Overview
I successfully completed "Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization," the second course in the Deep Learning Specialization on Coursera. This course delved into practical techniques to enhance the performance of deep learning models, covering:
- Practical aspects of Deep Learning: Training, development, and deployment considerations.
- Optimization algorithms: Exploring various gradient descent optimization methods like Momentum, RMSprop, and Adam.
- Hyperparameter tuning: Systematic approaches to finding the best set of hyperparameters for a model.
- Batch Normalization: Understanding and implementing batch normalization to accelerate training.
- Programming Frameworks: Getting familiar with TensorFlow.
This course equipped me with valuable skills to build more effective and efficient deep learning models.
Key Concepts Covered
- Bias/Variance tradeoff
- Regularization techniques (L2, dropout)
- Vanishing/exploding gradients
- Gradient checking
- Mini-batch gradient descent
- Optimization algorithms (Momentum, RMSprop, Adam)
- Learning rate decay
- Batch Normalization
- TensorFlow basics