Neural network optimization,
in this course we will learn about the Neural Network Optimization techniques essential for improving model performance and training efficiency. Starting with foundational concepts, we explore optimization algorithms like Gradient Descent, Stochastic Gradient Descent (SGD), and advanced methods such as Adam, RMSProp, and AdaGrad. You’ll discover how learning rate schedulers enhance convergence, including step decay and exponential decay strategies. The course covers essential techniques like Regularization (L1, L2, and Dropout) to prevent overfitting, Batch Normalization to stabilize training, and Early Stopping to avoid unnecessary computations. Additionally, we’ll dive into Hyperparameter Tuning, Weight Initialization methods, and Data Augmentation for robust model training. By the end, you’ll master the tools and strategies needed to optimize neural networks effectively for real-world applications. TutorialsPoint
