What are some common optimization algorithms used in machine learning?
There a number of popular machine learning optimizations. Here is a list of some commonly-used ones:
1) L2 regularization: This adds an L2 penalty term to the loss function, which forces weights to be small and prevents overfitting by shrinking large coefficients towards zero. Regularization helps prevent models from memorizing noise in the training data and improves generalization performance.
2) Dropout: This technique involves randomly masking parts of the input during training, which has the effect of decorrelating features and reducing overfitting. dropout is often used with recurrent neural networks (RNNs), where it can help improve the model's ability to capture long-term dependencies. 
3) Data augmentation: This involves creating additional copies or transformations of the training data to increase its size and diversity. This can help reduce overfitting and improve generalizability. For example, rotating images or stretching/squashing audio files can produce new examples that challenge the model.
4) Parameter sharing: This involves sharing parameters across different layers of a neural network. This can help reduce the number of parameters required for training and improve efficiency. For instance, you can use shared weight matrices between convolutional layers in a deep neural network.
5) Ensembling: This involves combining multiple trained models into a single ensemble. The output of each model is weighted according to its accuracy, and the final predictions are made based on the average of all models' outputs. Ensembles can boost prediction performance by combining the strengths of multiple models and averaging out errors. 
6) Early stopping: This involves monitoring the model's performance during training and stoping training when it reaches a certain threshold of accuracy. This can help prevent overfitting and ensure that the model has learned enough about the training data.
7) Domain randomization: This involves randomizing various aspects of the training data, such as the values of feature variables or labels, to encourage the model to learn generalizable representations instead of memorizing specific patterns in the data.
8) GPU acceleration: Using GPUs can speed up training time and improve overall performance, especially for large datasets and complex models. Many machine learning libraries provide support for using GPUs for efficient parallel processing.