人工智慧 / Machine Learning
◎ 人工智慧一大分支
利用資料與以往的經驗進行學習、工作程序上的優化,是定義上有智慧的學習。透過機器學習,我們不再透過既定演算法來跑迴圈與布林判斷,而是透過機率、統計等數學方式教導電腦如何透過經驗法則自動改進演算法,其中又可分為監督式與非監督式學習,兩者的差異就在訓練資料集中有沒有人工標注。
◎ 機器學習框架
目前當紅的框架有Google的TensorFlow與Keras、Facebook的Pytorch等。不論會不會寫程式,讀者們僅需基本的數學能力,如微積分、機率與統計等背景知識,就可從此三個領域輕鬆上手!有許多入門書籍一步步帶領各位初心者們,深入淺出的從基本程式語言到機器學習專業領域上的應用。
◎ 應用層面廣泛
簡單來說,機器學習是從給定的特徵中,學習出一個函式,並透過函式預測目標結果。
如奠定了深度學習基礎的類神經網路、支持向量機;基於條件機率而成的統計分類、回歸分析;以及近期最熱門的強化學習(Reinforcement Learning)中也使用了如蒙地卡羅等啓發式搜尋演算法,都是奠基於機器學習的理論之上發展而成。
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