fabriciofarias9118

Fabrício Farias's starred repositories

lexical

Lexical is an extensible text editor framework that provides excellent reliability, accessibility and performance.

Language:TypeScriptMIT1909500

android-modular-architecture

📚 Sample Android Components Architecture on a modular word focused on the scalability, testability and maintainability written in Kotlin, following best practices using Jetpack.

Language:KotlinApache-2.0241300

stream-chat-android

:speech_balloon: Android Chat SDK ➜ Stream Chat API. UI component libraries for chat apps. Kotlin & Jetpack Compose messaging SDK for Android chat

Language:KotlinNOASSERTION142800

KRichEditor

A rich text editor (based on QuillJs and MRichTextEditor) ported to Kotlin

Language:JavaScriptMIT8800

quill_html_editor

HTML rich text editor for Android, iOS, and Web, using the QuillJS library. QuillJS is a free, open source WYSIWYG editor built for the modern web.

Language:DartMIT100

quill_html_editor

HTML rich text editor for Android, iOS, and Web, using the QuillJS library. QuillJS is a free, open source WYSIWYG editor built for the modern web.

Language:DartMIT8500

MRichEditor

A rich text editor sample (based on summernote)

Language:Java116500

Pokedex

🗡️ Pokedex demonstrates modern Android development with Hilt, Material Motion, Coroutines, Flow, Jetpack (Room, ViewModel) based on MVVM architecture.

Language:KotlinApache-2.0775800

Kotlin-Base-MVVM

💎 Android Base Project with Kotlin and MVVM applying clean architecture

Language:Kotlin12300

FirebaseWithMVVM

Firebase with MVVM is a series of videos in which you will learn how to implement firebase with MVVM along with UI designs, GitHub branches, merging, and resolving conflicts.

Language:Kotlin7000

An hybrid content based and collaborative filtering Recommender System for the social network instagram that provides users with personalized recommendations of influencers based on the preferences of each one.

Language:Jupyter Notebook200

NMF-Recommendation-System

A Non Negative Matrix Factorization Recommendation System that suggests content for a social network application

Language:Jupyter NotebookMIT200

Hybrid-Hotel-Recommendation-System-for-Yelp

Hotel Recommendation system based on Content, Collaborative, Social Network Based Systems

Language:Java700

Recommendation-systems

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

Language:Jupyter NotebookGPL-3.016300