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 curated set of deep learning projects using TensorFlow/Keras, covering vision, NLP, audio, GANs and transfer learning, with practical examples and tutorials.

Modulabs NLP & Sequential Modeling - Lectures with Python/Tensorflow Tutorials

📚 Hands-On Machine Learning Portfolio — Full implementation of Aurélien Géron’s book + Bahrak’s tutorials. Covers Scikit-Learn, TensorFlow/Keras, CNNs, RNNs, NLP, GANs, RL (DQN), clustering, PCA, and real-world projects like fraud detection, Bach generation, and Lunar Lander. Clean, production-ready code with saved models & TensorBoard.

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This Repo deals with the basic to advance NLP tutorial available on the official tensorflow website.

Hi, I am interested in NLP, following TensorFlow Tutorials, I am doing this Text classification with TensorFlow Hub on movie reviews

Explore sentiment analysis with BERT through a detailed tutorial. Learn setup with TensorFlow, numpy, and Hugging Face libraries. Dive into data preprocessing, model training, optimization, and evaluation for practical insights. Perfect for understanding and implementing sentiment analysis in NLP.

This repository explores Machine Learning through projects, scripts, and tutorials. It covers data preprocessing, model training, evaluation, and deployment using frameworks like scikit-learn, TensorFlow, and PyTorch, with applications in classification, regression, NLP, and computer vision.

A simple tutorial on how to use distilBERT or BERT for a NLP classification problem on Google Colab using TensorFlow and Hugging Face pre-trained models. The used data are reviews scraped from the web and pulled through Google Translate to obtain an English review dataset together with the corresponding ratings.