東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Productive and efficient data scienc...

Sarkar, Tirthajyoti.

FindBook

Google Book

Amazon

博客來

Productive and efficient data science with Python = with modularizing, memory profiles, and parallel/GPU processing /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Productive and efficient data science with Python/ by Tirthajyoti Sarkar.
其他題名:	with modularizing, memory profiles, and parallel/GPU processing /
作者:	Sarkar, Tirthajyoti.
出版者:	Berkeley, CA :Apress : : 2022.,
面頁冊數:	xxi, 383 p. :ill., digital ;24 cm.
內容註:	Chapter 1: What is Productive and Efficient Data Science -- Chapter 2: Better Programming Principles for Efficient Data Science -- Chapter 3: How to Use Python Data Science Packages more Productively -- Chapter 4: Writing Machine Learning Code More Productively -- Chapter 5: Modular and Productive Deep Learning Code -- Chapter 6: Build Your Own Machine Learning Estimator/Package -- Chapter 7: Some Cool Utility Packages -- Chapter 8: Testing the Machine Learning Code -- Chapter 9: Memory and Timing Profiling -- Chapter 10: Scalable Data Science -- Chapter 11: Parallelized Data Science -- Chapter 12: GPU-Based Data Science for High Productivity -- Chapter 13: Other Useful Skills to Master -- Chapter 14: Wrapping It Up.
Contained By:	Springer Nature eBook
標題:	Python (Computer program language) -
電子資源:	https://doi.org/10.1007/978-1-4842-8121-5
ISBN:	9781484281215

Productive and efficient data science with Python = with modularizing, memory profiles, and parallel/GPU processing /
Sarkar, Tirthajyoti.

Productive and efficient data science with Pythonwith modularizing, memory profiles, and parallel/GPU processing /[electronic resource] :by Tirthajyoti Sarkar. - Berkeley, CA :Apress :2022. - xxi, 383 p. :ill., digital ;24 cm.

Chapter 1: What is Productive and Efficient Data Science -- Chapter 2: Better Programming Principles for Efficient Data Science -- Chapter 3: How to Use Python Data Science Packages more Productively -- Chapter 4: Writing Machine Learning Code More Productively -- Chapter 5: Modular and Productive Deep Learning Code -- Chapter 6: Build Your Own Machine Learning Estimator/Package -- Chapter 7: Some Cool Utility Packages -- Chapter 8: Testing the Machine Learning Code -- Chapter 9: Memory and Timing Profiling -- Chapter 10: Scalable Data Science -- Chapter 11: Parallelized Data Science -- Chapter 12: GPU-Based Data Science for High Productivity -- Chapter 13: Other Useful Skills to Master -- Chapter 14: Wrapping It Up.

This book focuses on the Python-based tools and techniques to help you become highly productive at all aspects of typical data science stacks such as statistical analysis, visualization, model selection, and feature engineering. You'll review the inefficiencies and bottlenecks lurking in the daily business process and solve them with practical solutions. Automation of repetitive data science tasks is a key mindset that is promoted throughout the book. You'll learn how to extend the existing coding practice to handle larger datasets with high efficiency with the help of advanced libraries and packages that already exist in the Python ecosystem. The book focuses on topics such as how to measure the memory footprint and execution speed of machine learning models, quality test a data science pipelines, and modularizing a data science pipeline for app development. You'll review Python libraries which come in very handy for automating and speeding up the day-to-day tasks. In the end, you'll understand and perform data science and machine learning tasks beyond the traditional methods and utilize the full spectrum of the Python data science ecosystem to increase productivity. You will: Write fast and efficient code for data science and machine learning Build robust and expressive data science pipelines Measure memory and CPU profile for machine learning methods Utilize the full potential of GPU for data science tasks Handle large and complex data sets efficiently.

ISBN: 9781484281215

Standard No.: 10.1007/978-1-4842-8121-5doiSubjects--Topical Terms:

729789
Python (Computer program language)

LC Class. No.: QA76.73.P98 / S37 2022

Dewey Class. No.: 005.133

Productive and efficient data science with Python = with modularizing, memory profiles, and parallel/GPU processing /
LDR:03324nmm a2200337 a 4500 001 2302247
003 DE-He213
005 20220701085230.0
006 m d
007 cr nn 008maaau
008 230409s2022 cau s 0 eng d
020 $a 9781484281215 $q (electronic bk.)
020 $a 9781484281208 $q (paper)
024 7 $a 10.1007/978-1-4842-8121-5 $2 doi
035 $a 978-1-4842-8121-5
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.73.P98 $b S37 2022
072 7 $a UN $2 bicssc
072 7 $a COM031000 $2 bisacsh
072 7 $a UN $2 thema
082 0 4 $a 005.133 $2 23
090 $a QA76.73.P98 $b S245 2022
100 1 $a Sarkar, Tirthajyoti. $3 3602422
245 1 0 $a Productive and efficient data science with Python $h [electronic resource] : $b with modularizing, memory profiles, and parallel/GPU processing / $c by Tirthajyoti Sarkar.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2022.
300 $a xxi, 383 p. : $b ill., digital ; $c 24 cm.
338 $a online resource $b cr $2 rdacarrier
505 0 $a Chapter 1: What is Productive and Efficient Data Science -- Chapter 2: Better Programming Principles for Efficient Data Science -- Chapter 3: How to Use Python Data Science Packages more Productively -- Chapter 4: Writing Machine Learning Code More Productively -- Chapter 5: Modular and Productive Deep Learning Code -- Chapter 6: Build Your Own Machine Learning Estimator/Package -- Chapter 7: Some Cool Utility Packages -- Chapter 8: Testing the Machine Learning Code -- Chapter 9: Memory and Timing Profiling -- Chapter 10: Scalable Data Science -- Chapter 11: Parallelized Data Science -- Chapter 12: GPU-Based Data Science for High Productivity -- Chapter 13: Other Useful Skills to Master -- Chapter 14: Wrapping It Up.
520 $a This book focuses on the Python-based tools and techniques to help you become highly productive at all aspects of typical data science stacks such as statistical analysis, visualization, model selection, and feature engineering. You'll review the inefficiencies and bottlenecks lurking in the daily business process and solve them with practical solutions. Automation of repetitive data science tasks is a key mindset that is promoted throughout the book. You'll learn how to extend the existing coding practice to handle larger datasets with high efficiency with the help of advanced libraries and packages that already exist in the Python ecosystem. The book focuses on topics such as how to measure the memory footprint and execution speed of machine learning models, quality test a data science pipelines, and modularizing a data science pipeline for app development. You'll review Python libraries which come in very handy for automating and speeding up the day-to-day tasks. In the end, you'll understand and perform data science and machine learning tasks beyond the traditional methods and utilize the full spectrum of the Python data science ecosystem to increase productivity. You will: Write fast and efficient code for data science and machine learning Build robust and expressive data science pipelines Measure memory and CPU profile for machine learning methods Utilize the full potential of GPU for data science tasks Handle large and complex data sets efficiently.
650 0 $a Python (Computer program language) $3 729789
650 0 $a Data mining. $3 562972
650 0 $a Machine learning. $3 533906
650 1 4 $a Data Science. $3 3538937
650 2 4 $a Python. $3 3201289
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Open Source. $3 2210577
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-1-4842-8121-5
950 $a Professional and Applied Computing (SpringerNature-12059)