Regularization in Deep Learning How it solves Overfitting

Optimization in deep learning, in this course we will learn about Optimization in Deep Learning course. Optimization is a core aspect of training neural networks efficiently and effectively. This course introduces key optimization algorithms used in deep learning, starting with the basics of gradient descent and expanding into advanced techniques such as SGD, Momentum, AdaGrad, RMSProp, and Adam. You will understand how each optimizer works, their mathematical foundations, when to use them, and how they impact learning dynamics. We’ll explore the role of learning rates, loss functions, convergence behavior, and techniques like learning rate scheduling and weight regularization. Through hands-on coding with TensorFlow and PyTorch, you will learn to implement and compare these optimizers on real-world datasets. The course also addresses common optimization challenges such as vanishing gradients and overfitting. By the end, you’ll be able to choose and fine-tune optimizers to improve model accuracy and training speed in any deep learning project. Learn With Jay