Written in an easily accessible style, this book provides the ideal blend of theory and practical, applicable knowledge. It covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization. It treads the fine line between adequate academic rigor and overwhelming students with equations and mathematical concepts. The author includes examples based on widely available datasets and practical and theoretical problems to test understanding and application of the material. The book describes algorithms with code examples backed up by a website that provides working implementations in Python.
##並不算特彆基礎的東西,但如果已經有一定的理論背景想要進入實踐,這是一本很好的書。
評分##前半部分數據、程序都很詳細,對基礎理論知識要求不高,一般有點基礎的都能看懂,對於沒有基礎的一般在章節的最後有詳細理論的講解(如講解神經網絡的章節)。 但是後半部分有較多的錯誤,而且對理論知識的要求較高但沒有詳細講解,建議有這部分理論知識之後再去...
評分 評分看完這本書之後覺得Machine Learning in Action及其中文版簡直太隨意瞭..
評分 評分 評分 評分 評分##從算法的角度學習ml,代碼很有學習價值。
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