A model library for exploring state-of-the-art deep learning topologies and techniques for optimizing Natural Language Processing neural networks

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

Enjoy major NLP Projects. I used NLP libraries,ML for NLP,DL for NLP....etc. In this repository, i will be making some cool projects in NLP from basic to intermediate level.

采用nlp-architect实现rasa-nlu中文意图提取和槽填充

NLP and Knowledge Graphs for architects, engineers, and contractors

AWS Academy Machine Learning for Natural Language Processing (NLP) is a follow-up course to AWS Academy Machine Learning Foundations. The course is at an intermediate technical level (similar to the AWS Academy Architecting, Operations, and Developing courses) and is appropriate for students who are pursuing careers that require ML knowledge

Development environments might not have the exact requirements as production environments. Moving data science and machine learning projects from idea to production requires state-of-the-art skills. You need to architect and implement your projects for scale and operational efficiency. Data science is an interdisciplinary field that combines domain knowledge with mathematics, statistics, data visualization, and programming skills. The Practical Data Science Specialization brings together these disciplines using purpose-built ML tools in the AWS cloud. It helps you develop the practical skills to effectively deploy your data science projects and overcome challenges at each step of the ML workflow using Amazon SageMaker. This Specialization is designed for data-focused developers, scientists, and analysts familiar with the Python and SQL programming languages who want to learn how to build, train, and deploy scalable, end-to-end ML pipelines - both automated and human-in-the-loop - in the AWS cloud. Each of the 10 weeks features a comprehensive lab developed specifically for this Specialization that provides hands-on experience with state-of-the-art algorithms for natural language processing (NLP) and natural language understanding (NLU), including BERT and FastText using Amazon SageMaker. Applied Learning Project By the end of this Specialization, you will be ready to: • Ingest, register, and explore datasets • Detect statistical bias in a dataset • Automatically train and select models with AutoML • Create machine learning features from raw data • Save and manage features in a feature store • Train and evaluate models using built-in algorithms and custom BERT models • Debug, profile, and compare models to improve performance • Build and run a complete ML pipeline end-to-end • Optimize model performance using hyperparameter tuning • Deploy and monitor models • Perform data labeling at scale • Build a human-in-the-loop pipeline to improve model performance • Reduce cost and improve performance of data products

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Morphological analysis for Modern Standard Arabic (MSA) and Dialectal Arabic (DA) uses NLP and Deep Learning to manage complex morphology, clitics, and root–pattern structures. While MSA is standardized, DA remains challenging due to orthographic variation, vocabulary diversity, and limited labeled data.

ECO (Early-stage Carbon Observer) is a machine learning tool that helps architects and designers predict the embodied carbon impact of building designs from early-stage textual descriptions. By extracting key features using NLP and applying HGB regression, ECO provides real-time feedback to inform sustainable design decisions.

A comprehensive collection of Natural Language Processing implementations. Features hands-on projects ranging from fundamental text preprocessing (Regex, Tokenization) to advanced vectorization (TF-IDF, Word Embeddings) and classification using SpaCy, NLTK, and Gensim.

PyTorch implementations of RNN architectures for NLP, including seq2seq models.

Implementations of Transformer-based NLP models in PyTorch

Proyecto para arquitectura de computadores: Trata de una simplificación de una aplicación real, del ámbito del (NLP) Natural Language Processing y (ML) Machine Learning al que se le van a aplicar tecnicas de paralelizacion mediante la libreria de OpenMP para encontrar la version mas eficiente.

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A federated learning-based medical diagnosis system that predicts diseases from patient symptom report images using OCR, NLP, and BERT. Ensures privacy, explainability with SHAP/LIME, and real-time predictions via a Flask web app. Ideal for decentralized, secure, and interpretable healthcare AI.

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I’m Mohammad Reza, a Machine Learning and NLP Engineer dedicated to bridging the gap between raw data and human-like understanding. 🤖 I specialize in architect

👋 Hi, I'm Saji! 🔥 Machine Learning Architect | Deep Learning Engineer | AI Consultant 🚀 Passionate about AI, NLP, and Hyperparameter Optimization 📌 Exploring Generative AI, RAG, and AutoML 📫 Connect with me: sajijohnmiranda@gmail.com

ArchAI-VisionGeo : Analyse de plans architecturaux via IA. Utilise CNN pour la vision, NLP pour le texte et GeoPandas pour l'enrichissement géospatial. Optimisé pour GPU, extensible, idéal pour architectes et urbanistes. Développé avec PyTorch et Streamlit.

ARCHITECT (Automated Responsive Creation of House Interior and Exterior Configurations and Transformations) is a modular Generative AI-based system designed to streamline architectural workflows and interior design processes. The platform combines NLP, computer vision, and deep learning to automate layout generation, design visualization

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Estetika: A Project Management App using NLP with a Recommendation System for Interior Design Architects

This repository hosts PyTorch implementations of RNNs, GRUs, LSTMs, and Transformers, used for a toy task of next-word prediction.

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