Description
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization & Optimization
Building a neural network is just the start. The real challenge? Making it **fast, accurate, and generalizable**. This course—part of the **Deep Learning Specialization by Andrew Ng**—teaches you the industry-proven techniques to diagnose and fix the most common deep learning pitfalls: vanishing gradients, overfitting, slow convergence, and poor generalization.
What You’ll Master
- Initialization methods—Xavier and He initialization to avoid saturation
- Regularization—L2, dropout, and data augmentation to prevent overfitting
- Optimization algorithms—Momentum, RMSprop, and Adam for faster convergence
- Batch normalization—stabilize training and enable deeper networks
- Hyperparameter tuning—systematic search strategies that actually work
- Debugging workflows—learning curves, error analysis, and orthogonalization
Real Projects You’ll Build
- A regularized deep network for image classification
- An optimizer comparison suite—watch Adam vs SGD in real time
- A hyperparameter tuning dashboard using random and grid search
Why This Course?
Most tutorials skip the *why* behind best practices. This course—taught by **Andrew Ng**—gives you the **theoretical foundation and practical intuition** used by top ML engineers at Google, Meta, and DeepMind.
Who Is This For?
- Intermediate deep learning practitioners hitting performance walls
- Kaggle competitors optimizing model scores
- Students preparing for ML interviews at top tech firms
- Engineers deploying models in production
From “It Works” to “It’s Optimal”
This course bridges the gap between academic knowledge and industrial-grade deep learning. You’ll learn not just *what* to do—but *when* and *why*.
Ready to level up your deep learning game? Enroll now.
