Discover and experiment with a variety of different initialization methods, apply L2 regularization and dropout to avoid model overfitting, then apply gradient checking to identify errors in a fraud detection model.
Optimization Algorithms
Develop your deep learning toolbox by adding more advanced optimizations, random minibatching, and learning rate decay scheduling to speed up your models.
Hyperparameter Tuning, Batch Normalization and Programming Frameworks
Explore TensorFlow, a deep learning framework that allows you to build neural networks quickly and easily, then train a neural network on a TensorFlow dataset.