L-6 | Significance of Optimizers | Deep Learning

In machine learning, optimization methods are algorithms or techniques used to minimize the loss function, or to make a model's predictions as accurate as possible.
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Here are some of the common optimization methods:
Gradient Descent: An optimization algorithm that adjusts model parameters iteratively by moving in the direction of the steepest descent in the loss landscape to minimize the loss function.
Stochastic Gradient Descent (SGD): A variant of gradient descent that updates model parameters using only a single data point at a time, which can make it faster and more suitable for large datasets.
Adam (Adaptive Moment Estimation): An optimization algorithm that combines the ideas of gradient descent with momentum (which accelerates SGD) and scaling of the gradient by an estimate of its variance to adjust the learning rate for each parameter.
RMSProp (Root Mean Square Propagation): An algorithm that adapts the learning rate for each parameter by keeping a moving average of the squared gradients, which normalizes the gradient step, making it scale-invariant.
#computervision #deeplearning #optimization

[@palurikrishnaveni8344]

Can you make a video on shallow neural networks and optimization like Bayesian, squirrel optimizers. (with TensorFlow )

[@CodeWithAarohi]

Sure!

[@rishabhsaxena3465]

Thanks for the amazing lecture can you please upload more lectures on it

[@CodeWithAarohi]

Yes I will

[@Sunil-ez1hx]

Excellent Explanation ma’am

[@CodeWithAarohi]

Thanks a lot 😊

[@tilkesh]

Thank you

[@CodeWithAarohi]

You're welcome