Welcome to the DLS Course 2: Improving Deep Neural Networks: Hyperparameter tuning, Regularization, and Optimization Discourse page!
In this course, you will open the deep learning black box to understand the processes that drive performance and generate good results systematically.
By the end, you will learn the best practices to train and develop test sets and analyze bias/variance for building deep learning applications; be able to use standard neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking; implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence; and implement a neural network in TensorFlow.
Please feel free to search for a topic that you’re interested in or start one of your own if you don’t find what you are looking for!