Machine learning for OpenCV : advanced methods and deep learning

lgli/F:\!upload\_books\Machine Learning for OpenCV.epub

Machine learning for OpenCV : advanced methods and deep learning 🔍

Michael Beyeler Packt Publishing Limited, Бестселлеры O'Reilly, Санкт-Петербург [и др.], Russia, 2018

英语 [en] · EPUB · 28.0MB · 2018 · 📘 非小说类图书 · 🚀/lgli/lgrs/nexusstc/zlib · Save

描述

"A practical introduction to the world of machine learning and image processing using OpenCV and Python. Computer vision is one of today's most exciting application fields of Machine Learning, From self-driving cars to medical diagnosis, computer vision has been widely used in various domains. This course will cover essential concepts such as classifiers and clustering and will also help you get acquainted with neural networks and Deep Learning to address real-world problems. The course will also guide you through creating custom graphs and visualizations, and show you how to go from raw data to beautiful visualizations. By the end of this course, you will be ready to create your own ML system and will also be able to take on your own machine learning problems."--Resource description page. Read more...
Abstract: "A practical introduction to the world of machine learning and image processing using OpenCV and Python. Computer vision is one of today's most exciting application fields of Machine Learning, From self-driving cars to medical diagnosis, computer vision has been widely used in various domains. This course will cover essential concepts such as classifiers and clustering and will also help you get acquainted with neural networks and Deep Learning to address real-world problems. The course will also guide you through creating custom graphs and visualizations, and show you how to go from raw data to beautiful visualizations. By the end of this course, you will be ready to create your own ML system and will also be able to take on your own machine learning problems."--Resource description page

备用文件名

lgrsnf/F:\!upload\_books\Machine Learning for OpenCV.epub

备用文件名

nexusstc/Machine learning for OpenCV : advanced methods and deep learning/bf86cd97f530a8222c1be67a4f603e88.epub

备用文件名

zlib/no-category/Michael Beyeler/Machine learning for OpenCV : advanced methods and deep learning_5404479.epub

备选标题

Python Machine Learning : Learn How to Build Powerful Python Machine Learning Algorithms to Generate Useful Data Insights with This Data Analysis Tutorial

备选标题

Python machine learning : unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics

备选标题

Введение в машинное обучение с помощью Python: руководство для специалистов по работе с данными: [полноцветное издание]

备选标题

Python и машинное обучение: наука и искусство построения алгоритмов, которые извлекают знания из данных

备选标题

Introduction to Machine Learning with Python : A Guide for Data Scientists

备选标题

Python Data Science Handbook : Essential Tools for Working with Data

备选标题

Python для сложных задач: наука о данных и машинное обучение: 16+

备选标题

Python Machine Learning, 1st Edition

备选作者

Андреас Мюллер, Сара Гвидо; [перевод с английского и редакция А. В. Груздева]

备选作者

Дж. Вандер Плас; [перевела с английского И. Пальти]

备选作者

Себастьян Рашка; перевод с англ. А. В. Логунова

备选作者

Jacob T. Vanderplas; Jake VanderPlas

备选作者

Andreas C. Mueller, Sarah Guido

备选作者

Andreas C. Müller; Sarah Guido

备选作者

Müller, Andreas, Guido, Sarah

备选作者

Raschka, Sebastian

备选作者

Плас, Джейк Вандер

备选作者

Sebastian Raschka

备选作者

Рашка, Себастьян

备选作者

Мюллер, Андреас

备用出版商

O'Reilly Media; O'Reilly Media, Inc.

备用出版商

O'Reilly Media, Incorporated

备用出版商

Диалектика

备用出版商

ДМК Пресс

备用出版商

Питер

备用版本

Community experience distilled, Community experience distilled, England, 2016

备用版本

First edition, Beijing; Boston; Farnham; Sebastopol; Tokyo, 2016

备用版本

Бестселлеры O'Reilly, Санкт-Петербург [и др.], Russia, 2020

备用版本

Community experience distilled, Birmingham, UK, 2015

备用版本

First edition, third release, Sebastopol, CA, 2017

备用版本

United Kingdom and Ireland, United Kingdom

备用版本

United States, United States of America

备用版本

Packt Publishing, Birmingham, UK, 2015

备用版本

Цветное издание, Москва, Russia, 2017

备用版本

O'Reilly Media, Sebastopol, CA, 2017

备用版本

O'Reilly Media, Sebastopol, CA, 2016

备用版本

First edition, Sebastopol, CA, 2016

备用版本

Москва [и др.], Russia, 2017

备用版本

1st Edition, Dec 10, 2016

备用版本

September 25, 2016

备用版本

1st edition, 2015

备用版本

Beijing, 2017

备用版本

1, FR, 2016

备用版本

1, PS, 2017

元数据中的注释

lg2474718

元数据中的注释

{"last_page":1,"publisher":"Packt Publishing"}

元数据中的注释

Includes index.

