Este repositório esta em constante construção!
Todo dia muito conteúdo sobre estatística, machine learning, programação em R, Python e tantos outros assuntos que interessam a quem estuda Ciência de Dados é gerado e nem sempre é possível acompanhar tanto conteúdo!
Os links guardados que acho interessante porque já me ajudaram ou poderiam vir a me ajudar no futuro se acumulam cada dia mais e para facilitar o acesso a estes links quando estou procurando por referências úteis para resolver os desafios do trabalho criei essa lista com alguns links que encontro por ai nos momentos de leitura, espero que ajude!
Existem links tanto em portugues quanto em inglês e a maioria dos links aqui está mais relacionado com estatística e programação em R mas também existem muitos dos links que apresentam os métodos em linguagem Python. A organização destes links foi sendo feita conforme os links foram adicionados então ainda tem muito a melhorar! O importante é que o conteúdo está ai e basta um ctrl+F para achar algum material sobre o assunto de interesse
- from Data to Viz
- Introduction to summarytools
- GUIA QUARTZ PARA LIMPEZA DE DADOS
- Using the ExPanDaR package for panel data exploration
- visdat - Preliminary Exploratory Visualisation of Data
- A data.table and dplyr tour
- DataExplorer - automate most of data handling and visualization, so that users could focus on studying the data and extracting insights
- Fast and efficient alternatives to tidyr functions built on data.table
- Correlation Coefficients in One Picture
- UpSetR - An R implementation of the UpSet set visualization technique
- naniar - Analise Exploratoria de dados faltantes
- visdat - helps you visualise a dataframe and “get a look at the data”
- DataExplorer - Exploratory Data Analysis (EDA) is the initial and an important phase of data analysis/predictive modeling
- SKIMR: ESTATÍSTICAS BÁSICAS COM R
- correlationfunnel - Methodology, Key Considerations, and FAQs
- Tidylog, logging your pipelines
- inspectdf - Exploratory data analysis with categorical data
- catmaply
- Getting started with the pwr package
- OPDOE: Optimal Design of Experiments
- G*Power: Statistical Power Analyses for Windows and Mac
- An Alternative to Cohen's Standardized Mean Difference Effect Size: A Robust Parameter and Confidence Interval in the Two Independent Groups Case.
- Effect Sizes for Research: Univariate and Multivariate Applications, Second Edition
- SMOTE explained for noobs - Synthetic Minority Over-sampling TEchnique line by line
- Visualizing and interpreting Cohen’s d effect sizes
ggplot2 - based on The Grammar of Graphics
- Top 50 ggplot
- 7 Visualizations You Should Learn in R
- Data Visualization em R
- The R Graph Gallery
- Patchwork - "Ridiculamente Simples combinar figuras do ggplot"
- Alluvial diagram ou Sankey no Kaggle
- ggplot2 - Easy Way to Mix Multiple Graphs on The Same Page
- Gráficos incluindo barras de erro
- GGplot com figuras aleatorias
- 5 Melhores extensões para o ggplot2
- Great Visualizations in R - Kaggle happiness 2017
- Curso ggplot2
- Gráfico e tabela descritiva na mesma imagem
- Seja incrível em ggplot2: um guia prático para ser altamente eficaz - software R e visualização de dados
- 5 melhores extensões de ggplot
- Diagrama de Venn com ggplot2
- Infographic-style charts using the R waffle package
- Criando Slopegraphs com R
- Beeswarms em vez de histogramas
- Por um mundo maior que o gráfico de pizza
- [ggplot2] Welcome viridis !
- Plotting Likert-Scales (net stacked distributions) with ggplot
- GGPLOT2 add logo
- Sankey Diagram for the 2018 FIFA World Cup Forecast
- How to Create Sankey Diagrams From Tables (Data Frames) Using R
- R colors - cores legais para R
- ggrepel - legendas legais
- Styling ggplot2 Graphics - Paleta de cores e text group
- Exploring ggplot2 boxplots – Defining limits and adjusting style
- beautiful graphics ggplot2
- Paleta de cores (cores diferenciadas - muitas)
- Gráfico de Sankey
- Facets for ggplot in R
- THE R GRAPH GALLERY - CONNECTION MAP
- Calendar Heatmap with ggplot2
- Gráficos sem programar no RStudio com esquisse
- Create data visualizations like BBC News with the BBC’s R Cookbook
- The ggforce Awakens (again)
- Graph analysis using the tidyverse
- Tidy correlation tests in R
- Set Analysis: A face off between Venn diagrams and UpSet plots
- 3D LEGO models and mosaics from images using R and #tidyverse
- scatterD3 - Um widget HTML R para visualização de gráficos de dispersão interativos. Ele é baseado no pacote R de htmlwidgets e na biblioteca javascript d3.js.
- ggalt - Extra Coordinate Systems, Geoms, Statistical Transformations, Scales & Fonts for ‘ggplot2’
- The 'see' package: beautiful figures for easystats
- ggfittext - provides a ggplot2 geom for fitting text inside a box
- Easyalluvial - Model Response Plots with Categorical Variables
- How to make Square (Pie) Charts for Infographics in R
- INTRODUCING 3D GGPLOTS WITH RAYSHADER
- Creating a pretty Gantt chart with ggplot2 - cronograma para projetos
- ggsci - Scientific Journal and Sci-Fi Themed Color Palettes for ggplot2
- MapPalettes
- heatmaply 1.0.0 – beautiful interactive cluster heatmaps in R
- Using gghalves - Frederik Tiedemann - Plot que ocupam metade do espaço
- Evolve new colour palettes in R with evoPalette
- {mdthemes} is on CRAN: markdown powered themes for {ggplot2}
- ggtext - Draw boxes containing text
- ggcyberpunk
- tinter - provides a simple way to generate monochromatic palettes
- 82 registered extensions available to explore
- Raincloud Plots
- How to adjust labels in flashlight breakdown plots
- Tabelas para ggplot2 - mmtable2
- ggsignif: Significance Brackets for ‘ggplot2’
- Reorder legend to match order of plot elements in ggplot2
- Bagplot: generalização bivariada do Boxplot| R
- Add dogs to ggplot2 with ggdogs
- tidyHeatmap
- ggbump creates elegant bump charts in ggplot
- A QUICK HOW-TO ON LABELLING BAR GRAPHS IN GGPLOT2
- geomtextpath - Create curved text in ggplot2
- ggeasy - is here to make ggplots a little easier.
- ggside: Plot Linear Regression using Marginal Distributions
- Animate your data wrangling
- Communicate your work with animated graphs in R!
- Animating Data Transformations
- gganimate has transitioned to a state of release
- Sunburst - Grafico moderno de setores / pizza / rosca
- Aster plot in D3js
- Introducing R2D3
- ggiraphExtra - Package ggiraphExtra contains many useful functions for exploratoty plots
- parcats trace, and adds marginal histograms for numerical variables.
- Analysing cryptocurrency market in r - API criptomoedas
- Coletando dados do Facebook
- Workshop sobre AIP do twitter facebook e youtube
- The Movie Database API
- Alpha Vantage - Free APIs for Realtime and Historical Financial Data, Technical Analysis, Charting, and More!
- Handling large datasets in R - Grandes bases de dados
- "Kindof" Big Data in R
- Como ler micro dados do ENEM no R
- Fellipe Gomes
- SimplyStats
- Business - Science
- RDojo
- Dr. Shirin Glander
- Vooo – Insights - Data Science. Python. Gestão.
