Neural Networks
- Neural Networks Basics [1]
- Shallow neural networks [1]
- Deep Neural Networks [1]
- DNN architecture
Practical aspects of Deep Learning [2]
- Multilayer perceptron (MLP) [4]
- Backpropagation [4]
- Bias / Variance [2]
- Regularization [2]
- Dropout [2]
- Vanishing / Exploding gradients [2]
- Weight Initialization [2]
- Gradient checking [2]
Optimization algorithms [2]
- Gradient descent [2]
- Gradient descent with momentum [2]
- Stochastic gradient descent [4]
- RMSprop [2]
- Adam [2]
- Learning rate decay [2]
Hyperparameter tuning, Batch Normalization and Programming Frameworks [2]
ML Strategy [3]
- Orthogonalization [3]
- Single number evaluation metric [3]
- Satisficing and Optimizing metric [3]
- Train/dev/test distributions [3]
- Size of the dev and test sets [3]
- When to change dev/test sets and metrics [3]
- Avoidable bias [3]
- human-level performance [3]
- Improving model performance [3]
- error analysis [3]
- Training and testing on different distribution [3]
- data mismatch [3]
- Transfer learning [3]
- Multi-task learning [3]
- end-to-end deep learning [3]