Gradient Descent is a key optimization algorithm in Machine Learning & Deep Learning. In this video, we explain:
✅ Batch Gradient Descent (BGD) – Uses the full dataset for updates
✅ Stochastic Gradient Descent (SGD) – Updates after each sample (faster but noisy)
✅ Mini-Batch Gradient Descent (MBGD) – Best balance for speed & accuracy
💡 What You’ll Learn:
✔️ How Gradient Descent works in ML models
✔️ Differences between Batch, Stochastic & Mini-Batch GD
✔️ Hands-on Python implementation of all three methods 🐍
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https://medium.com/p/1919f8ec31db