- STATWORX Blog
- STHDA - Statistical tools for high-throughput data analysis
- Julia Silge
- Data Science Academy
- DataCamp - Official Blog
- Ensina.AI
- colah's blog - Artigos bons sobre Redes Neurais
- Good code vs bad code: why writing good code matters, and how to do it
- Why I want to write nice R code
- Alinhando Comentários
- Auto format r code in Rstudio e tidy_eval
- styler - Um formatador de código-fonte não invasivo para R
- The tidyverse style guide
- Styler
- Efficient R programming - Coding style
- Google's R Style Guide
- Advanced R by Hadley Wickham - Style guide
- Pacote datapasta para copiar e colar df, tbl, dbl
- Building Reproducible Data Packages with DataPackageR
- beautifyR - RStudio addin for formatting Rmarkdown tables
- remedy - RStudio Addins to Simplify Markdown Writing https://rtask.thinkr.fr
- goodpractice - Advice on R Package Building
- logger - A lightweight, modern and flexibly logging utility for R – heavily inspired by the futile.logger R package and logging Python module
- 3 Princípios para facilitar a vida de todo programador (KISS, YAGNI, DRY)
- Top 9 Keyboard Shortcuts in VSCode for Data Scientists
- How to tailor your Academic CV for Data Science roles
- How to create a timeline of your cv in R
- Um guia para reuniões "one on one" com lideranças
- O QUE OS RECRUTADORES PROCURAM EM UM CURRÍCULO PARA CIENTISTA DE DADOS?
- Carta de referencia para um emprego, modelo ideal
- Post no Linkedin com dicas para CV - Step-by-step breakdown of a resume
- Resume Owrded - Free instant feedback on your resume and Linkedin profile
- The steps of a Kaggle project - Bruna Wundervald
- R Learn the language designed for data analysis. This track includes data set-up, machine learning and data visualization.
- STUDENT PERFORMANCE IN EXAMS
- Data Science with Compassion
- Spatial analysis tutorial
- Good Feature Building Techniques — Tricks for Kaggle — My Kaggle Code Repository
- GGPLOT2
- Introduction to ggplot2—the grammar
- STHDA super post GGPLOT2
- RMarkdown
- Curso do Google Machine Learning
- 100 Free Tutorials for Learning R
- Ciencia de dados com R - conceitos básicos
- Code for Workshop: Introduction to Machine Learning with R
- Aulas USP | Inteligência Artificial em saúde: o uso de machine learning
- Data Science and Big Data Analytics: Making Data-Driven Decisions - MIT
- Teaching R to New Users - From tapply to the Tidyverse
- Learning from Data - Machine Learning course - recorded at a live broadcast from Caltech
- stanford.edu - Machine Learning
- USGS - R Training Curriculum
- FOUNDATIONS OF MACHINE LEARNING - Bloomberg ML EDU
- Mais de 100 cursos de Harvard, gratuitos e com certificado
- Machine Learning para Cientista de Dados
- R Learn the language designed for data analysis. This track includes data set-up, machine learning and data visualization.
- R e PostgreeSQL - como usar o postgreesql a partir do R
- Improving your statistical inferences - coursera
- Data Science for Beginners - A Curriculum
- Practical Deep Learning for Coders - Practical Deep Learning
- HANDBOOK - Summary and Analysis of Extension Program Evaluation in R
- CheetSheet Super resumão resumido
- A bit more understanding of Cronbach’s alpha
- Where do p-values come from? Fundamental concepts and simulation approach
- How to calculate poker probabilities in python
- Pacote para interpretar testes estatísticos
- Rules of thumb on magnitudes of effect sizes
- Z-test
- Testes de variância e Análise de Variância (ANOVA)
- A Visual Demonstration of a Chi Squared Test
- ggstatsplot: ggplot2 Based Plots with Statistical Details
- Testes de hipoteses para normalidade - karinnecristina - AceleraDev_Codenation
- ANCOVA example
- https://www.researchgate.net/post/How_can_I_write_a_clear_rationale_for_the_3-way_interaction_2_x_2_x_2
- https://stats.stackexchange.com/questions/245239/in-three-way-anova-how-to-interpret-the-three-way-interaction
- https://stats.idre.ucla.edu/stata/faq/how-can-i-understand-a-3-way-continuous-interaction-stata-12/
- http://psycnet.apa.org/fulltext/2006-08435-013.html
- Definição e Interpretação de Efeitos de Interação
- Testing and Dropping Interaction Terms in Regression and ANOVA models
- https://stats.stackexchange.com/questions/22916/treatment-of-interactions-in-multiple-regression
- https://www.theanalysisfactor.com/why-anova-and-linear-regression-are-the-same-analysis/
- https://stats.stackexchange.com/questions/190984/anova-vs-multiple-linear-regression-why-is-anova-so-commonly-used-in-experiment
- https://stats.stackexchange.com/questions/76250/r-anova-and-linear-regression/76292#76292
- https://www.researchgate.net/post/Can_anyone_help_me_to_get_the_core_differences_between_regression_model_and_ANOVA_model2
- https://www.researchgate.net/post/Two-Way_ANOVA_or_Mixed_ANOVA
- https://stats.stackexchange.com/questions/13241/the-order-of-variables-in-anova-matters-doesnt-it
- https://stats.stackexchange.com/questions/212496/why-do-p-values-change-in-significance-when-changing-the-order-of-covariates-in
- https://stats.stackexchange.com/questions/45878/r-model-tables-incorrect-means-possible-bug
- https://www.researchgate.net/post/Kruskal-Wallis_test_on_Interaction_following_by_a_pairwisewilcoxtest
- rcompanion.org/handbook - Kruskal–Wallis Test
- Experimentos fatoriais - LEG
- https://stats.stackexchange.com/questions/78365/what-is-the-non-parametric-equivalent-of-a-two-way-anova-that-can-include-intera
- http://www.talkstats.com/threads/non-parametric-equivalent-of-a-factorial-anova-not-repeated-measures.21958/
- https://stats.idre.ucla.edu/?s=non+parametric+anova+fatorial
- https://www.researchgate.net/post/Is_there_a_non-parametric_equivalent_of_a_2-way_ANOVA
- rcompanion.org/handbook - Two-way Ordinal Regression with CLM
- https://www.theanalysisfactor.com/non-parametric-anova-in-spss/
- Planeamento Experimental usando ANOVA de 1 e 2 fatores com R – uma breve abordagem prática - PDF
- Rank Transformations as a Bridge between Parametric and Nonparametric Statistics
- The Aligned Rank Transform for Nonparametric Factorial Analyses Using Only ANOVA Procedures
- https://stats.stackexchange.com/questions/78365/what-is-the-non-parametric-equivalent-of-a-two-way-anova-that-can-include-intera
- Interaction Effects in Regression
- https://stats.stackexchange.com/questions/91872/alternatives-to-one-way-anova-for-heteroskedastic-data
- https://stats.idre.ucla.edu/r/faq/how-can-i-do-post-hoc-pairwise-comparisons-in-r/
- https://stackoverflow.com/questions/12288312/in-r-how-do-i-run-a-two-way-anova-that-uses-type-iii-errors-and-looks-at-pairwi
- Tutorial para iniciantes em Inferência Bayesiana
- Bayesplot - plotando modelo bayesianos
- How Bayesian inference works - Data Science and Robots Blog
- bayestestR - Utilities for analyzing Bayesian models and posterior distributions
- Describe and understand Bayesian models and posteriors using bayestestR
- Bayesian models in R
- Bayesian Statistics - A departure from Frequentist Statistics
- Variable selection using Gibbs sampling
- Analysis of Environmental Data Conceptual Foundations: Bayesian Inference
- Slides: Machine Learning - Bayesian Learning
- How Bayesian inference works part of the End-to-End Machine Learning library
