Keras Model Optimization: Tuning Optimizers and Learning Rates

Getting poor accuracy or slow convergence with your deep learning models? Often, the secret to unlocking better performance lies not in changing your neural network architecture, but in fine-tuning how it learns. This written course guides you through the essential mechanics of optimization in Keras, taking you from default training settings to confidently selecting, swapping, and tuning optimizers to achieve faster convergence and higher model accuracy. What you'll learn: Understand the fundamental role of optimizers and loss functions in training neural networks; Configure Stochastic Gradient Descent (SGD) with custom learning rates and momentum; Compare modern optimization algorithms including Adam, AdamW, and RMSprop; Implement learning rate schedulers to dynamically adjust training speed; Analyze training curves to diagnose underfitting, overfitting, and optimization bottlenecks. The course begins with foundational optimization concepts before moving into practical code explanations where you will learn to modify optimizer parameters and evaluate their impact on model performance. This course is designed for beginners with basic Python knowledge and a foundational understanding of neural networks who want to improve their practical model-tuning skills. Start optimizing your neural networks today.