Data

As data scientists, we’ve turn out to be extremely focused on constructing algorithms, causal/predictive models, and advice systems (and now genAI). We optimize for accuracy, fine-tune hyperparameters, and search for the subsequent big fancy...

are large-scale AI models trained on an unlimited and diverse range of information, comparable to audio, text, images, or a mix of them. For this reason versatility, foundation models are revolutionizing Natural Language...

to preparing videos for machine learning/deep learning. As a consequence of the scale and computational cost of video data, it's vital that it's processed in as efficient a way possible to your use...

attract all of the hype today inside data science, but I’d argue they’re each secondary to a more vital—and often-ignored—section of the sector. When coping with data, there are two essential steps: Processing and analyzing...

my first n8n workflow, as a knowledge scientist, it felt like I used to be cheating.I could connect with APIs without reading 30-page docs, trigger workflows from Gmail or Sheets, and deploy something...

, I discovered myself wondering why some dashboards immediately grabbed my attention, while others just felt flat. A giant a part of that magic is color. As basic because it sounds, it plays a...

To get essentially the most out of this tutorial, you must have a solid understanding of the right way to compare two distributions. For those who don’t, I like to recommend testing this excellent...

    MIT News,  Q:How are synthetic data created?A: Synthetic data are...