- Inferência Bayesiana - Intuição e Exemplo
- stantargets - an extension to targets and cmdstanr for Bayesian data analysis
- https://michael-barr.com/datasci/bayesian-mbo?utm_source=rss&utm_medium=rss&utm_campaign=bayesian-mbo
- App Shiny ilustrando métodos de MCMC
- Common Probability Distributions: The Data Scientist’s Crib Sheet
- CheatSheet de distribuições de probabilidade
- JOHNSON TRANSFORMATION FOR NON-NORMAL DATA
- bestNormalize: Flexibly calculate the best normalizing transformation for a vector Travis-CI Build Status CRAN version
- Seeing Theory - site muito legal explicando as distribuicoes de probabilidade com graficos animados
- Análise de Sobrevivência Aplicada - Enrico e Suely
- Survival analysis with strata, clusters, frailties and competing risks in in Finalfit
- Survival Analysis - Fitting Weibull Models for Improving Device Reliability in R
- Survival Analysis - Fitting Weibull Models for Improving Device Reliability in R
- RNotebook
- Run R Online
- emo(ji) is to make it very easy to insert emoji into RMarkdown
- How to self-publish a book
- SoFIFA webcrawler and Machine Learning prediction
- Artigo sobre historia do R
- Operadores de Python e muito mais para R com Roperators
- O Índice Big Mac da Economist é calculado com R
- Agendador de tarefas
- DYSPLAYR - Using R to Create Free Online Dashboards
- Writing an R package from scratch
- Data Tidying - Visualizacao do tidyverse - imagens muito boas para apresentacao
- Datapasta allows you to copy and paste code into R
- askpass - login to RStudio
- 6 Reasons To Learn R For Business
- staplr - Este pacote fornece funções para manipular arquivos PDF
- giphyr - A R package for giphy API
- Simple Gantt charts in R with ggplot2 … and Microsoft Excel (cronograma para projetos)
- Hacking RStudio - useR Colin Fay
- Praise useRs when they have done something good, or when they just need it
- diffobj - Diffs for R Objects
- Why I use R - Gordon Shotwell
- Why Gordon Shotwell uses R
- Syntax Highlighting in Blogdown; a very specific solution
- deepdep - Visualize e explore profundas dependências de pacotes R
- Introducing portfoliodown: The Data Science Portfolio Website Builder
- flow - provides tools to visualize as flow diagrams the logic of functions, expressions or scripts and ease debugging.
- deepdep - Visualise and Explore Deep Dependencies of R Packages
- "R"eflexões um pouco de história e experiencias com R - Paulo Justiniano
- BETS - Brazilian Economic Times Series
- Introdução à Ciência de Dados com o R - UserR!
- A Era do Big Data: Como você e a sua empresa estão encarando essas mudanças?
- Apresentação Thiago - NMEC
- prR R Intro
- Top 10 TED Talks about data science
- 18 livros que valem mais que um doutorado em data science
- Learn R : 12 Free Books and Online Resources
- Data Science CheatSheet
- Command Line Tricks For Data Scientists
- Python vs (and) R for Data Science
- R vs Python: Which is better for Data Science?
- R vs Python: Usability, Popularity, Pros & Cons, Jobs, and Salaries
- When “learning Python” becomes “practicing R”
- Diferença entre Estatístico, Cientista de Dados, Engenheiro de Dados e Engenheiro de Software
- What frustrates Data Scientists in Machine Learning projects?
- R ou Python para Análise de Dados?
- Diário de um Cientista de Dados no Booking.com
- Slides - IA e o Futuro do Trabalho (Flavio Abdenur / SLQ)
- Entregando projetos de Machine Learning com Marvin-AI Parte 1
- THE R GRAPH GALLERY - ART FROM DATA
- Avoid These 5 Common Mistakes If You Want To Ace Data Science
- Storytelling with Data
- Este mapa alucinante explica como tudo na matemática está conectado
- The 5 Basic Statistics Concepts Data Scientists Need to Know
- 24 Data Science, R, Python, Excel, and Machine Learning Cheat Sheets
- The one critical skill many data scientists are missing
- Differences Between AI and Machine Learning and Why it Matters
- Best Practices for Administering RStudio in Production - Nathan Stephens
- A diferença real entre estatísticas e aprendizado de máquina
- Statistics versus machine learning
- Why GitHub Won't Help You With Hiring
- Matemática que você não precisa saber para aprendizado de máquina
- Statistical Mistakes Even Scientists Make
- R & stats illustrations by @allison_horst
- Not even scientists can easily explain p-values
- Creating your google chat R bot in under 5 minutes
- 170 projetos de DATA SCIENCE e MACHINE LEARNING com Python, resolvidos e explicados
- Is Data Scientist Still the Sexiest Job of the 21st Century?
- Selecting Forecasting Methods in Data Science
- garchmodels
- Automatic Forecasting with ahead::dynrmf and Ridge regression
- Livro R Avançado do Hadley Wickham
- Deep Learning Book
- Livro TextMining da Julia Silge
- Bibliografia datascience
- 3 livros sobre machine learning em portugues
- The Elements of Statistical Learning Data Mining, Inference, and Prediction
- Machine Learning for Text
- Mining of Massive Datasets
- Forecasting and Machine Learning: Principles and Practice
- Data Science Live Book
- An Introduction to Statistical and Data Sciences via R
- plotly for R
- An Introduction to Statistical Learning
- Data Science: Theories, Models, Algotirhms ans analytics
- 80+ Free Data Science Books
- Data Science Live Book
- HANDBOOK - Summary and Analysis of Extension Program Evaluation in R
- Fundamentals of Data Visualization
- Exploring Data Science
- Interpretable Machine Learning - A Guide for Making Black Box Models Explainable
- Reproducible finance with R
- Métodos Computacionais em Inferência Estatística 20ªSINAPE
- Feature Engineering and Selection: A Practical Approach for Predictive Models
- Machine Learning Algorithms From Scratch - link para comprar
- Mastering Software Development in R
- H2O Tutorials
- An Introduction to Statistical Learning - with Applications in R
- The Elements of Statistical Learning
- R Graphics Cookbook, 2nd edition
- Introduction to Econometrics with R
- Building Big Shiny Apps - A Workflow
- Free Book: Lecture Notes on Machine Learning
- The Little Book of LDA
- Ciencia de dados com R - Introdução - BPAD
- O Zen do R - Curso R
- Feature Engineering and Selection: A Practical Approach for Predictive Models
- Mastering Shiny - Joe Cheng
- Hands-On Machine Learning with R
- Creating APIs in R with Plumber
- Shiny Production with AWS Book
- YaRrr! The Pirate’s Guide to R
- Mastering Shiny - Hadley Wickham
- Princípios de Modelagem Preditiva - andlima.github.io
- CS 109B: Advanced Topics in Data Science - Harvard
- Course Notes for IS 6489, Statistics and Predictive Analytics
- Curso Avançado em Estatística com R da UFFS
- Análise Explicativa do Modelo - Explore, explique e examine modelos preditivos
- Curso-R Ciência de Dados em R
- Dive into Deep Learning
- Bayes Rules! An Introduction to Bayesian Modeling with R
- Aprendizado de máquina: uma abordagem estatística
- Big Book of R - Lista curada de links para livros relacionados à R
- Modern Data Science with R
- Practical Data Science - Doing more with your data
- Modern Data Science with R
- Bayes Rules! An Introduction to Bayesian Modeling with R
- Introduction to Data Science - Data Analysis and Prediction Algorithms with R
- An Introduction to Statistical Learning
