Loss Functions - EXPLAINED!
8:30
YouTubeCodeEmporium
Loss Functions - EXPLAINED!
Many animations used in this video came from Jonathan Barron [1, 2]. Give this researcher a like for his hard work! SUBSCRIBE FOR MORE CONTENT! RESEOURCES [1] Paper on adaptive loss function: https://arxiv.org/abs/1701.03077 [2] CVPR paper presentation: https://www.youtube.com/watch?v=BmNKbnF69eY [3] Regression Loss Functions: https ...
156.7K viewsJan 20, 2020
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Artificial Intelligence | AI on Instagram: "Gradient descent is an iterative optimization technique used to train machine learning models by finding the set of parameters (like weights in a neural network) that make the loss function as small as possible. Given a simple surface, we calculate the gradient, which is the collection of partial derivatives of the loss with respect to x and y. A partial derivative tells us how the loss changes if we tweak just one parameter while holding the others co
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Artificial Intelligence | AI on Instagram: "Gradient descent is an iterative optimization technique used to train machine learning models by finding the set of parameters (like weights in a neural network) that make the loss function as small as possible. Given a simple surface, we calculate the gradient, which is the collection of partial derivatives of the loss with respect to x and y. A partial derivative tells us how the loss changes if we tweak just one parameter while holding the others co
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AI • Machine Learning • Tech on Instagram: "Gradient descent is an optimization method used to train machine learning models by finding the set of parameters (like weights in a neural network) that make the loss function as small as possible. You can think of the loss as a surface, where each point represents a different set of parameters, and our goal is to move downhill toward the lowest point. To do this, we calculate the gradient, which is a collection of partial derivatives of the loss with
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AI • Machine Learning • Tech on Instagram: "Gradient descent is an optimization method used to train machine learning models by finding the set of parameters (like weights in a neural network) that make the loss function as small as possible. You can think of the loss as a surface, where each point represents a different set of parameters, and our goal is to move downhill toward the lowest point. To do this, we calculate the gradient, which is a collection of partial derivatives of the loss with
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