💫 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

Here are the notebooks used during the spacy youtube series.

A Notebook based on NLP Spacy course

Natural language processing (NLP) is an exciting branch of artificial intelligence (AI) that allows machines to break down and understand human language. I plan to walk through text pre-processing techniques, machine learning techniques and Python libraries for NLP. Text pre-processing techniques include tokenization, text normalization and data cleaning. Once in a standard format, various machine learning techniques can be applied to better understand the data. This includes using popular modeling techniques to classify emails as spam or not, or to score the sentiment of a tweet on Twitter. Newer, more complex techniques can also be used such as topic modeling, word embeddings or text generation with deep learning. We will walk through an example in Jupyter Notebook that goes through all of the steps of a text analysis project, using several NLP libraries in Python including NLTK, TextBlob, spaCy and gensim along with the standard machine learning libraries including pandas and scikit-learn.

A comprehensive set of Jupyter notebooks that take you from NLP fundamentals to advanced techniques. Covers text preprocessing, POS tagging, NER, sentiment analysis (with VADER), text classification, word embeddings, and transformer models like BERT. Built with real-world datasets using NLTK, spaCy, scikit-learn, and Hugging Face Transformers.

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

A Jupyter notebook demonstrating entity extraction on headlines with SpaCy.

NLP Notebooks covering implementation for Knowledge Graph, BERT, spaCy, NLTK

notebooks and scripts showing how to use spacy for simple NLP tasks

This repository hosts notebooks demonstrating Word2Vec implementation and visualization techniques using various libraries like Gensim, spaCy, and Keras. It includes examples of training Word2Vec models, visualizing word vectors, and utilizing pre-trained embeddings.

Jupyter Notebook that accompanies my article on spaCy's Matcher and Phrase Matcher. See the article at https://medium.com/@tdubon6/in-this-article-were-going-to-talk-about-the-matcher-and-the-phrasematcher-in-the-spacy-toolbox-c28c845c113b

ATS Scanner for Resumes using Data Analytics in Jupyter Notebook to optimize resumes. Key features: resume parsing, keyword matching, scoring system, and data visualization. Technologies: Python, pandas, numpy, NLTK, Spacy, matplotlib, seaborn, regex, scikit-learn. Empowering job seekers with actionable insights.

training spaCy's spancat component in a notebook

The jupyter notebook is meant to be a complementary resource to my medium article on semantic analysis with spacy

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.

spaCyを基盤とした多言語対応のテキストマイニングツールキットです。コンコーダンス検索、共起語分析、特徴語分析、依存構造解析、共起ネットワーク分析など、多様な分析機能を提供します。Jupyter Notebookベースの対話的な分析環境により、多言語に対応したテキストデータの効率的な処理、探索的分析、検証的分析を可能にします。

Notebooks zum Workshop SpaCy und Flair

In this notebook i explain how stemming and lemmatization can be performed with NLTK and spaCy libraries in Python

Jupyter notebooks on the spacy course

Notebooks pertaining to learning NLP using Spacy

This repository contains NLP notebooks using SpaCy library

Py2.7 Jupyter Notebook SpaCy docker image for learning

This repository aims to store a notebook about SpaCy

Convert Jupyter Notebook to NLP SpaCy Web Application with Mercury

A Notebook which illustrates the use of Spacy's Matcher

Jupyter notebooks and text files for sentiment analysis with NLTK and topics analysis with spaCy and Gensim.

This repository contains the python notebook for the NLP extraction using spaCy and PyTextRank.

A Natural Language Processing project to analysis resumes using spaCy library in Python jupyter notebook

It is a notebook showcasing the common tasks and techniques used in NLP. I have used spacy and nltk libraries of python in the notebook. It contains the notebook used in my blog