- Dos estatísticos, estatísticas
- THE ART OF MACHINE LEARNING Algorithms+Data+R - by Norm Matloff
- Introduction to Econometrics with R
- R for Geographic Data Science
- Aprendizado de Máquina - Uma abordagem estatística
- Tidy Finance with R
- Introduction to Probability for Data Science
- A Short Chronology Of Deep Learning For Tabular Data
datasummary: Crosstabs, frequencies, correlations, balance (a.k.a.- Amostragem: Teoria e Prática Usando R
- Imbalanced Binary Classification - A survey with code
- Bayesian Optimization
- Flexible Imputation os Missing Data
- Plano de estudos em machine learning com conteúdos em português
- FOUNDATIONS OF MACHINE LEARNING - DEEP UNDERSTANDING OF THE CONCEPTS, TECHNIQUES AND MATHEMATICAL FRAMEWORKS USED BY EXPERTS IN MACHINE LEARNING - at Bloomberg
- Introdução em profundidade ao aprendizado de máquina em 15 horas de vídeos especializados
- OneDrive com conteúdos de Machine Learning
- Curso do Google
- Practical Machine Learning with R and Python – Part 1
- mlr: Machine Learning in R
- A visual introduction to machine learning par I
- A visual introduction to machine learning par II
- Dataaspirant - Posts by Rahul Saxena - Blog legal sobre Machine Learning
- Grupos de Estudos de Machine Learning da USP
- Machine Learning tips and tricks cheatsheet
- stanford.edu - Machine Learning
- Explaining Black-Box Machine Learning Models
- FOUNDATIONS OF MACHINE LEARNING - Bloomberg ML EDU
- Dicas e truques de aprendizado de máquina - MIT stanford.edu em PORTUGUES
- Machine Learning Black Friday Dataset - Explicações rf, gbm, pca,
- The 25 Best Data Science and Machine Learning GitHub Repositories from 2018
- automl package: part 2/2 first steps how to
- Material de machine learning
- Quando Bayes, Ockham e Shannon se unem para definir o aprendizado de máquina
- Modelagem Avançada (em R) - Encontre o melhor modelo preditivo usando o pacote R / caret / modelgrid
- 14 tipos diferentes de aprendizado no aprendizado de máquina
- A Tour of Machine Learning Algorithms
- SuperML is to provide sckit-learn's fit,predict,transform standard way of building machine learning models in R
- tidymodels - logistic regression
- Curso-R workshop - XGBoost com tidymodels
- Customer Churn Modeling using Machine Learning with parsnip
- Iterative Bayesian optimization of a classification model
- Tutorial on tidymodels for Machine Learning
- Tune XGBoost with tidymodels and #TidyTuesday beach volleyball
- PCA and UMAP with tidymodels and #TidyTuesday cocktail recipes
- A Gentle Introduction to tidymodels
- Tunando seu modelo preditivo no R com o pacote {dials}
- Use racing methods to tune xgboost models and predict home runs
- Bootstrap in R
- Cross-Validation: Concept and Example in R
- Beyond normality: the bootstrap method for hypothesis testing
- UNDERSTAND EMPLOYEE CHURN USING H2O MACHINE LEARNING AND LIME
- INTERPRETABLE MACHINE LEARNING ALGORITHMS WITH DALEX AND H2O
- IML AND H2O: MACHINE LEARNING MODEL INTERPRETABILITY AND FEATURE EXPLANATION
- Efficient Machine Learning in H2O with R and Python, Part 1
- Finally, You Can Plot H2O Decision Trees in R
- Hidden Markov Model example in r with the depmixS4 package
- Bayesian Network Example with the bnlearn Package
- Introduction to Bayesian Thinking: from Bayes theorem to Bayes networks
- Logistic Regression. Simplified.
- LM - Machine learning fundamentals (I): Cost functions and gradient descent
- What Is A Decision Tree Algorithm?
- Chapter 5: Random Forest Classifier
- Machine Learning for Humans, Part 4: Neural Networks & Deep Learning
- Introduction to Bayesian Thinking: from Bayes theorem to Bayes networks
- Chapter 2 : SVM (Support Vector Machine) — Theory
- Gradient Boosting from scratch
- Introduction to k-Nearest-Neighbors
- Regularization in Machine Learning
- Linear, Quadratic, and Regularized Discriminant Analysis
- A Tour of Machine Learning Algorithms
- Dicas de aprendizado supervisionado - MIT stanford.edu em PORTUGUES
- sparklyr: supervised learning
- AdaBoost, Clearly Explained - video youtube
- 6 Easy Steps to Learn Naive Bayes Algorithm with codes in Python and R
- Visualizando Resíduos
- Linear Model Selection
- O LASSO
- A comprehensive beginners guide for Linear, Ridge and Lasso Regression
- Going Deeper into Regression Analysis with Assumptions, Plots & Solutions
- The Lasso Page - L1-constrained fitting for statistics and data mining
- Regularization: Ridge Regression and the LASSO
- Seleção de modelos usando o pacote glmulti
- Intuition behind Bias-Variance trade-off, Lasso and Ridge Regression
- 15 TIPOS DE REGRESSÃO QUE VOCÊ DEVE SABER
summ- Tools for summarizing and visualizing regression models- Regularização: Ridge, Lasso e Elastic Net - DataCamp Tutorials
- REGRESSÃO LOGÍSTICA: ASPECTOS COMPUTACIONAIS - COM TENSORFLOW
- Empurrando os Mínimos Quadrados Ordinários até o limite com Xy()
- Intuitive Machine Learning : Gradient Descent Simplified
- Quantile Regression in Python
- Basic quantile regression in R
- What is logistic in the logistic regression?
- Regularization Part 2: Lasso Regression - video youtube
- Regularization Part 3: Elastic Net Regression - video youtube
- Robust and Resistant Regression - artigo
- 3 Types of Regression in One Picture
- Behind the Scenes with Lasso
- Difference Between Correlation and Regression in Statistics
- Logistic Regression in One Picture
- Implementing the Gradient Descent Algorithm in R
- How To Interpret R-squared and Goodness-of-Fit in Regression Analysis
- Fitting penalized regressions - In R package {bigstatsr}, you can fit efficient penalized (linear and logistic) regressions using functions big_spLinReg() and big_spLogReg().
- Understanding Lasso and Ridge Regression
- Tutorial: Poisson Regression in R
- A Shiny app for simple linear regression by hand and in R
- lmSubsets: Exact variable-subset selection in linear regression
- Machine Learning with R: A Complete Guide to Linear Regression
- 7 Classical Assumptions of Ordinary Least Squares (OLS) Linear Regression
- Interpreting Generalized Linear Models
- Descida de Gradiente para Regressão Logística Simplificada - Guia Visual Passo a Passo
- Curso: GAMs in R: by Noam Ross
- Explaining Logistic Regression as Generalized Linear Model (in use as a classifier)
- blorr package offers tools for building and validating binary logistic regression models
- Introdução aos Modelos Mistos no RPubs muito boa
- A Practical Guide to Mixed Models in R
- Mixture modelling from scratch, in R
- Compare Models And Select The Best Using The Caret R Package
- Critérios de Seleção de Modelos
- Illustrated Guide to ROC and AUC - Plot da curva ROC
- Machine Learning Evaluation Metrics in R
- Plot da matriz de confusão
- Gain Curve
- Gain Curve interpretation
- Confusion between caret randomForest predict() results and reported model performance
- Introduction to modelplotr
- yardstick is a package to estimate how well models are working
- Uma explicação visual para função de custo “binary cross-entropy” ou “log loss”
- Artigo sobre curva ROC
- Critérios de Seleção de Modelos
- DALEX - Descriptive mAchine Learning EXplanations
- DALEX for Multi Layer Perceptron with H2O and Keras
- xaibot - conversas com modelos preditivos!
