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Gradient Descent, Stochastic Gradient Descent, and Momentum are all optimization methods used to train neural networks. 🧠 Here's the key difference: 🔹 Gradient Descent uses the entire dataset to calculate each update. 🔹 SGD uses one training example at a time, making updates faster but noisier. 🔹 Momentum builds on SGD by using information from previous updates to smooth out the path toward the minimum. Why use them? ✅ Gradient Descent provides stable updates. ✅ SGD is faster and more practi
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TikTokdatasciencefoundry
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