1. Work on your final project. Recall that progress reports are due on Wed. As a rough guideline, I expect that each of you puts in 4-8 hours of work on your final project by Wed.
2. Watch the following Coursera Deep Learning 2 videos in preparation for the next class.
Key: * = required ? = optional Course 2: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization WEEK 1 Practical aspects of Deep Learning ? 12 Video: Train / Dev / Test sets [you do need to know what "dev set" means] ? 8 Video: Bias / Variance * 6 Video: Basic Recipe for Machine Learning ? 9 Video: Regularization ? 7 Video: Why regularization reduces overfitting? * 9 Video: Dropout Regularization * 7 Video: Understanding Dropout * 8 Video: Other regularization methods ? 5 Video: Normalizing inputs * 6 Video: Vanishing / Exploding gradients ? 6 Video: Weight Initialization for Deep Networks ? 6 Video: Numerical approximation of gradients ? 6 Video: Gradient checking ? 5 Video: Gradient Checking Implementation Notes Heroes of Deep Learning ? 25 Video: Yoshua Bengio interview * 36 min ? 64+25 min