- Descriptive mAchine Learning EXplanations https://pbiecek.github.io/DALEX/
- How to use DALEX with parsnip
- A gentle introduction to tidymodels
- Model evaluation audit
- auditor: uma visita guiada através de resíduos
- ROC Curve Example Using Base R
- modelStudio e a gramática da análise interativa de modelos explicativos
- Scott Lundberg, Microsoft Research - Explainable Machine Learning with Shapley Values - Youtube
- Book - Hands-on Machine Learning with R - Chapter 16 - Interpretable Machine Learning
- gist - SHAP_with_H2O_AutoML.R
- 11 Important Model Evaluation Metrics for Machine Learning Everyone should know
- fairmodels: a Flexible Tool for Bias Detection, Visualization, and Mitigation in Binary Classification Models
- Deep Learning para Sistemas de Recomendação (Parte 1) — Introdução
- Dataset obtido em grouplens: MovieLens
- Non-negative matrix factorization
- Item-item collaborative filtering
- Recommender system
- k-nearest neighbors algorithm
- Knowledge-based recommender system
- Cluster analysis
- Pearson correlation coefficient
- Cosine similarity
- Bayesian network
- Hidden Markov model
- Collaborative filtering
- Uma nova métrica para calcular relevância de busca
- Using 3D visualizations to tune hyperparameters in ML models
- Como implementar otimização bayesiana do zero em Python
- Using Bayesian Optimization to reduce the time spent on hyperparameter tuning
- Seleção de recursos em R com o pacote Boruta R
- Boruta explained exactly how you wished someone explained to you
- Feature Selection using Genetic Algorithms in R
- https://www.business-science.io/code-tools/2021/02/16/power-score-vs-correlation-funnel.html
- https://www.analyticsvidhya.com/blog/2016/03/select-important-variables-boruta-package/
- https://www.machinelearningplus.com/machine-learning/feature-selection/
- SAFE - Simplify your model: Supervised Assisted Feature Extraction for Machine Learning
- Tree-based Methods - lagunita.stanford.edu (Muito Bom)
- Decision Trees in R
- Parallel Gradient Boosting Decision Trees
- Um tutorial completo sobre a modelagem baseada em Tree (Árvore) do Zero (em R & Python)
- BooST (Boosting Smooth Trees) a new Machine Learning Model for Partial Effect Estimation in Nonlinear Regressions
- Tunning XGBOOST in R: Part I
- Tuning xgboost in R: Part II
- Parallel Gradient Boosting Decision Trees
- Um guia de ponta a ponta para entender a matemática por trás do XGBoost
- User written splitting functions for RPART - cran.r-project.org
- Tips for data science competitions - ALL XGBoost
- Mastering The New Generation of Gradient Boosting - CatBoost
- PDF PUC RIO - Árvore de Decisão
- Coding Regression trees in 150 lines of R code
- Luz na Aprendizagem de Máquina de Matemática: Guia Intuitivo para Entender Árvores de Decisão
- Machine Learning Basics - Gradient Boosting & XGBoost
- How to use DALEX with the xgboost models
- The Complete Guide to Decision Trees
- ggparty - ggplot2 visualizations for the partykit package - conditional trees
- Ensemble methods: bagging, boosting and stacking
- Regularization Methods in Random Forests
- Codificando Florestas Aleatórias em 100 linhas de código em R *
- Explaining Predictions: Random Forest Post-hoc Analysis (randomForestExplainer package)
- Network model trees
- FFTrees - plot elegante dos resultados da arvore de decisao
- A Kaggle Master Explains Gradient Boosting
- autoxgboost: Automatic XGBoost using Bayesian Optimization
- ggparty: Graphic Partying - ggparty aims to extend ggplot2 functionality to the partykit package. It provides the necessary tools to create clearly structured and highly customizable visualizations for tree-objects of the class 'party'.
- UM ESTUDO SOBRE OS CLASSIFICADORES GRADIENT BOOSTING
- ggRandomForests - ALL YOUR FIGURE ARE BELONG TO US
- stackgbm offers a minimalist implementation of model stacking for gradient boosted tree models built by xgboost, lightgbm, and catboost.
- slides xgboost teoria e passo a passo - curso r
- Fast Gradient Boosting with CatBoost - kdnuggets
- Understanding XGBoost Algorithm In Detail
- Understanding Linear SVM with R
- SUPPORT VECTOR MACHINE CLASSIFIER IMPLEMENTATION IN R WITH CARET PACKAGE
- Learn Support Vector Machine (SVM) from Scratch in R
- Dicas de aprendizado não supervisionado - MIT stanford.edu em PORTUGUES
- Self Organizing Maps in R | Kohonen Networks for Unsupervised and Supervised Maps
- Visualize K-Means Clustering on a Single Vector
- Guia de iniciantes para aprender técnicas de redução de dimensão
- Factoextra R Package: Easy Multivariate Data Analyses and Elegant Visualization
- Comprehensive Guide on t-SNE algorithm with implementation in R & Python
- How to Use t-SNE Effectively
- Dimensionality Reduction Methods: PCA, t-SNE, SOM
- Análise fatorial em R
- Otimo TCC - ANÁLISE FATORIAL E UMA APLICAÇÃO EM PERFIL DE COMPRAS DE PEQUENOS VAREJISTAS
- Articles - Principal Component Methods in R: Practical Guide
- Principal Component Analysis 4 Dummies: Eigenvectors, Eigenvalues and Dimension Reduction
- Interpretar os principais resultados para Análise de componentes principais - Minitab
- Dissecando Análise de Componentes Principais
- MCA - Multiple Correspondence Analysis in R: Essentials
- PCA vs Autoencoders for Dimensionality Reduction
- Factoextra R Package: Easy Multivariate Data Analyses and Elegant Visualization
- 5 functions to do Principal Components Analysis in R
- Intuition for principal component analysis (PCA)
- Linear, Quadratic, and Regularized Discriminant Analysis
- Spectral clustering - The intuition and math behind how it works!
- YOUTUBE - Principal Component Analysis (PCA)
- 10 Tips for Choosing the Optimal Number of Clusters - visualizações muito boas
- K-Prototype in Clustering Mixed attributes
- Artigo - Visualizing Data using t-SNE
- Introduction to t-SNE - datacamp
- How to Automatically Determine the Number of Clusters in your Data - and more
- Spectrum - Spectrum is a fast self-tuning spectral clustering method for single or multi-view data
- Como determinar automaticamente o número de clusters nos seus dados - e mais
- Determining The Optimal Number Of Clusters: 3 Must Know Methods
- Introdução à Análise de Componentes Principais
- uwot umap - An R package implementing the UMAP dimensionality reduction method.
- Determining Number of Clusters in One Picture
- explor - é um pacote R para permitir a exploração interativa de resultados de análise multivariada.
- Quick and easy t-SNE analysis in R
- 3 distances that every data scientist should
- The essence of Eigenvalues and eigenvectors in Machine Learning
- Tudo o que você precisa fazer com o PCA é em Factoshiny!
