Improving Deep Neural Networks: Hyper-parameter tuning, Regularization and Optimization
This course teachs one the “magic” of getting deep learning to work well. Rather than the deep learning process being a black box, one understands what drives performance, and is able to more systematically get good results, besides learning TensorFlow.
Students are able to:
- Understand industry best-practices for building deep learning applications.
- Effectively use the common neural network “tricks”, including initialization, L2 and dropout regularization, Batch normalization, 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.
- Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance
- Implement a neural network in TensorFlow.
This is the second course of the Deep Learning Specialization.