A Gentle Introduction to Lists and Data Frames in R

Vectors in R are supposed to be of homogeneous data type. You can use a list as the container if there are mixed data types, such as numbers and strings. The list and data frame are closely related in R. The data frame is probably more useful because it reflects how we usually collect statistics. …

A Gentle Introduction to Vectors in R

Last Updated on August 18, 2023 R is a language for programming with data. Unlike many other languages, the primitive data types in R are not scalars but vectors. Therefore, understanding how to deal with vectors is crucial to programming or reading the R code. In this post, you will learn about various vector operations …

Celebrating Devart’s 26th Birthday with an Exclusive 20% Discount on Data Connectivity Tools!

Last Updated on August 16, 2023 Sponsored Post     Devart, a leading provider of database connectivity solutions, is celebrating its 26th birthday.   Devart has been at the forefront of delivering innovative solutions that empower businesses and developers to connect, manage, and optimize their databases seamlessly. With a rich history of providing top-notch database …

MOSTLY AI: The most accurate synthetic data generator

Last Updated on July 27, 2023 Sponsored Post   By Georgios Loizou, AI & Machine Learning Product Owner at MOSTLY AI     As businesses attempt to extract relevant insights and build powerful machine-learning models, the need for high-quality, accurate, synthetic data generators has grown. In our pursuit of excellence, we at MOSTLY AI, the …

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Training Diffusion Models with Reinforcement Learning

Training Diffusion Models with Reinforcement Learning replay Diffusion models have recently emerged as the de facto standard for generating complex, high-dimensional outputs. You may know them for their ability to produce stunning AI art and hyper-realistic synthetic images, but they have also found success in other applications such as drug design and continuous control. The key …

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On the Stepwise Nature of Self-Supervised Learning

Figure 1: stepwise behavior in self-supervised learning. When training common SSL algorithms, we find that the loss descends in a stepwise fashion (top left) and the learned embeddings iteratively increase in dimensionality (bottom left). Direct visualization of embeddings (right; top three PCA directions shown) confirms that embeddings are initially collapsed to a point, which then …