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Titlebook: A Full Axiomatic Development of High School Geometry; David M. Clark,Samrat Pathania Textbook 2023 The Editor(s) (if applicable) and The A

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發(fā)表于 2025-3-23 11:47:10 | 只看該作者
David M. Clark,Samrat PathaniaProvides thorough account of principles of high school geometry for professional mathematicians.Focuses on a guided-inquiry approach to teaching the axiomatic development of geometry.Includes referenc
12#
發(fā)表于 2025-3-23 16:00:28 | 只看該作者
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發(fā)表于 2025-3-23 19:04:46 | 只看該作者
Front Matterstruct a new form of expertise in order to cope with the contradictions of modern society: On the one hand, images of scientific and technical competence create utopian hopes of technological feasibility, on the other hand, these same images provoke fears of an alienating, impersonal society.
14#
發(fā)表于 2025-3-23 23:34:29 | 只看該作者
15#
發(fā)表于 2025-3-24 02:45:40 | 只看該作者
Neutral Geometry,ls using the CAPTUM framework that shows feature attribution, neuron attribution,and activation attribution.? ?.After reading this book, you will understand AI and machine learning models and be able to put that knowledge into practice to bring more accuracy and transparency to your analyses..What Y
16#
發(fā)表于 2025-3-24 08:44:10 | 只看該作者
Similar Figures,fference in scaled activations of the features in the last convolutional layer of the CNN for both classes. In doing so, we exploit the feature similarity between the two diseases for training purposes, thereby mitigating issues arising due to the scarcity of COVID-19 data. In models with overlappin
17#
發(fā)表于 2025-3-24 11:59:25 | 只看該作者
18#
發(fā)表于 2025-3-24 16:32:39 | 只看該作者
Angle Measure,int in this chapter about making science with ML? The answer, long story short, is that explainability is exactly what we need to climb “the ladder of causation” (we will talk about it in a while). We will use XAI in the domain of “knowledge discovery” with a specific focus on scientific knowledge.
19#
發(fā)表于 2025-3-24 21:05:03 | 只看該作者
20#
發(fā)表于 2025-3-24 23:25:16 | 只看該作者
Circle Measure, way to strengthen the quality of AI and Machine learning outcomes. I hope that this book will become a primer for teachers, data Science educators, and ML developers, and together we practice the art of interpretive machine learning..--Anusha Dandapani, Chief Data and Analytics Officer, UNICC and A
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