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
Luminal is a deep learning library that uses composable compilers to achieve high performance.
Current ML libraries tend to be large and complex because they try to map high level operations directly on to low level handwritten kernels, and focus on eager execution. Libraries like PyTorch contain hundreds of thousands of lines of code, making it ne... See more
Current ML libraries tend to be large and complex because they try to map high level operations directly on to low level handwritten kernels, and focus on eager execution. Libraries like PyTorch contain hundreds of thousands of lines of code, making it ne... See more
r/MachineLearning - Reddit
Essential Math for Data Science
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
Hadley Wickham, Garrett Grolemund
amazon.com
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).