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Jiang Hui (York University Toronto) - Machine Learning Fundamentals A Concise Introduction - Paperback Switch Acc Theo must learn to embrace

Jiang Hui (York University Toronto) - Machine Learning Fundamentals A Concise Introduction - Paperback Switch Acc Theo must learn to embraceBinding: Paperback Description: This lucid accessible introduction to supervised machine learning presents core concepts in a focused and logical way that is easy for beginners to follow. The author assumes basic calculus linear algebra probability and statistics but no prior exposure to machine learning. Coverage includes widely used traditional methods such as SV Ms boosted trees HM Ms and LD As plus popular deep learning methods such as convolution

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Theo must learn to embrace her own power if she has any hope of standing against the girl she once called her heart's sister

Barcode: 9780367236021

Opening with a general introduction and overview of twentieth century Britain the book contains a wealth of chronologies facts and figures introductions to major themes the historiography of twentieth century Britain a guide to sources and resources biographies of the most important figures and a dictionary of key terms providing a comprehensive and up - to - date introduction to this key period of change and development in this most urban of nations

As she struggles with her new reality Paige learns that the apocalypse did not happen by accident

This item's title is: Dolenz Sings R

Jiang Hui (York University Toronto) - Machine Learning Fundamentals A Concise Introduction - Paperback Switch Acc Theo must learn to embraceBinding: Paperback Description: This lucid accessible introduction to supervised machine learning presents core concepts in a focused and logical way that is easy for beginners to follow. The author assumes basic calculus linear algebra probability and statistics but no prior exposure to machine learning. Coverage includes widely used traditional methods such as SV Ms boosted trees HM Ms and LD As plus popular deep learning methods such as convolution

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