- Video: Train / Dev / Test sets
- Video: Bias / Variance
- Video: Basic Recipe for Machine Learning
- Video: Regularization
- Video: Why regularization reduces overfitting?
- Video: Dropout Regularization
- Video: Understanding Dropout
- Video: Other regularization methods
- Video: Normalizing inputs
- Video: Vanishing / Exploding Gradients
- Video: Weight Initialization for Deep Networks
- Video: Numerical approximation of gradients
- Video: Gradient checking
- Video: Gradient Checking Implementation Notes
- Quiz: Practical aspects of deep learning
- Programming Assignment: Initialization
- Programming Assignment: Regularization
- Programming Assignment: Gradient Checking
- Video: Yoshua Bengio Interview
- Video: Mini-batch gradient descent
- Video: Understanding mini-batch gradient descent
- Video: Exponentially weighted averages
- Video: Understanding exponentially weighted averages
- Video: Bias correction in exponentially weighted averages
- Video: Gradient descent with momentum
- Video: RMSprop
- Video: Adam optimization algorithm
- Video: Learning rate decay
- Video: The problem of local optima
- Quiz: Optimization algorithms
- Programming Assignment: Optimization
- Video: Yuanqing Lin Interview
- Video: Tuning process
- Video: Using an appropriate scale to pick hyperparameters
- Video: Hyperparameter tuning in practice: Pandas vs. Caviar
- Video: Normalizing activations in a network
- Video: Fitting Batch Norm into a Neural Network
- Video: Why does Batch Norm work?
- Video: Batch Norm at test time
- Video: Softmax Regression
- Video: Training a softmax classifier
- Video: Deep learning frameworks
- Video: Tensorflow
- Quiz: Hyperparameter tuning, Batch Normalization, Programming Frameworks
- Programming Assignment: Tensorflow