Model Comparison and Tuning in TensorFlow and Keras

How do you know if your neural network has the right number of layers or if you are overfitting your data? Choosing the best machine learning model requires systematic comparison, evaluation, and tuning. This text-based course guides you through the core principles of evaluating and comparing deep learning models. You will transition from building simple neural networks to systematically analyzing how architectural changes, like modifying layers, neurons, and validation strategies, impact your model's accuracy and performance. What you will learn: Understand foundational machine learning terms, including training, validation, and test dataset splits; Configure neural network architectures by adjusting layers and neurons in TensorFlow and Keras; Analyze model performance metrics to identify underfitting and overfitting; Compare multiple model variations systematically to find the optimal configuration; Apply modern validation techniques to ensure your model generalizes well to unseen data. You will start with the essential terminology of model evaluation before moving into practical text-based exercises. Through step-by-step code explanations, you will learn to construct, train, and compare different neural network structures. This course is designed for beginners in machine learning and data science who want to move beyond basic tutorials and learn how to optimize neural networks. No prior deep learning experience is required, though a basic familiarity with Python is helpful. Start reading today to master the art of model comparison and build more reliable neural networks.