Introduction Large language models (LLMs) represent the next generation of artificial intelligence applications, attaining widespread attention and adoption. These models demand substantial energy and resources for training. Consequently, there has been a shift towards developing pre-trained models like bidirectional encoder representations from transformers (BERT), utilizing millions of parameters from training texts to create general use models. An evolution of this approach involves fine-tuning models with domain-specific data, enhancing their utility in fields such as medicine, law, and science.

Gradient Boosting Models Gradient boosting classifier models are a powerful type of machine learning algorithm that outperform many other types of classifiers. In simplest terms, gradient boosting algorithms learn from the mistakes they make by optmizing on gradient descent. A gradient boosting model values the gradient descent, or the direction of the steepest increase of a function, to make adjustments so that the function can increase rapidly over each iteration. Gradient boosting models can be used for classfication or regression.

Traditional Methods vs AI Methods in Forecasting: When Simplicity Outperforms Complexity Forecasting remains an indispensable tool for businesses to make informed decisions about future trends, demands, and opportunities. Accurate forecasts can lead to better resource allocation, inventory management, and strategic planning. When it comes to forecasting, data scientists have many tools at their disposal, ranging from traditional and time-tested methods to advanced artificial intelligence (AI) techniques. While AI methods have gained significant attention in recent years, traditional forecasting methods like Moving Average, Exponential Moving Average, and Linear Regression Forecasting still hold their ground in many real-world scenarios.

As a data science team leader, I understand the importance of effective time management. Data-driven projects require careful planning, focused execution, and constant collaboration. However, one common obstacle that can hinder productivity is the abundance of time-wasting meetings. To address this challenge, I have adopted a management style that minimizes unnecessary meetings, allowing my team to maximize their productivity and focus on delivering results. In this blog post, I will share my approach, emphasizing the significance of streamlined communication and outlining the key standing meetings that have proven invaluable in managing my small data science team.

3D printing has revolutionized the manufacturing industry in the last few years. This technology enables individuals and businesses to create objects from a digital model using a 3D printer. The process of fused deposition modeling, or FDM for short, 3D printing involves adding layers of material until the desired object is created. This technology has become increasingly popular and is being used in a variety of fields including medicine, architecture, and engineering.

Twitch, parent company Amazon, is the leading streaming platform on the web reaching over 7.03 million users in 2022. The vast majority of streamers using the Twitch service are streaming video game gameplay or “Just Chatting”. Among the video games categories you can find almost any video game imaginable. Amongst the “Just Chatting” category you will find streamers that primarily interact with chat with any combination of engaging gimmicks. As a long time Twitch user, I have recently begun experimenting with what I hope will be a unique interactive and engaging concept that will allow Twitch users that are interested in STEM concepts like engineering, mathematics, and technology to participate in my interest by watching and participating in my stream.