- Principal Component Analysis vs. Linear Discriminant Analysis
- modelDown: um gerador de website para seus modelos preditivos
- Why ROC curves are a bad idea to explain your model to business people - Intro modelplotr
- Animations with receiver operating characteristic curve (ROC curve)
- Exploring Models with lime
- ROC Curve Explained in One Picture
- Visualizing ML Models with LIME
- shapper is on CRAN, it’s an R wrapper over SHAP explainer for black-box models
- ROC Curve Example Using Base R
- h3r - Biblioteca de indexação geográfica h3 do Uber
- Geocoding with ggmap and the Google API - Map Porn
- geobr: Loads Shapefiles of Official Spatial Data Sets of Brazil
- Interactive visualization of large-scale spatial data sets in R
- yutannihilation/ggsflabel - Labels for 'sf' with 'ggplot2'
- leaflet extensions for mapview
- r5r: Rapid Realistic Routing with R5 in R
- Select points on maps
- Recording and Measuring Your Musical Progress with R
- Introduction to the chorrrds package
- Rspotify: Access to Spotify API
- Microsoft r open 3.5 está disponível
- Using Microsoft R Open with RStudio
- Creating and saving multiple plots to Powerpoint
- Macro para Word para formatar todas as tabelas
- Por que o R é melhor que o Excel?
- Dicas para analisar dados do Excel em R
- How To Use R With Excel
- BERT - Basic Excel R Toolkit
- Anomaly Detection in R – The Tidy Way - Outliers??
- Spatial outliers detection in R?
- Técninca para detectar outliers - Minimum Regularized Covariance Determinant Estimator
- Detectando outliers com heatmap
- Detectando valores faltantes
- Z-test
- INFORMATION SECURITY: ANOMALY DETECTION AND THREAT HUNTING WITH ANOMALIZE
- ANOMALIZE: TIDY ANOMALY DETECTION
- INFORMATION SECURITY: ANOMALY DETECTION AND THREAT HUNTING WITH ANOMALIZE
- Twitter Anomaly Detection
- Z-Score: Definição, Fórmula e Cálculo
- Amelia e Boruta
- CRAN’s New Missing Data Task View
- Function that implements SMOTE (synthetic minority over-sampling technique)
- Introduction to Outlier Detection Methods
- Visualize Missing Data with VIM Package
- Parece bom - Getting Started with naniar
- Detectando outliers com heatmap
- Detectando valores faltantes
- Outlier Detection with Extended Isolation Forest
- How to Scale Data With Outliers for Machine Learning
- Tratar dados faltantes com random forest e tidymodels
- kableExtra
- HighCharter
- Plotly
- dygraphs for R
- plumber - converte seu código R existente em uma API da Web
- testthat e livro do hadley para isso
- Top 20 R Libraries for Data Science in 2018 [Infographic]
- Bibliotecas de Data Science em Python, R e Scala
- cronR - Agendar scripts/processes
- mlr: Machine Learning in R e MlrCheatsheet
- data.table
- TIBBLETIME
- purrr tutorial
- modelDown: um gerador de website para seus modelos preditivos
- BETS
- DBPLRY
- googleLanguageR
- future.apply – Parallelize Any Base R Apply Function
- imager
- poweRlaw
- janitor
- gamlss - Generalized Additive Models for Location, Scale and Shape
- yardstick is a package to estimate how well models are working
- Gráficos do ggplot2 sem programar com equisse
- finalfit package provides functions that help you quickly create elegant final results
- bestNormalize: Flexibly calculate the best normalizing transformation for a vector Travis-CI Build Status CRAN version
- needs is a simple R package to make package loading / installation hassle-free
- progress - Progress bar in your R terminal
- stressR - A set of stress tests for CPU, memory, and hdd, callable by R
- memuse - An R package of utilities for benchmarking and optimization
- checkpoint - Install R packages from snapshots on checkpoint-server
- emayili: Sending Email from R
- fastmap - Fast map implementation for R
- apexcharter - Htmlwidget for apexcharts.js : A modern JavaScript charting library to build interactive charts and visualizations with simple API.
- Fast Disk-Based Parallelized Data Manipulation Framework for Larger-than-RAM Data https://diskframe.com
- peep - R's default head or tail functions print 6 or so rows and all columns.
- R Markdown: The Definitive Guide
- Conteúdo para aprender RMarkdown
- Template pretydoc
- Template rticles
- Steve's R Markdown Templates
- Citação style languages
- Rmarkdown templates
- Modelo-LaTeX-IFSul
- Alternative to Latex for High Quality Reports with RMarkdown
- Template para formatação de Tese da ESALQ em Rnw
- Templates for R Markdown
- Creating a basic template package in R
- Remedy - A package for easier Markdown writing
- Alternative to Latex for High Quality Reports with RMarkdown
- Pimp my RMD: a few tips for R Markdown
- Distill for R Markdown Scientific and technical writing, native to the web
- posterdown - Use RMarkdown to generate PDF Conference Posters via HTML
- myprettyreport - Detailed introduction of "myprettyreport" R package
- Welcome to reportfactory! - Lightweight Infrastructure for Handlling Multiple R Markdown Documents
- Building a Daily Bitcoin Price Tracker with Coindeskr and Shiny in R
- Como monitorar o uso do aplicativo no Shiny Server Open Source
- RINNO: FULL STACK DATA SCIENCE MEETUP
- Shiny Template
- Show me Shiny - SQL CONNECTION
- shinyjqui - jQuery - UI Interactions and Effects for Shiny
- shinyWidgets : Extend widgets available in shiny
- The R Shiny packages you need for your web apps!
- Semantic dashboard - new open source R Shiny package
- Create outstanding dashboards with the new semantic.dashboard package
- Streaming Data
- Current Time
- AdminLTE - shiny widgets examples
- Awesome dashbords
- Dashboardthemes
- Three R Shiny tricks to make your Shiny app shines (1/3)
- Three R Shiny tricks to make your Shiny app shines (2/3): Semi-collapsible sidebar
- Three R Shiny tricks to make your Shiny app shines (3/3): Buttons to delete, edit and compare Datatable rows
- Shiny tips & tricks for improving your apps and solving common problems
- shinyWidgets Overview
- github - dreamRs/shinyWidgets
- Shiny DND
- Machine Learning Calculator: Bias/Variance Tradeoff - Model Fit Comparison.
- Add more interactivity to interactive charts - manipulateWidget
- Modularizing Shiny app code
- Our Package template to design a prod-ready Shiny application
- dashboardthemes v1.0.1 - github - custom theme support for R Shinydashboard applications.
- dashboardthemes v1.0.1 - Wordpress -
- ADICIONE JAVASCRIPT, CSS E HTML PERSONALIZADOS EM SHINY
- ShinyShortcuts
- ShinyMaterial - Material design in Shiny apps
- Announcing the 1st Shiny Contest - Para inspiração
- waypointer - Simple animated waypoints for shiny
- Styling DataTables
- Stencilled - Download and Email reports in R Shiny app
- rintrojspacote - R integra o Intro.js ao Shiny, para que os usuários possam adicionar facilmente instruções ao seu aplicativo sem precisar usar HTML, CSS ou JavaScript
- Add tex path to RStudio - Ubuntu - StackOverFlow
- 2 ways to permanently set $PATH variable in ubuntu
- yihui/tidytex - Github - issue - pdflatex not found
- yihui/tidytex - Github - issue - Shiny server in Docker #34
- R Sweave: NO TeX installation detected - StackOverFlow
- Building An R Shiny App On Google Cloud To Display BigQuery Data
- How I implemented googleSignIn in R (shiny) and lived
- Shiny Apps for Interactive Data Analysis - Exemplo de Shiny muito bom
- countdown - Contagem regressiva em um simples cronômetro de contagem regressiva em slides e documentos HTML escritos em R Markdown.
