Data Privacy Challenges with Large Language Models
Large Language Models (LLMs) have rapidly evolved, building on decades of research and advances in computing power and data availability. Most LLMs consist of billions of parameters; trained on vast amounts of diverse datasets. LLMs generate highly contextual and personalized outputs. This.
Introduction: From Simple to Multiple Linear Regression
In our previous post, we introduced Linear Regression, a fundamental technique used to predict outcomes based on a single factor—such as estimating house prices based on square footage. We also discussed essential performance metrics like Mean Squared Error (MSE) and R-squared, which help assess.
Exchanging Knowledge and Insights About AI and ML with DSS Community Events
Generative AI, synthetic data, language models in healthcare, AI in finance, and ML in the enterprise are among the most significant topics to be covered during the Data Science Salon community events.
Understanding Customer Concerns in Retail: A review of LLMs and Traditional ML-based methods for Topic Modeling and Multi-label Classification
Did you know that organizations risk losing an average of 8% of their revenue due to poor Customer Experience (CX)? This significant potential loss can be attributed to the fact that over 50% of consumers either decreased or stopped spending with a business after a bad experience.
Machine Learning in Production
In recent years, Machine Learning (ML) has propelled software systems into new realms of capability. From revolutionizing medical assistance and personalized recommendations to enabling chatbots and self-driving cars, ML has become a cornerstone of modern technology.
Selection Bias in product analytics and common pitfalls
Statistical Inference is centered on using random samples effectively. In order to draw a sample at random, one has to ensure that each observation is drawn independently from the same population. A random sample of observations is said to be independent, identically distributed (iid) and this.
Data Literacy as a Basic Need: The Vital Role of Data Literacy in the AI Era for Bridging the Gap
Data is now essential for organizations, driving their operations as a lifeblood and fueling Artificial Intelligence (AI). The amount of data produced daily is growing rapidly, currently reaching 2.5 quintillion bytes per day. Data Literacy (DL) is no longer just a skill; it has become a.
Linear regression basics guide - part 1
In today's data intensive world, data scientists play a crucial role. Their main job is to make sense of large amounts of data, turning it into useful information that can guide decisions. This involves both creative and technical skills, using a range of methods and tools. For beginners, it's.
Graphing Wisdom: Empowering Large Language Models using Knowledge Graphs
Large Language Models (LLMs) are revolutionizing the AI domain at an incredible pace. The diffusion of such powerful models is happening across various domains, from education to entertainment to healthcare.
DSS NYC 2024 - practical examples of leveraging AI for Finance
The Data Science Salon New York 2024 provided a remarkable platform for professionals to connect and exchange insights on the evolving landscape of AI and Machine Learning. The event featured in-depth discussions on Generative AI, Large Language Models (LLMs) in Finance, and a range of other AI.
