Python Data Science Cheat Sheet - Keras

This is not a formally published print book but a Keras cheat sheet from the "Python Data Science Cheat Sheets" series released by DataCamp and translated and circulated by Chinese-language communities (such as the "Dainiao" translator team), distributed online as a single-page PDF. In one page, using categorized diagrams and minimal code snippets, the cheat sheet lays out the core Keras modeling workflow: starting from the two modeling approaches—the Sequential model and the functional API—it shows how to write common layers such as Dense, Conv2D, and LSTM and where their parameters go; then covers using compile() to specify the optimizer, loss function, and evaluation metrics, using fit() to set epochs, batch_size, and validation_split to train a model, using evaluate()/predict() for evaluation and prediction, and finally gives common code for saving and loading models and for checking summary() and the history training curves. The cheat sheet itself does not explain theory such as backpropagation or the derivation of loss functions, nor does it provide complete project case studies; its purpose is to be "a navigation chart at hand while coding": a quick reference when you forget a method or parameter name, so you do not have to keep flipping through documentation. It is best read alongside the official Keras documentation or a systematic textbook. Note: the file corresponding to this entry comes from an online collection and is not a formal publication, so the publisher, publication year, ISBN, and similar details cannot be verified.