-
Optimization Algorithms In Deep Learning.
Optimization Algorithms For Neural Networks Table Of Contents: What Is An Optimization Algorithm? Gradient Descent Variants: Batch Gradient Descent (BGD) Stochastic Gradient Descent (SGD) Mini-Batch Gradient Descent Convergence analysis and trade-offs Learning rate selection and scheduling Adaptive Learning Rate Methods: AdaGrad RMSProp Adam (Adaptive Moment Estimation) Comparisons and performance analysis Hyperparameter tuning for adaptive learning rate methods Momentum-Based Methods: Momentum Nesterov Accelerated Gradient (NAG) Advantages and limitations of momentum methods Momentum variants and improvements Second-Order Methods: Newton’s Method Quasi-Newton Methods (e.g., BFGS, L-BFGS) Hessian matrix and its computation Pros and cons of second-order methods in deep learning Optimization Challenges and
