To be specific, the book assumes that readers have a basic understanding of the following topics: ML models such as clustering, logistic regression, decision trees, collaborative filtering, and various neural network architectures including feed-forward, recurrent, convolutional, and transformer ML techniques such as supervised versus unsupervised, gradient descent, objective/loss function, regularization, generalization, and hyperparameter tuning Metrics such as accuracy, F1, precision, recall, ROC, mean squared error, and log-likelihood Statistical concepts such as variance, probability, and
...more
