第 1/3 張圖片
第 1/3 張圖片
Hands-On Machine Learning with - Paperback, by Géron Aurélien - Very Good
US $35.00
大約HK$ 272.66
或講價
狀況：
良好
曾被閱讀過的書籍，但狀況良好。封面有諸如磨痕等在內的極少損壞，但沒有穿孔或破損。精裝本書籍可能沒有書皮。封皮稍有磨損。絕大多數書頁未受損，存在極少的褶皺和破損。使用鉛筆標注文字處極少，未對文字標記，無留白處書寫文字。沒有缺頁。
運費：
US $6.88（大約 HK$ 53.60） USPS Media MailTM.
所在地：Everett, Washington, 美國
送達日期：
估計於 9月28日, 六至 10月3日, 四之間送達 運送地點 43230
退貨：
不可退貨.
保障：
請參閱物品說明或聯絡賣家以取得詳細資料。閱覽全部詳情查看保障詳情
（不符合「eBay 買家保障方案」資格）
賣家必須承擔此刊登物品的所有責任。
eBay 物品編號：266608302051
物品細節
- 物品狀況
- Book Title
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlo
- ISBN
- 9781492032649
- Subject Area
- Mathematics, Computers
- Publication Name
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow : Concepts, Tools, and Techniques to Build Intelligent Systems
- Publisher
- O'reilly Media, Incorporated
- Item Length
- 9.4 in
- Subject
- Intelligence (Ai) & Semantics, General, Data Processing, Computer Vision & Pattern Recognition
- Publication Year
- 2019
- Type
- Textbook
- Format
- Trade Paperback
- Language
- English
- Item Height
- 1.4 in
- Item Weight
- 43.2 Oz
- Item Width
- 7 in
- Number of Pages
- 856 Pages
關於產品
Product Identifiers
Publisher
O'reilly Media, Incorporated
ISBN-10
1492032646
ISBN-13
9781492032649
eBay Product ID (ePID)
8038668355
Product Key Features
Number of Pages
856 Pages
Language
English
Publication Name
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow : Concepts, Tools, and Techniques to Build Intelligent Systems
Subject
Intelligence (Ai) & Semantics, General, Data Processing, Computer Vision & Pattern Recognition
Publication Year
2019
Type
Textbook
Subject Area
Mathematics, Computers
Format
Trade Paperback
Dimensions
Item Height
1.4 in
Item Weight
43.2 Oz
Item Length
9.4 in
Item Width
7 in
Additional Product Features
Edition Number
2
Intended Audience
Trade
LCCN
2020-304725
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
006.3/1
Synopsis
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks--Scikit-Learn and TensorFlow--author Aur lien G ron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets, Now fully updated, this bestselling book uses concrete examples, minimal theory, and two production-ready Python frameworks--Scikit-Learn and TensorFlow 2--to help users gain an intuitive understanding of the concepts and tools for building intelligent systems.t systems., Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks--Scikit-Learn and TensorFlow--author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets
LC Classification Number
QA76.73.P98G45 2019
賣家提供的物品說明
賣家信用評價 （410）
- l***2 (137)- 買家留下的信用評價。過去 1 個月購買已獲認證Has not arrived yet
- 4***b (348)- 買家留下的信用評價。過去 1 個月購買已獲認證Great deal thank you
- l***b (227)- 買家留下的信用評價。過去 1 個月購買已獲認證Hard cover book great price!!!Fast shipping. Book is in great condition! A+++++++