF 2017)

PyTorch NLP tutorial, O'Reilly AI NYC 2018

NLP Classification Tutorial with PyTorch

A Pytorch based LSTM Punctuation Restoration Implementation/A Simple Tutorial for Leaning Pytorch and NLP

A comprehensive repository of PyTorch tutorials, projects, and examples to master deep learning concepts and model implementation. Covers neural networks, computer vision, and NLP with hands-on code and detailed explanations for learners.

O'Reilly AI training - London 2018

A batch-wise NLP tutorial with Pytorch

PyTorch Tutorials for NLP with Deep Learning

My PyTorch playground for NLP

Deep Learning and Natural Language Processing using PyTorch (O'Reilly AI - NYC, 2019)

Welcome to my Transformers tutorial series! In this series, I'll be diving into the powerful Transformer architecture and its implementation in TensorFlow and PyTorch. Whether you're an experienced NLP practitioner or just starting out, I hope you'll find the series informative and engaging.

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In this tutorial I'll show you how to use BERT with the huggingface PyTorch library to quickly and efficiently fine-tune a model to get near state of the art performance in sentence classification. More broadly, I describe the practical application of transfer learning in NLP to create high performance models with minimal effort on a range of NLP tasks.

Pytorch LSTM tagger tutorial with minibatch training. Includes discussion on proper padding, embedding, initialization and loss calculation.

NLP tutorial with pytorch

DL-NLP is a tutorial for NLP(Natural Language Processing) based on DL(Deep Learning) by using Pytorch and Tensorflow.

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NLP tutorial using PyTorch

RNN

This repository contains the complete tutorial with implementation of NLP and from scrach implementation of GRU and LSTM and RNN architectures in pytorch. Imbd data set used for sentiment analysis on each of these architectures. And also have the implementation of concepts like embeddings etc.

Intro tutorials for PyTorch