Explaining about various optimization techniques which is used to change model weights and learning rates, like Gradient Descent, Stochastic Gradient Descent, Stochastic Gradient descent with momentum, Mini-Batch Gradient Descent, AdaGrad, RMSProp, AdaDelta, and Adam. These optimization techniques play a critical role in the training of neural networks, as they help to improve the model by adjusting its parameters to minimize the loss of function value.
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Explaining about various optimization techniques which is used to change model weights and learning rates, like Gradient Descent, Stochastic Gradient Descent, Stochastic Gradient descent with momentum, Mini-Batch Gradient Descent, AdaGrad, RMSProp, AdaDelta, and Adam. These optimization techniques play a critical role in the training of neural networks, as they help to improve the model by adjusting its parameters to minimize the loss of function value.
I agree to follow this project's Code of Conduct I'm a GSSoC'24 contributor I want to work on this issue