Roshna S H

Dr. Roshna S H is a scientist by profession with more than five years of teaching experience at graduate and postgraduate levels. She has obtained a Ph.D. from IIT Madras after M.Sc., M. Phil in Physics. She has also cleared CSIR-NET and GATE with top ranks. She is a constant content creator through publications in peer-reviewed international journals and by writing concept based blog articles. She has research experiences in diverse areas spans from atmospheric science to optics as well as spintronics and magnetism. She has obtained awards and recognitions at various international platforms for her contributions as scientific articles and oral presentations. Dr. Roshna completed her Ph.D. with exposure at American physical society, Material research society, University of Oxford, etc. She focuses on communicating the concepts with utmost clarity in the simplest possible way.

AutoML for Decision Making

AutoML for Data Driven Decision Making

In the domain of data-driven decision-making, AutoML (Automated Machine Learning) emerges as a revolutionary tool. It automates the complex process of applying machine learning to real-world problems, enabling users of all expertise levels to swiftly build high-performing models. By simplifying tasks such as data preprocessing, model selection, and hyperparameter tuning, AutoML streamlines workflows, saving valuable …

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Multimodal AI

The power of Integrated Technology: Multimodal AI

In the ever-evolving landscape of artificial intelligence, there emerges a groundbreaking approach that combines the prowess of multiple modalities such as text, images, speech, and more – to enhance comprehension and interaction. This integration of diverse data sources gives birth to what is known as multimodal AI, revolutionizing how machines perceive and understand the world …

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Generative AI

The Boundless Creativity of Generative AI: From Art to Science

Generative Artificial Intelligence (AI) stands at the forefront of innovation, reshaping the boundaries of creativity and technology. It represents a paradigm shift in AI research, enabling machines to autonomously produce original content across various domains, including artwork, music, literature, and design. This technology emerged from the intersection of deep learning, neural networks, and computational creativity. …

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Machine Learning in Material Science

Machine Learning in Material Science Research

In recent years, the integration of machine learning (ML) techniques with material science research has catalyzed innovation, revolutionizing traditional approaches to material discovery, characterization, and optimization. This article explores the diverse applications of ML in material science, highlighting its transformative impact on various research domains. Areas in Material Science where Machine Learning is Used Prediction …

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Math for data science

Essential Math for Data Science : A Guide to Beginner’s

Data science has become an indispensable field in today’s digital age, revolutionizing industries with its ability to derive valuable insights from vast amounts of data. While proficiency in programming languages and data manipulation tools is crucial, having a solid foundation in mathematics is equally essential for success in this field. In this article, we’ll explore …

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Stable diffusion models

Stable Diffusion Models in Deep Learning

Stable diffusion models stand out for their ability to capture intricate data distributions while maintaining stability during training and inference. Unlike traditional approaches that may suffer from issues like mode collapse or vanishing gradients, stable diffusion models offer a robust solution for tasks ranging from image generation to data denoising. The title image depicting a …

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