Machine Learning: An Algorithmic Perspective, 2/e (Hardcover) (機器學習:算法觀點(第二版))
Stephen Marsland
- 出版商: CRC
- 出版日期: 2014-10-08
- 定價: $3,300
- 售價: 8.0 折 $2,640
- 語言: 英文
- 頁數: 457
- 裝訂: Hardcover
- ISBN: 1466583282
- ISBN-13: 9781466583283
-
相關分類:
Machine Learning、Algorithms-data-structures
-
相關翻譯:
機器學習:算法視角(Machine Learning: An Algorithmic Perspective 2/e) (簡中版)
立即出貨 (庫存 < 4)
買這商品的人也買了...
-
$3,500$3,325 -
$1,870$1,777 -
$620$490 -
$1,980$1,881 -
$2,232Neural Network Learning: Theoretical Foundations (Paperback)
-
$1,980$1,881 -
$3,990An Elementary Introduction to Statistical Learning Theory (Hardcover)
-
$400$380 -
$950$903 -
$250NumPy 攻略-Python 科學計算與數據分析 (NumPy Cookbook)
-
$680$578 -
$280$238 -
$780$616 -
$350$298 -
$420$332 -
$680$537 -
$550$468 -
$580$452 -
$780$616 -
$360$284 -
$620$484 -
$1,617Deep Learning (Hardcover)
-
$990Hands-On Machine Learning with Scikit-Learn and TensorFlow (Paperback)
-
$480$379 -
$1,850$1,758
相關主題
商品描述
A Proven, Hands-On Approach for Students without a Strong Statistical Foundation
Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area.
Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation.
New to the Second Edition
- Two new chapters on deep belief networks and Gaussian processes
- Reorganization of the chapters to make a more natural flow of content
- Revision of the support vector machine material, including a simple implementation for experiments
- New material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptron
- Additional discussions of the Kalman and particle filters
- Improved code, including better use of naming conventions in Python
Suitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code. Each chapter includes detailed examples along with further reading and problems. All of the code used to create the examples is available on the author’s website.
商品描述(中文翻譯)
自從第一版暢銷書出版以來,機器學習領域已經有幾個重要的發展,包括對機器學習算法的統計解釋的工作越來越多。不幸的是,沒有強大統計背景的計算機科學學生往往很難在這個領域入門。
為了彌補這個不足,《機器學習:算法透視》第二版幫助學生理解機器學習的算法。它讓他們走上掌握相關數學和統計知識以及必要的編程和實驗的道路。
第二版的新內容包括:
- 兩個關於深度信念網絡和高斯過程的新章節
- 重新組織章節,使內容更加自然流暢
- 修訂支持向量機材料,包括一個用於實驗的簡單實現
- 關於隨機森林、感知器收斂定理、準確度方法和多層感知器的共軛梯度優化的新材料
- 關於卡爾曼濾波器和粒子濾波器的進一步討論
- 改進的代碼,包括更好地使用Python的命名慣例
這本書適合初級一學期課程和更高級的課程,強烈鼓勵學生通過編程實踐。每章都包含詳細的示例,以及進一步閱讀和問題。用於創建示例的所有代碼都可以在作者的網站上找到。