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Peter Math on Instagram: "Singular Value Decomposition (SVD) is one of the most powerful and versatile tools in linear algebra. It breaks down any matrix—even a non-square one—into three simpler matrices that reveal its fundamental structure: one that captures its directions (U), one that scales them (Σ), and one that rotates or reflects them (Vᵀ). At its core, SVD helps us understand how a matrix transforms space by identifying the principal components of that transformation. SVD is everywhere—
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Artificial Intelligence | AI on Instagram: "Principal Component Analysis (PCA) is a powerful dimensionality reduction method that simplifies complex datasets by finding the most important patterns of variation. Imagine taking some data points and stretching them along their most variable directions (the directions that have the most information); these are called the principal components. These components act like new axes that capture the maximum variability in the data, reducing high-dimension
Artificial Intelligence | AI on Instagram: "Principal Component Analysis (PCA) is a powerful dimensionality reduction method that simplifies complex datasets by finding the most important patterns of variation. Imagine taking some data points and stretching them along their most variable directions (the directions that have the most information); these are called the principal components. These components act like new axes that capture the maximum variability in the data, reducing high-dimension
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DATA SCIENCE on Instagram: "Do you know this fact 🐼 ✨The name "pandas" comes from the term "panel data", which refers to a type of data that contains observations over multiple variables and time periods. ✨pandas was created by Wes McKinney, a data scientist and software engineer, in 2008. ✨pandas provides two primary data structures: Series (1-dimensional labeled array) and DataFrame (2-dimensional labeled data structure with columns of potentially different types). ✨pandas provides several op
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Deeply AI: AI, ChatGPT, Robotics, Data Science on Instagram: "In a neural network, three-dimensional data goes through a series of mathematical operations to extract and refine features step by step. Each layer processes the input using matrix multiplications and biases, handling the 3D structure—like depth, width, height, or RGB channels—as organized information to work with. Non-linear activation functions are then applied, adding complexity so the network can capture patterns that simple line
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