University of Michigan - Ann Arbor

Google

- **High Avoidable bias**: if it does not fit training set well on cost function (e.g., comparable to human level performance), you might train a bigger network, train longer, switch to a better optimization algorithm, like the Adam, or change the neural network architecture/hyperparameter search.
- **High Variance**: if it does not fit dev set on cost function, you may use regularization, increase the size of training set, or change the neural network architecture/hyperparameter search.
- If it does not fit test set well on cost function, you may change or increase the size of dev set.
- If it does not perform well in real world (e.g., happy users), you may change or increase the size of test set or change the cost function.

Troubleshooting a deep learning model

Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects.
https://www.deeplearning.ai/machine-learning-yearning/

Note: The content of the book is aligned with the Coursera Deeplearning.ai specialization.  https://www.deeplearning.ai/deep-learning-specialization/ 

Machine Learning Yearning (Deeplearning.ai)

One reason that people manipulate hyperparameters is to improve the bias and variance that a given model contains. Bias is the difference between the average prediction of our model and the correct value which we are trying to predict. Models with high bias pay little attention to the training data and oversimplify the model, which will lead to high errors on training and test sets. Variance is the variability of model prediction for a given data point or a value which tells us spread of our data. High Variance models do not generalize based on previous data much, if at all, and thus while it may perform well on training data it will not on test sets. Briefly, the Bias vs Variance scale is a scale between over- and under-fitting of a model. By manipulating various hyperparameters, you can change between over- and under-fitting to get the best predictions

Bias and Variance in Deep Learning

When an experienced deep learning engineer works on a new problem, they can usually use insight from previous problems to train a good model on the first try, without needing to iterate multiple times through different models. True/False?

Regularization is a modification made to a learning algorithm with the goal of reducing generalization error, but not training error.

Linear Regression is useful for modeling outputs whose relation to their inputs is linear. However, linear regression performs poorly when trying to model non-linear relationships. We can use regularization to help learning algorithms select certain models over others, particularly to avoid overfitting. 

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