- golem - É um pacote que fornece ferramentas para um melhor fluxo de trabalho para trabalhar em shinyapps
- Lorem-Ipsum-like - Helpers for fast Shiny Prototyping
- fakir - Create Fake Data in R
- shinysnippets - The goal of shinysnippets is to Installation of snippets
- Intro Shiny Modules
- Building Big Shiny Apps — A Workflow – 1/2
- Building Big Shiny Apps — A Workflow 2/2
- Our Package template to design a prod-ready Shiny application
- Shiny com Painel de Fundos de Renda Fixa
- Getting started with the manipulateWidget package (quase um grid.arrange() para shiny)
- shinytoastr - Notifications in Shiny, via toastr
- shinyF7 - shiny API for Framework7 (IOS/android)
- reactlog - Mostra o percurso dos reativos no aplicativo
- Bookmarking state: users can save the state of an application and get a URL which will restore the application with that state
- shinyglide -shinyglide is an R package which provides carousel-like or assistant-like components to shiny applications
- Give httr::progress the ability to talk to shinyWidgets::progressBar.
- shinymeta — a revolution for reproducibility
- What NOT to do when building a shiny app (lessons learned the hard way)
- polished - Authentication and Administration for Shiny apps https://polished.tychobra.com/
- bsplus - provide a framework to use Bootstrap’s JavaScript-markup API for shiny: http://getbootstrap.com/javascript/
- bulma.io for Shiny. Contains extensions: bulma-extensions as well as themes
- reacttable - Interactive data tables for R, based on the React Table library and made with reactR
- Awesome Shiny Extensions - curated list
- shinymobile - miniUI 2.0 brings new inputs for iOs and android
- MobileTrigger Setup: Run R Scripts, Models, Reports with Mobile Device
- vov.css animations for shiny
- reactor - reactor is a proof-of-concept for an https://observablehq.com/ like experience powered by Shiny's built-in reactive expressions. (Parece um jupyter notebook feito em shiny)
- Drag-and-Drop in shiny Apps with SortableJS
- shinymanager - Simple and secure authentification mechanism for single ‘Shiny’ applications. Credentials are stored in an encrypted ‘SQLite’ database.
- shinyFeedback - R package for displaying user feedback alongside Shiny inputs
- Shiny App ideas
- Shiny Basics Introduction
- Shiny.admin – user management and usage statistics
- howler - Interactive Audio Player
- bslib - provides tools for customizing Bootstrap themes directly from R
- R Shiny Dashboard Templates From Appsilon Are Now Available
- Visualising US Voting Records with shinydashboard
- Boas práticas de dashboards ShinyDashboards para Finanças
- bs4Dash - Bootstrap 4 shinydashboard using AdminLTE3
- argonR - argonDash - Argon dashboard template
- Deploying a secure Shiny Server and RStudio Server on a free Google Cloud virtual machine
- ShinyProxy - nova plataforma de código aberto para implantar app para empresa ou organizações maiores.
- Instalando ShinyServer no linux
- ShinyProxy in a container
- Nosso modelo de pacote para criar um shinyapp pronto para produção
- Instalação do R, Rstudio-server, shiny-server, nginx, ssl e autenticação do usuário do shinyapp no Ubuntu 16.04.
- Using Cookie Based Authentication with Shiny
- Apresentação Rodrigo Casa&Video - Shiny em produção
- Adding Authentication to Shiny Server in 4 Simple Steps
- Authentication and database - Shiny
- nginx default public www location - StackOverFlow
- xameeramir/default nginx configuration file - Github
- Inicie o RStudio Server no Google Cloud com duas linhas de R
- Configuração e utilização do RStudio e Shiny Server na AWS
- Business Science University - Exemplo de galeria de aplicativos
- Beginner’s guide to use docker (Build, Run, Push and Pull)
- R Docker tutorial
- rize - A robust method to automagically dockerize your R Shiny Application
- Super basic practical guide to Docker and RStudio
- Dockerizando Shiny Apps
- YOUTUBE: Docker + Banco de Dados: Descomplicando a montagem de ambientes (Desenvolvimento e Testes)
- rocker-org/rocker - Using the RStudio image
- github: rocker-org/rocker-versioned com latex
- How To Dockerize R Shiny App — Part 1
- How To Dockerize an R shiny App — Part 2
- Hospedando seu shiny app no now com docker
- Dockeriser une application Shiny - ThinkR
- docker_shiny-server_centos7/Dockerfile - Github
- Shiny Server on Docker: CentOS 7 Edition
- Lançamento OpenCPU 2.1: serviços escalonáveis
- CONTAINERS ARE NOT VMS
- Running your R script in Docker
- Docker para cientistas de dados (uma das principais habilidades para 2020)
- Deploying a Shiny Flexdashboard with Docker
- docknitr: Using Docker in Rmarkdown - Ben Artin
- O que é o pipe:
%>%? - Pipe para R
- Pipe para Python
- Comparativo R e Python
- Por que R para ciência de dados - e não Python?
- How to Learn Python in 30 days
- The reticulate package solves the hardest problem in data science: people
- rminiconda
- Configurando o VS Code para desenvolvimento Python como o RStudio
- Why you should use siuba, the dplyr of Python
- Jupyter Black [Black formatter for Jupyter Notebook] - identa o codigo automaticamente
- Plotnine: Grammar of Graphics for Python
- Data-science? Agile? Cycles? My method for managing data-science projects in the Hi-tech industry.
- Structuring R projects
- ProjectTemplate - ProjectTemplate é um sistema para automatizar as partes impensadas de um projeto de análise de dados:
- Cookiecutter Data Science - A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
- UMA TESE SÃO “SÓ” 5 COISAS…
- Construindo uma rede neural a partir do zero em R
- An Introduction to Recurrent Neural Networks
- neuralnet: Train and Test Neural Networks Using R
- Everything you need to know about AutoML and Neural Architecture Search
- A Eficácia Irrazoável de Redes Neurais Recorrentes
- How to build your own Neural Network from scratch in Python
- YOUTUBE - Backpropagation calculus | Deep learning, chapter 4
- TL-GAN: transparent latent-space GAN - interface para alterar rostos com GAN
- “GANs” vs “ODEs”: the end of mathematical modeling?
- A Recipe for Training Neural Networks
- Introduction to Turing Learning and GANs
- How do Graph Neural Networks Work?
- Hacker's guide to Neural Networks
- One LEGO at a Time: Explaining the Math of how Neural Networks Learn with Implementation from Scratch
- NEW RSTUDIO ADDINS FOR NETWORK ANALYSIS
- R Neural Network - (Data Science, Machine Learning and Predictive Analytics)
- Matrix Multiplication in Neural Networks - COlor Guided Matrix Multiplication for Binary Classification Task with N = 4
- Dicas de aprendizado profundo - MIT stanford.edu em PORTUGUES
- Feedforward Deep Learning Models
- Deep Learning Book
- Deep Learning with R - Keras and TensorFlow
- Image-to-image translation with pix2pix Conditional GANs (cGANs)
- A gentle introduction to OCR
- Awesome Deep Learning links
- R vs Python: Image Classification with Keras
- It's that easy! Image classification with keras in roughly 100 lines of code.
- Deep Learning for the Masses (and The Semantic Layer)
- GAN — Some cool applications of GANs.
- Generative Adversarial Networks (GANs) — A Beginner’s Guide
- Aprendizagem Profunda Intuitiva Parte 1a: Introdução às Redes Neurais
- Aprendizagem Profunda Intuitiva Parte 1b: Introdução às Redes Neurais
- Por que o GEMM está no centro do aprendizado profundo
- Are Deep Neural Networks Dramatically Overfitted?
- Convolutional Neural Networks (CNN) Simplified (Part 4) (Com vídeo!)
- R interface to Keras
- Building Convolutional Neural Networks with Tensorflow
- Tesla’s Deep Learning at Scale: Using Billions of Miles to Train Neural Networks
- Deep Learning Models - A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks
- An End to End Introduction to GANs
- Afinal, o que é Deep learning?
