This article discusses the challenges of analyzing data that follows a Power Law distribution and presents a technique called the “Log-Log approach” to detect Power Laws in real-world data. It also introduces the Maximum Likelihood method as a more mathematically sound approach to estimating the parameters of a Power Law distribution. The article provides example…

Summary: The text discusses the key elements that power advanced recommendation engines, focusing on two-tower neural networks and the use of negative sampling. It explores the efficiency and effectiveness of two-tower networks in ranking, the impact of loss functions and negative sampling on model accuracy, and the role of negative sampling in recommendation systems. The…

Researchers have developed an active learning workflow to create a machine learning (ML) model for efficient prediction of hydrogen combustion. The workflow expands the dataset and utilizes negative design data acquisition and metadynamics simulations. The ML model accurately predicts transition states and reaction mechanisms, providing insights into potential energy surfaces. The approach shows promise for…

LLMs are powerful linguistic agents used for programming tasks, but there is a gap between their capabilities in controlled settings and real-world programming scenarios. Existing benchmarks focus on code generation, but real-world programming often involves using existing libraries. A new study introduces ML-BENCH, a dataset to evaluate LLMs’ ability to interpret user instructions and generate…

This article discusses four different methods of passing arguments to Python scripts. For more information, please read the full article on Towards Data Science.

The article discusses how to use Pandas and the YouTube Data API to obtain statistical insights. For more details, please visit Towards Data Science.

Artificial Intelligence and deep learning have made significant advancements in technology, enabling robots to perform tasks previously limited to human intelligence. Symbolic Regression in AI plays an important role in scientific research, focusing on algorithms that interpret complex patterns in datasets. The Φ-SO framework, a Physical Symbolic Optimization method, automates the process of finding analytic…

This article discusses the use of network analytics to analyze international trade data provided by UN Comtrade. The author highlights the importance of this approach in gaining insights into global trade patterns. For more information, read the full article on the Towards Data Science website.

OpenAI made headlines this week with a dramatic series of CEO appointments and firings. Sam Altman was initially removed as CEO, leading to a backlash from OpenAI staff. However, it seems that Altman will be reinstated as CEO under a new board. In other news, Microsoft expressed interest in attracting disgruntled OpenAI staff and released…

Purdue University researchers have introduced a novel approach using topological data analysis (TDA) to interpret complex prediction models, including machine learning and neural networks. They leveraged TDA to construct Reeb networks, providing a topological view that aids interpretation. The method was successfully applied to various domains and showcased its scalability across large datasets, with applications…