Computer Vision to explore the marvels of Visual Intelligence
Visual Intelligence using Computer Vision
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 …
Interview Questions on Generative AI (Part-9)
Here is a set of frequently asked interview questions on generative AI. What is generative AI, and how does it differ from other types of AI? Generative AI focuses on creating new data, such as images, texts, or sounds, that resemble real data. Unlike other AI, which might classify or predict, generative AI produces entirely …
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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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 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 …
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 …