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Santosh Kumar on Instagram: "🔢Day 163– Data_Science_Journey(Deep Learning) 📊Topic : Loss Function and Cost Function Today I Explored loss function and cost function in deep learning and how to use regression and classification in deep learning *Loss Function :- A way to calculate Error / diff bewteen predicted and acutal -Jab mai indivisual JaKe error ko calculate karta hu -loss function *Cost Function : Pure Batch ke lye calculate karta hai wo hai cost function *Regression 1. Mean Square Erro
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Santosh Kumar on Instagram: "🔢Day 164– Data_Science_Journey(Deep Learning) 📊Topic :Classification- Loss Function and Cost Function Today I Explored loss function and cost function in deep learning and how to use classification in deep learning Classification 1.Binary Cross Entrophy / Log Loss : Binary cross-entropy (log loss) is a loss function used in binary classification problems. It quantifies the difference between the actual class labels (0 or 1) and the predicted probabilities output by
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AI • Machine Learning • Tech on Instagram: "Mean Squared Error (MSE) is a popular and useful loss function in deep learning, used to measure the average squared difference between predicted and actual values. By penalizing larger errors more significantly, MSE helps optimize neural network weights during training through gradient descent. This optimization ensures the model learns to make more accurate predictions over time, making MSE a fundamental tool in regression tasks. Join our AI communit
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