國立虎尾科技大學 |

Ensemble Learning for AI Developers = Learn Bagging, Stacking, and Boosting Methods with Use Cases /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Ensemble Learning for AI Developers/ by Alok Kumar, Mayank Jain.
其他題名:	Learn Bagging, Stacking, and Boosting Methods with Use Cases /
作者:	Kumar, Alok.
其他作者:	Jain, Mayank.
面頁冊數:	XVI, 136 p. 51 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Open Source. -
電子資源:	https://doi.org/10.1007/978-1-4842-5940-5
ISBN:	9781484259405

Ensemble Learning for AI Developers = Learn Bagging, Stacking, and Boosting Methods with Use Cases /
Kumar, Alok.

Ensemble Learning for AI DevelopersLearn Bagging, Stacking, and Boosting Methods with Use Cases /[electronic resource] :by Alok Kumar, Mayank Jain. - 1st ed. 2020. - XVI, 136 p. 51 illus.online resource.

Chapter 1: Why Ensemble Techniques Are Needed -- Chapter 2: Mix Training Data -- Chapter 3: Mix Models -- Chapter 4: Mix Combinations -- Chapter 5: Use Ensemble Learning Libraries -- Chapter 6: Tips and Best Practices.-.

Use ensemble learning techniques and models to improve your machine learning results. Ensemble Learning for AI Developers starts you at the beginning with an historical overview and explains key ensemble techniques and why they are needed. You then will learn how to change training data using bagging, bootstrap aggregating, random forest models, and cross-validation methods. Authors Kumar and Jain provide best practices to guide you in combining models and using tools to boost performance of your machine learning projects. They teach you how to effectively implement ensemble concepts such as stacking and boosting and to utilize popular libraries such as Keras, Scikit Learn, TensorFlow, PyTorch, and Microsoft LightGBM. Tips are presented to apply ensemble learning in different data science problems, including time series data, imaging data, and NLP. Recent advances in ensemble learning are discussed. Sample code is provided in the form of scripts and the IPython notebook. You will: Understand the techniques and methods utilized in ensemble learning Use bagging, stacking, and boosting to improve performance of your machine learning projects by combining models to decrease variance, improve predictions, and reduce bias Enhance your machine learning architecture with ensemble learning.

ISBN: 9781484259405

Standard No.: 10.1007/978-1-4842-5940-5doiSubjects--Topical Terms:

1113081
Open Source.

LC Class. No.: Q334-342

Dewey Class. No.: 006.3

Ensemble Learning for AI Developers = Learn Bagging, Stacking, and Boosting Methods with Use Cases /
LDR:02863nam a22003855i 4500 001 1028712
003 DE-He213
005 20200701161816.0
007 cr nn 008mamaa
008 210318s2020 xxu| s |||| 0|eng d
020 $a 9781484259405 $9 978-1-4842-5940-5
024 7 $a 10.1007/978-1-4842-5940-5 $2 doi
035 $a 978-1-4842-5940-5
050 4 $a Q334-342
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
100 1 $a Kumar, Alok. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1325295
245 1 0 $a Ensemble Learning for AI Developers $h [electronic resource] : $b Learn Bagging, Stacking, and Boosting Methods with Use Cases / $c by Alok Kumar, Mayank Jain.
250 $a 1st ed. 2020.
264 1 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2020.
300 $a XVI, 136 p. 51 illus. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a Chapter 1: Why Ensemble Techniques Are Needed -- Chapter 2: Mix Training Data -- Chapter 3: Mix Models -- Chapter 4: Mix Combinations -- Chapter 5: Use Ensemble Learning Libraries -- Chapter 6: Tips and Best Practices.-.
520 $a Use ensemble learning techniques and models to improve your machine learning results. Ensemble Learning for AI Developers starts you at the beginning with an historical overview and explains key ensemble techniques and why they are needed. You then will learn how to change training data using bagging, bootstrap aggregating, random forest models, and cross-validation methods. Authors Kumar and Jain provide best practices to guide you in combining models and using tools to boost performance of your machine learning projects. They teach you how to effectively implement ensemble concepts such as stacking and boosting and to utilize popular libraries such as Keras, Scikit Learn, TensorFlow, PyTorch, and Microsoft LightGBM. Tips are presented to apply ensemble learning in different data science problems, including time series data, imaging data, and NLP. Recent advances in ensemble learning are discussed. Sample code is provided in the form of scripts and the IPython notebook. You will: Understand the techniques and methods utilized in ensemble learning Use bagging, stacking, and boosting to improve performance of your machine learning projects by combining models to decrease variance, improve predictions, and reduce bias Enhance your machine learning architecture with ensemble learning.
650 2 4 $a Open Source. $3 1113081
650 2 4 $a Python. $3 1115944
650 1 4 $a Artificial Intelligence. $3 646849
650 0 $a Computer programming. $3 527822
650 0 $a Open source software. $3 561177
650 0 $a Python (Computer program language). $3 1127623
650 0 $a Artificial intelligence. $3 559380
700 1 $a Jain, Mayank. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1325296
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9781484259399
776 0 8 $i Printed edition: $z 9781484259412
856 4 0 $u https://doi.org/10.1007/978-1-4842-5940-5
912 $a ZDB-2-CWD
912 $a ZDB-2-SXPC
950 $a Professional and Applied Computing (SpringerNature-12059)
950 $a Professional and Applied Computing (R0) (SpringerNature-43716)