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AI Innovations and Researches(Agitix AI) on Instagram: "To explain the Mathematics of an Artificial Neural Network (ANN), we must look at it as a composite function f(x) that maps an input vector to an output vector through a series of linear transformations and non-linear activations. 1. The Forward Pass: Linear Transformation Each neuron in a layer performs a weighted sum of its inputs and adds a bias term. For a single layer, this is represented by the matrix equation: z=W⋅x+b x: The input ve
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Rakesh Kumar on Instagram: "📉 Loss Function (MSE) Explained with Maths | ML Basics How does a Machine Learning model know it’s wrong? 👉 By using a Loss Function. In this reel, I explained Mean Squared Error (MSE) step by step: What error means in ML MSE formula: average of squared errors Why squaring the error matters How models minimize loss during training 📌 If you understand MSE, concepts like Linear Regression and Gradient Descent become much easier. Save this reel for ML revision 🚀 Foll
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