What you'll learn

Modern deep learning requires more than just building a neural network: it requires knowing whether the model truly generalizes, how to tune it efficiently, and how to optimize training for stability and speed. In this course, Deep Learning Model Evaluation, Tuning, and Optimization, you’ll gain the ability to evaluate, debug, tune, and optimize deep learning models with confidence. First, you’ll explore evaluation techniques, learning how to apply metrics, analyze confusion matrices, and detect underfitting, overfitting, and data leakage using training curves. Next, you’ll discover how to tune hyperparameters, including learning rates, batch sizes, optimizers, and regularization techniques such as dropout, weight decay, and early stopping. Finally, you’ll learn to optimize the training process, using batch normalization, gradient clipping, mixed-precision training, efficient data pipelines, and monitoring tools like TensorBoard or Weights and Biases. When you’re finished with this course, you’ll have the skills and knowledge of deep learning evaluation and optimization needed to build models that train faster, generalize better, and remain stable in real-world environments