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Das Problem, das in dieser Arbeit behandelt wird, ist kein geringeres als die Frage nach dem spekulativen Übergang der (spekulativ gedachten) Logik in die Natur, wie sie von Hegel in den letzten beiden Absätzen seiner 'Wissenschaft der Logik' aufgeworfen wird. Die Tatsache, dass Hegel diesen Übergang dabei nur andeutet, wirft interpretatorische Fragen auf, die auf ein generelles Interpretationsproblem der hegelschen Philosophie hinweisen. Im Zentrum der Untersuchung steht der für die Auflösung der Übergangsproblematik virulente Gedanke der Idee im weiten Sinn und der Gedanke der endlichen, logischen und absoluten Idee im engen Sinn.

Logic --- Hegel, Georg W.F. --- Metaphysics --- Hegel, Georg Wilhelm Friedrich, --- Hegel, Georg Wilhelm Friedrich, - 1770-1831

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As part of thebest-selling Pocket Primerseries, thisbook is designed to introducebeginners to basic machine learning algorithms using TensorFlow 2. It isintended to be a fast-paced introduction to various "core" features ofTensorFlow, with code samples that cover machine learning and TensorFlowbasics. A comprehensive appendix contains someKeras-based code samples and the underpinnings of MLPs, CNNs, RNNs, and LSTMs. The material inthe chapters illustrates how to solve a variety of tasks after which you can dofurther reading to deepen your knowledge. Companion files with all of the codesamples are available for downloading from the publisher by emailing proof of purchase to info@merclearning.com.Features:Uses Python for codesamplesCovers TensorFlow 2 APIsand DatasetsIncludes a comprehensiveappendix that covers Keras and advanced topics such as NLPs, MLPs, RNNs, LSTMsFeatures the companion files with all of thesource code examples and figures (download fromthe publisher)

TensorFlow.

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This book is designed to provide the reader with basic Python 3 programming concepts related to machine learning. The first four chapters provide a fast-paced introduction to Python 3, NumPy, and Pandas. The fifth chapter introduces the fundamental concepts of machine learning. The sixth chapter is devoted to machine learning classifiers, such as logistic regression, k-NN, decision trees, random forests, and SVMs. The final chapter includes material on NLP and RL. Keras-based code samples are included to supplement the theoretical discussion. The book also contains separate appendices for regular expressions, Keras, and TensorFlow 2. -- Provided by publisher.

Python (Computer program language)

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Artificial intelligence. --- Machine learning.

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This book is designed to provide the reader with basic Python 3 programming concepts related to machine learning. The first four chapters provide a fast-paced introduction to Python 3, NumPy, and Pandas. The fifth chapter introduces the fundamental concepts of machine learning. The sixth chapter is devoted to machine learning classifiers, such as logistic regression, k-NN, decision trees, random forests, and SVMs. The final chapter includes material on NLP and RL. Keras-based code samples are included to supplement the theoretical discussion. The book also contains separate appendices for regular expressions, Keras, and TensorFlow 2. -- Provided by publisher.

Python (Computer program language)