Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization (Coursera)

After 3 weeks, you will:

• Understand industry best-practices for building deep learning applications.
• Be able to effectively use the common neural network "tricks", including initialization, L2 and dropout regularization, Batch normalization, gradient checking,
• Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence.
• Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance
• Be able to implement a neural network in TensorFlow.

Course 2 of 5 in the Deep Learning Specialization.

Syllabus

WEEK 1: Practical aspects of Deep Learning
WEEK 2: Optimization algorithms
WEEK 3: Hyperparameter tuning, Batch Normalization and Programming Frameworks