Optimizers and Loss Functions in TensorFlow and Keras

Every successful machine learning model relies on two critical components: a loss function to measure error and an optimizer to guide the model toward the correct solution. Understanding how these two elements interact is the key to training stable, highly accurate neural networks. This text-based course provides a clear, practical introduction to choosing and configuring solvers and loss functions using modern TensorFlow and Keras APIs. You will transition from basic theory to writing clean, efficient training configurations. What you'll learn: - Understand the fundamental roles of loss functions and optimizers in gradient descent - Select the right loss function for regression, binary classification, and multi-class problems - Configure modern Keras optimizers like Adam, RMSprop, and SGD with custom learning rates - Implement loss functions and solvers in model compilation steps using clean Keras code - Troubleshoot common training issues like vanishing gradients and exploding losses - Practice setting up robust training workflows through structured written code exercises You will start with core mathematical concepts explained in plain English, progress to hands-on configuration examples using modern Keras APIs, and finish with practical debugging strategies. This course is designed for beginner machine learning developers and data analysts who want to understand the mechanics of model training without getting lost in complex academic formulas. Start reading today to master the engine that drives neural network training.