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
- Getting started with the pwr package
- OPDOE: Optimal Design of Experiments
- G*Power: Statistical Power Analyses for Windows and Mac
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
- Animate your data wrangling
- Communicate your work with animated graphs in R!
- Animating Data Transformations
- gganimate has transitioned to a state of release
- 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
- 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
- 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.
- 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
- 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
- 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
- HANDBOOK - Summary and Analysis of Extension Program Evaluation in R
- CheetSheet Super resumão resumido
- 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
- Tutorial para iniciantes em Inferência Bayesiana
- Bayesplot - plotando modelo bayesianos
- How Bayesian inference works - Data Science and Robots Blog
- 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
- 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
- "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: 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
- 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
- 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
- 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
- Dicas de aprendizado supervisionado - MIT stanford.edu em PORTUGUES
- sparklyr: supervised learning
- AdaBoost, Clearly Explained - video youtube
- 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
- 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
- Interpreting Generalized Linear Models
- Descida de Gradiente para Regressão Logística Simplificada - Guia Visual Passo a Passo
- 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
- 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
- Understanding Linear SVM with R
- SUPPORT VECTOR MACHINE CLASSIFIER IMPLEMENTATION IN R WITH CARET PACKAGE
- 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
- 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
- h3r - Biblioteca de indexação geográfica h3 do Uber
- Geocoding with ggmap and the Google API - Map Porn
- 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
- 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
- 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
- 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
- Visualising US Voting Records with shinydashboard
- Boas práticas de dashboards ShinyDashboards para Finanças
- 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
- Aprendendo com Python - IME USP
- 5 principais IDEs Python para Data Science
- Python Wrapper for NVD3 - It's time for beautiful charts
- Jupyter Notebook Tutorial: The Definitive Guide
- 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
- Data-science? Agile? Cycles? My method for managing data-science projects in the Hi-tech industry.
- Structuring R projects
- 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
- 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
- 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
- R Markdown: How to number and reference tables
- R xlsx package : A quick start guide to manipulate Excel files in R
- 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
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- 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