Artificial Intelligence in Medical Sciences and Psychology
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491_9781484282168
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Chapter 1: An Introduction to Artificial Intelligence for Medical SciencesChapter goal: This is the initial chapter. Subsequently, it encapsulates the specific context and structure of the book. Then, it states the varying medical specialties central to this book. Likewise, it properly presents independent subsets of artificial intelligence. Besides that, it unveils valuable tools for undertaking exercises; Python programming language, distribution package, and libraries. Afterward, it sufficiently acquaints you with different algorithms, including when to carry them out.Sub-topics:? Context of the book.? The books central point.? Artificial Intelligence subsets covered in this book.? Structure of the book.? Tools that this book implements.? Python distribution package.? Anaconda distribution package.? Jupyter Notebook.? Python libraries.? Encapsulating Artificial Intelligence.? Debunking algorithms.? Debunking supervised algorithms.? Debunking unsupervised algorithms.? Debunking Artificial Neural Networks.Chapter 2: Realizing Patterns in Common Diseases with Neural NetworksChapter goal: This chapter purportedly contains the application of artificial neural networks in modelling medical data. It properly instigates deep belief networks to model data and predicts whether a patient suffers from an ordinary disease (i.e., pneumonia and diabetes). Equally, it appraises the networks with fundamental metrics to discern the magnitude to which the networks set apart patients who suffer from the disease from those who do not.Sub-topics:? Classifying patients Cardiovascular disease diagnosis outcome data by executing a deepbelief network.? Preprocessing the Cardiovascular disease diagnosis outcome data.? Debunking deep belief networks.o Designing the deep belief network.o Relu Activation function.o Sigmoid activation function.? Training the deep belief network.? Outlining the deep belief networks predictions.? Considering the deep bel
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