Large Language Model (LLM)-based agents have demonstrated remarkable effectiveness.
As the field of data science continues to grow, there will be an ever-increasing demand for tools that make machine learning accessible to non-experts.
Over the past decade, data science and machine learning has grown from a mysterious art form to a staple tool across a variety of fields in academia, business, and government.
In this chapter, we present a genetic programming-based AutoML system called TPOT that optimizes a series of feature preprocessors and machine learning models with the goal of maximizing classification accuracy on a supervised classification problem.
Exploratory data science largely happens in computational notebooks with dataframe APIs, such as pandas, that support flexible means to transform, clean, and analyze data.
Databases Human-Computer Interaction
Smarter applications are making better use of the insights gleaned from data, having an impact on every industry and research discipline.
However, existing systems often struggle with processing large datasets due to Out-of-Memory (OOM) problems caused by poor data partitioning.
Distributed, Parallel, and Cluster Computing
This is the second in a series of articles dealing with machine learning in asset management.
We present Syft 0. 5, a general-purpose framework that combines a core group of privacy-enhancing technologies that facilitate a universal set of structured transparency systems.
We detail a new framework for privacy preserving deep learning and discuss its assets.