Gradient Descent (and Variants like SGD)The core optimization algorithm to minimize loss.
Iteratively updates weights by moving opposite to the gradient.
Variants: Batch GD,Mini-batch GD, Stochastic GD(most common in practice).
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The most frustrating data set is the one with no labels.
The challenge of Unsupervised Learning isn't the model. It's the human mind's need for neat answers.
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Depth estimation using the depth anything v2 model 🔥
Key features:
✅More precise depth maps even in complex scenes.
✅Works well in cluttered scenes.
✅Handles unseen objects & new environments more reliably.
More info👇
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