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.