- R Markdown Notebooks for "Deep Learning with R
- Neural Style Transfer com Torch
- Computer vision basics in Microsoft Excel
- How to build your own image recognition app with R! [Part 2]
- Semelhanca de imagens - Jogo dos sete erros com visao computacional
- Reconhecimento facial com R
- Analisando face emotions no R
- Human Face Detection with R
- GFPGAN aims at developing a Practical Algorithm for Real-world Face Restoration
- Detecção de objetos com 10 linhas de codigo
- How to build your own image recognition app with R! [Part 2]
- THE TIDY TIME SERIES PLATFORM: TIBBLETIME 0.1.0
- Forecasting Using a Time Series Signature with timetk
- Análise e simulação de investimentos com o pacote calcCidadao
- DEMO WEEK: TIDY TIME SERIES ANALYSIS WITH TIBBLETIME
- TIBBLETIME
- Introduction to Forecasting with ARIMA in R
- Time Series Analysis using R
- arimax: Fitting an ARIMA model with Exogeneous Variables
- Time series shootout: ARIMA vs. LSTM (talk)
- Decomposition-Based Approaches to Time Series Forecasting
- A Bayesian Approach to Time Series Forecasting
- Everything you can do with a time series
- sweep: Extending broom for time series forecasting
- Demo Week: Time Series Machine Learning with timetk
- 7 ways to time series - From Machine Learning To Time Series Forecasting
- TSstudio 0.1.2 - ferramentas para análise descritiva e preditiva de dados de séries temporais interativo
- Introduction to optimal changepoint detection algorithms
- How a Kalman filter works, in pictures
- ggseas - seasonal adjustment on the fly extension for ggplot2
- Understanding LSTM Networks
- First experience of building a LSTM model with TensorFlow
- Time Series Machine Learning (and Feature Engineering) in R (tidymodels) - Business Science
- Machine Learning (XGBoost) Time-Series Classification Trading Strategy
- Advanced Time Series Analysis - with TSsudio
- Simple Time Series Forecasting Models to Test So That You Don’t Fool Yourself
- How DTW (Dynamic Time Warping) algorithm works - Youtube
- Welcome to the Dynamic Time Warp project! - Comprehensive implementation of Dynamic Time Warping algorithms in R and Python
- Slidex - converter ppt para rmd
- Apresentation Ninja with Xaringan
- Kunoichi ⚔くノ一 A xaringan theme for the R-Ladies ninja
- xaringan template with logo on all slides
- Flipbooks - Flipbooks present code step-by-step and side-by-side with its output.
- praise - Praise Users- Build friendly R packages that praise their users if they have done something good, or they just need it to feel better.
- R Markdown: How to number and reference tables
- R xlsx package : A quick start guide to manipulate Excel files in R
- How to Make Beautiful Tables in R
- I am very happy to announce dataui (source,site,JavaScript library) for the R sparkline lovers.
- reactablefmtr
datasummary: Crosstabs, frequencies, correlations, balance (a.k.a.
- TensorSpace.js - Neural network 3D visualization framework (playgroud)
- Experimenting with autoregressive flows in TensorFlow Probability
- Tadpoles on TensorFlow: Hierarchical partial pooling with tfprobability
- R Tensorflow Multiple Linear Regression
- WordCloud positive - negative words
- Mining twitter with R - Guia de Nuvem de Palavras
- Livro TextMining da Julia Silge
- Summarizing Web Articles with R using lexRankr
- Tidy Sentiment Analysis in R
- Word2vec baby step in deep learning but leap towards NLP
- Analise sentimentos com dados do spotify
- What is sentiment analysis
- googleLanguageR
- Conectando R com o Twitter parte 1
- A guide to working with character data in R
- Machine Learning and NLP using R: Topic Modeling and Music Classification
- Introduction to googleLanguageR
- crfsuite for natural language processing
- remove emoji from string in R
- qdapRegex is a collection of regex tools
- function
rm_emoticon(): Remove/Replace/Extract Emoticons - Emoticons decoder for social media sentiment analysis in R
- Practicing sentiment analysis with Harry Potter
- ASSOCIAÇÕES DE PALAVRAS DO SMALL WORLD OF WORDS - (AED e GLM)
- TENSORFLOW, JANE AUSTEN, AND TEXT GENERATION
- TRAINING, EVALUATING, AND INTERPRETING TOPIC MODELS
- markovifyR - gerador de texto com cadeias de markov
- TEXT CLASSIFICATION WITH TIDY DATA PRINCIPLES
- WORD ASSOCIATIONS FROM THE SMALL WORLD OF WORDS
- RPubs - Text-Mining with rvest and qdap
- Distributed Representations of Words and Phrases and their Compositionality - artigo word2vec google
- Word2Vec Experiments - telesens
- Training and Visualising Word Vectors
- The amazing power of word vectors
- Making sense of word2vec
- Lecture 2 | Word Vector Representations: word2vec - Video do youtube Stanford University School of Engineering
- SMART Information Retrieval System - Wikipedia
- textfeatures - Easily extract useful features from character objects.
- Análise de dados: Latent Dirichlet Allocation (LDA) Aplicada em Textos Jornalistícos
- d3wordcloud - d3wordcloud is a wrapper for the Word Cloud Layout by Jason Davies based on htmlwidgets
- REORDERING AND FACETTING FOR GGPLOT2 - Julia Silge
- quanteda - COLLOCATION ANALYSIS
- Training, evaluating, and interpreting topic models
- Topic modeling with stm package - 1 - Julia Silge
- Topic modeling with stm package - 2 - Julia Silge
- A Friendly Introduction to Text Clustering
- Topic Modeling On Twitter Using Sentence BERT
- Topic Modeling with BERT
- Bert For Topic Modeling ( Bert vs LDA )
- Using LDA Topic Models as a Classification Model Input
- Seeded Topic Models as a Yard Stick: Implement them in R with keyATM
- cleanNLP - R package providing annotators and a tidy data model for natural language processing
- Build a Simple Cosine Similarity Search Engine in R
- Build a search engine in 20 minutes or less
- How to get started with Word2Vec — and then how to make it work
- Latent semantic analysis
- Stop Using word2vec
- Word Tensors
- WORD VECTORS WITH TIDY DATA PRINCIPLES - Julia Silge
- GloVe vs word2vec revisited
- Introducing text2vec 0.4
- UDPipe Introduction
- You did a sentiment analysis with tidytext but you forgot to do dependency parsing to answer WHY is something positive/negative
- ruimtehol: R package to Embed All the Things! using StarSpace
- udpipe version 0.7 for Natural Language Processing (#NLP) alongside #tidytext, #quanteda, #tm
- Uma visão geral das técnicas de extração de palavras-chave
- Textrank for summarizing text
- Exploring the State of the Union Addresses: A Case Study with cleanNLP
- Modelos de tópicos BTM: Biterm para texto curto
- crfsuite - Conditional Random Fields for NLP
- awesome-nlp - Awesome - A curated list of resources dedicated to Natural Language Processing
- How to solve 90% of NLP problems: a step-by-step guide
- Introduction to Amazon SageMaker Object2Vec
- COTA: Improving Uber Customer Care with NLP & Machine Learning - Uber Engineering
- Projecto Floresta Sintá(c)tica
- Stanza - A Python NLP Library for Many Human Languages
- Como ter a performance de BERT utilizando uma Regressão Logística?
- RegExplain - Expressões regulares são complicadas. RegExplain torna mais fácil ver o que você está fazendo.
- RVerbalExpressions - make it easier to construct regular expressions using grammar and functionality inspired by VerbalExpressions