One of the first things Data Scientists learn as they run predictions is to avoid the use of loops. That’s because most ML libraries support vectorized inference, combining many inputs into a batch and more efficiently calculating the results. This specialized technique combines framework-level features with specialized hardware like GPUs, making p... See more
Tim Liu • Breaking Up With Flask & FastAPI: Why ML Model Serving Requires A Specialized Framework
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
Hadley Wickham, Garrett Grolemund
amazon.com
ultimately just dealing with data. And current neural nets—with current approaches to neural net training—specifically deal with arrays of numbers. But in the course of processing, those arrays can be completely rearranged and reshaped. And as an example, the network we used for identifying digits above starts with a 2D “image-like” array, quickly
... See moreStephen Wolfram • What Is ChatGPT Doing ... And Why Does It Work?
Sometimes we want to analyze clusters of data (K-means) or reduce the dimensions or features in play (manifold learning) or convert from one type of data to another (autoencoding).
Eric Redmond • Deep Tech: Demystifying the Breakthrough Technologies That Will Revolutionize Everything
