ADAM Optimization Algorithm Explained Visually | Deep Learning #13
In this video, you’ll learn how Adam makes gradient descent faster, smoother, and more reliable by combining the strengths of Momentum and RMSProp into a single optimizer. We’ll see how Adam uses moving averages of both gradients and squared gradients, how the beta parameters control responsiveness, and why bias correction is needed to avoid slow starts. This combination allows the optimizer to adapt its step size intelligently while still keeping a strong sense of direction. By the end, you’ll understand not just the equations, but the intuition behind why Adam has become one of the most powerful and widely used optimization methods in deep learning.
Links for Important videos ✅ :-
EWMA:- • Exponentially Weighted Moving Average (EWM...
Gradient descent :- • How Gradient Descent REALLY Works
RMSProp:- • RMSProp Optimizer Visually Explained | Dee...
Momemtum Gradient descent:- • Gradient Descent With Momentum | Visual Ex...
Data Normalization:- • Data Normalization | Why Scaling Your Data...
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