Animating elements from display: none to display: block has long been a challenge in CSS. The traditional display property doesn't support transitions, making smooth animations difficult. However, with the introduction of the @starting-style at-rule and other new CSS features, we now have powerful tools to create seamless transitions between these states.
Next.js 13 introduced Server Actions, which provide a powerful way to handle form submissions directly on the server.
This article will guide you through creating a form, handling submissions, showing loading states, and applying validations.
Handling missing data is crucial in data analysis and machine learning. Visualizing missing data helps to understand the extent and pattern of missingness, which can inform the choice of strategies for dealing with it. In this guide, we'll use Python and the missingno library to visualize missing data in a dataset.
You can download the Jupiter notebook of the example Visualizing Missing Data
# Install a pip package in the current Jupyter kernel
import sys
!{sys.executable} -m pip install YOUR_PACKAGE_NAME
Ref: https://jakevdp.github.io/blog/2017/12/05/installing-python-packages-from-jupyter/
Handling missing values is a crucial step in preparing data for machine learning. This tutorial provides examples of how to manage missing values using Python, focusing on the Pandas library. We'll import the necessary libraries, read the data, and explore various methods to handle missing values.
You can check the full code in the Jupyter Notebook
The iloc indexer in pandas is a powerful tool for data selection, slicing, and manipulation, essential for preparing datasets for machine learning tasks. Here's a comprehensive guide to help you master iloc.
You can download the .ipynb file from here
Imagine you're a teacher and your students are working on a group project. One student is a math whiz, another excels at writing, and a third is a history buff. If you grade each section based on the individual's absolute strengths, the math whiz would dominate the score, even if the writing and history were excellent. This is similar to what can happen in machine learning with features (data points) on vastly different scales.
Machine learning (ML) has become a cornerstone of modern technology, driving advancements in various fields such as healthcare, finance, and transportation. To build effective ML models, it's essential to understand the three main steps in the machine learning process: Data Preprocessing, Modeling, and Evaluation. This article breaks down these steps, detailing the sub-steps involved and providing references for further reading and understanding.
The new React compiler introduced in React 19 it will significantly improve React development.
React's new compiler is an innovative tool designed to automatically optimize your React applications. By deeply understanding your code, the compiler applies optimizations grounded in React’s core principles. These optimizations can lead to significant performance enhancements, especially for complex applications.
Currently in its experimental phase, the new compiler has the potential to revolutionize React development. It's particularly interesting to see how it will interact with the inline optimization technique used in React like memo, useMemo useCallback.
The ongoing development and integration of the compiler promise exciting advancements in the efficiency and performance of React applications. As the tool matures, it could become a game-changer for developers seeking to build faster, more efficient applications.
Hi everyone! I'm venturing into the exciting world of machine learning (ML), and this article details the tools I'm using to get started.
Python: As a widely used general-purpose language, Python is a popular choice for ML due to its readability, extensive libraries, and large community.
Python download: https://www.python.org/downloads/
R: Another powerful language specifically designed for statistics and data analysis. R offers a rich ecosystem of packages specifically tailored for ML tasks.
You can download R from the official website: https://www.r-project.org/
RStudio: An integrated development environment (IDE) built specifically for R. It provides a user-friendly interface for writing, running, and managing your R code. It also offers features like code completion, syntax highlighting, and debugging tools, making your R experience smoother.
Download RStudio from the official website: https://www.rstudio.com/products/rstudio/