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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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Sine Function in TinyML

Approximating Sine Function in TinyML

Introduction TinyML is the field which involves deployment of machine learning models into resource constrained devices such as micro-controllers. Such devices called edge devices often have few Kilobytes of RAM and flash memory but consumes power in milli-watts range. This feature makes the technology an  ideal choice for remote sensing applications, weather stations, tiny gadgets …

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