X: vector
INDEX: list of factors
FUN: function to apply
... : additional args
simplify: return vector/matrix
🧵 9/10 🧵
🔁 When to Use Which Function?
1️⃣ apply(): use with matrices/arrays for row/column operations
2️⃣ lapply(): use with lists/vectors, get a list output
3️⃣ sapply(): use with lists/vectors, get a vector/matrix output
10/10 🧵
4️⃣ vapply(): use with lists/vectors, enforce output type
5️⃣ mapply(): use with multiple lists/vectors
6️⃣ tapply(): use with vector subsets defined by factors
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[1/8] 📚 Introducing #Quarto: A Versatile, New and Exciting Publishing Tool! 🌟
Quarto is a powerful, open-source, and user-friendly publishing framework that streamlines the process of creating beautiful books, documents, and websites. Let’s explore it now! #RStats#DataScience
[2/8] 🤓 Language Agnostic: Quarto works seamlessly with multiple languages, including #Markdown, #LaTeX, #RMarkdown, and #Jupyter notebooks. So, whether you're a researcher or a creative writer, Quarto has you covered! 🌍 #DataScience#RStats
[3/8] 🔁 Format Flexibility: With Quarto, you can convert your content into various formats, such as PDF, HTML, EPUB, and even slide presentations. It makes sharing your work with diverse audiences a breeze! 🌬️ #RStats#DataScience
🧵1/9 A deep dive into the history of #Backpropagation: A key technique in training multilayer architectures for neural networks. This powerful method revolutionized the way we train AI systems, leading to major breakthroughs in various domains. 🤖#DataScience#DeepLearning#AI
🧵2/9 #Backpropagation is based on a simple concept: use gradient descent to optimize multilayer networks. By applying the chain rule for derivatives, it computes gradients efficiently, leading to optimized weight configurations in each layer of the network. #DataScience#AI
🧵3/9 The shift to Rectified Linear Units (ReLU) accelerated learning in deep networks, allowing training without unsupervised pre-training. This non-linear activation function proved more effective than its smoother predecessors like tanh(z) or 1/(1+exp(−z)). #ReLU#DataScience
🧵1/8 Loading datasets from various sources is crucial for data analysis. In this thread, we'll explore how to read datasets from different sources and software using R! 📚 #RStats#DataScience
🧵2/8 CSV Files: The "read.csv" function is a go-to for reading comma-separated values files. For improved performance and more flexibility, consider using the "read_csv" function from the readr package or the fread function from the data.table package. 📃 #CSV#RStats
🧵3/8 Excel Files: The readxl package provides functions like "read_excel" for reading data from Excel files (.xls and .xlsx). Alternatively, the openxlsx package offers more features, including reading and writing Excel files. 📊 #Excel#RStats
Thread: (1/9) You might have heard the term 'bootstrapping' thrown around in discussions about statistics, data analysis, or machine learning. But what does it mean, and why is it so powerful? Let's break it down in simple terms! #RStats#DataScience
(2/9) Bootstrapping is a resampling technique that involves taking multiple samples from the original dataset, each time with replacement. It's like drawing marbles from a bag, putting each one back after recording its color. This helps us understand the uncertainty in our data.
(3/9) In real-life situations, it's not always feasible to collect more data. Bootstrapping allows us to make the most of what we have, creating a 'pseudo-replica' of our dataset through resampling. This helps us understand the variability of our estimates. #RStats#DataScience
1/ 🎯 Introduction 📌
The #caret package in #R is a powerful tool for data pre-processing, feature selection, and machine learning model training. In this thread, we'll explore some useful tips & tricks to help you get the most out of caret. #DataScience#MachineLearning#RStats
2/ 🧹 Data Pre-processing 📌
caret offers various data pre-processing techniques, like centering, scaling, and removing near-zero-variance predictors. Use the preProcess() function to apply these methods before model training.🧪 #RStats#DataScience
3/ ⚙️ Feature Selection 📌
Use the rfe() function for recursive feature elimination. This method helps you find the most important features in your dataset, improving model performance & interpretation.🌟 #RStats#DataScience
1/🧶📝 Welcome to a Twitter thread discussing the pros & cons of the #R packages, #knitr and #sweave. These packages allow us to create dynamic, reproducible documents that integrate text, code, and results. Let's dive into the strengths and weaknesses of each. #Rstats
2/🔍 #knitr is a more recent and widely-used package that simplifies the creation of dynamic reports. It's an evolution of #sweave and supports various output formats, including PDF, HTML, and Word. Plus, it's compatible with Markdown and LaTeX! #Rstats
3/🌟 Pros of #knitr:
✅ Better syntax highlighting
✅ Cache system to speed up compilation
✅ Inline code chunks
✅ Flexible output hooks
✅ More output formats
✅ Integrates with other languages
Overall, it provides more control and customization in document creation. #RStats