Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

The deep learning course that teaches you why your model isn't working

The second course in DeepLearning.AI's landmark Deep Learning Specialization, this intermediate course opens up the practical side of neural networks that most introductions skip: hyperparameter tuning, regularization, and optimization. It's been taken by an enormous 635,134 learners.

Rather than adding new architectures, it deepens your ability to actually make a neural network perform — diagnosing overfitting, tuning learning rates, applying regularization techniques, and using TensorFlow to put the theory into working code.

Ideal for

• Learners who've completed an intro deep learning course and hit a performance wall
• Applied ML practitioners who need debugging and evaluation skills, not just model-building
• Anyone continuing through the broader Deep Learning Specialization

This is the course where deep learning stops feeling like magic and starts feeling like an engineering discipline you can debug systematically.