💫 Jupyter notebooks for spaCy examples and tutorials

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

Tutorial for BERT (and other transformer) embeddings with spaCy and Rasa

spaCy tutorial in English and Japanese. spacy-transformers, BERT, GiNZA.

A brief tutorial for spaCy 2.x, which runs on Google Colab

NLP Tutorial 8 - Sentiment Classification using SpaCy for IMDB and Amazon Review Dataset

A collection of Python-based NLP projects exploring text processing, sentiment analysis, and language modeling using libraries like NLTK, SpaCy, and Transformers. Includes hands-on implementations with datasets and tutorials for building and evaluating NLP models.

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A quick tutorial for using spaCy Layout.

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This is a tutorial about NER with traditional spaCy and spaCy transformers roBERTa

Accompanying code to www.shanelynn.ie tutorial on FastAPI NLP API creation. In this simple API, we're building a web application that can process text in HTTP requests to extract sentiment and entities automatically.

In this tutorial notebook we will be covering the following NLP libraries and its python implementation¶ Table of Contents Knowlege Graph (KG) BERT spaCy NLTK

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Tutorial introdutório sobre como configurar um ambiente completo para executar os primeiros testes com Processamento de Linguagem Natural (PLN) usando a biblioteca spacy.

Natural Language Processing (NLP) is a field of artificial intelligence that focuses on the interaction between computers and humans through natural language.

Utilizing a SpaCy tutorial to use NLP to analyze a poem

In this tutorial I will show an example of multiclass text classification using prodigy and spacy.

this tutorial is about Training a custom NER model with spaCy Using it's pre-trained transformer model in order to extract fashion related entities such us colors, print and patterns, product types...from clothing texts.

A tutorial applying modern NLP tools (Spacy, SciSpacy) to MIMIC-III medical notes for clinical concept extraction. Includes entity recognition, word embeddings, and t-SNE visualizations. Additional exploration of advanced models like BERT. Fully documented in a Jupyter notebook with teaching slides.

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spaCy tutorial from spacy.io

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A Notebook which illustrates the use of Spacy's Matcher

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SpaCy is an open-source software library by ExplosionAI for Natural Language Processing Tasks. It has capabilities to perform same functions as NLTK (Natural Language Tool Kit) library but is built for speed. Thus SpaCy is preferred over NLTK in an enterprise setting.

A brief walkthrough on how to use Spacy for NLP problems