元数据中的注释

gaaagpl

元数据中的注释

Предм. указ.: с. 465-472
Пер.: Müller, Andreas C. Introduction to machine leaning with Python Beijing [etc.] : O'Reilly, cop. 2017 978-1-449-36941-5

元数据中的注释

РГБ

元数据中的注释

Russian State Library [rgb] MARC:
=001 008925002
=005 20180420133212.0
=008 170623s2017\\\\ru\||||\\\\\\\0||\|\rus|d
=017 \\ $a КН-П-18-028128 $b RuMoRKP
=017 \\ $a 17-47693 $b RuMoRKP
=020 \\ $a 978-5-9908910-8-1 $c 1000 экз.
=040 \\ $a RuMoRGB $b rus $e rcr $d RuMoRGB
=041 1\ $a rus $h eng
=044 \\ $a ru
=084 \\ $a З973.2-018.19Python,0 $2 rubbk
=100 1\ $a Мюллер, Андреас
=245 00 $a Введение в машинное обучение с помощью Python $h [Текст] : $b руководство для специалистов по работе с данными : [полноцветное издание] $c Андреас Мюллер, Сара Гвидо ; [перевод с английского и редакция А. В. Груздева]
=260 \\ $a Москва [и др.] $b Диалектика $c 2017
=300 \\ $a 472, [1] с. $b ил., табл., цв. ил. $c 24 см
=336 \\ $a текст (text) $b txt $2 rdacontent
=337 \\ $a неопосредованный (unmediated) $b n $2 rdamedia
=338 \\ $a том (volume) $b nc $2 rdacarrier
=500 \\ $a Предм. указ.: с. 465-472
=534 \\ $p Пер.: $a Müller, Andreas C. $t Introduction to machine leaning with Python $c Beijing [etc.] : O'Reilly, cop. 2017 $z 978-1-449-36941-5
=650 \7 $a Вычислительная техника -- Вычислительные машины электронные цифровые -- Программирование -- Языки программирования -- Python -- Пособие для специалистов $2 rubbk
=650 \7 $a PYTHON, язык программирования $0 RU\NLR\AUTH\661326547 $2 nlr_sh
=700 1\ $a Гвидо, Сара
=852 \\ $a РГБ $b FB $j 2 17-43/104 $x 90
=852 7\ $a РГБ $b CZ2 $h З973.2-018/М98 $x 83
=852 \\ $a РГБ $b FB $j 2 18-18/413 $x 90

元数据中的注释

Фактическая дата выхода в свет - 2019
Пер.: Plas, Jake Vander Python data science handbook Beijing [etc.] : O'Reilly, cop. 2017 978-1491912058

元数据中的注释

Russian State Library [rgb] MARC:
=001 010135037
=005 20200818133845.0
=008 191025s2020\\\\ru\\\\\\\\\\\\|||\|\rus|d
=017 \\ $a КН-П-20-041981 $b RuMoRKP
=017 \\ $a КН-П-19-080267 $b RuMoRKP
=020 \\ $a 978-5-4461-0914-2
=040 \\ $a RuMoRGB $b rus $e rcr
=041 1\ $a rus $h eng
=044 \\ $a ru
=084 \\ $a З973.2-018.19Python,0 $2 rubbk
=100 1\ $a Плас, Джейк Вандер
=245 00 $a Python для сложных задач: наука о данных и машинное обучение : $b 16+ $c Дж. Вандер Плас ; [перевела с английского И. Пальти]
=260 \\ $a Санкт-Петербург [и др.] $b Питер $c 2020
=300 \\ $a 572, [1] с. $b ил., табл. $c 24 см
=336 \\ $a Текст (визуальный)
=337 \\ $a непосредственный
=490 0\ $a Бестселлеры O'Reilly
=500 \\ $a Фактическая дата выхода в свет - 2019
=534 \\ $p Пер.: $a Plas, Jake Vander $t Python data science handbook $c Beijing [etc.] : O'Reilly, cop. 2017 $z 978-1491912058
=650 \7 $a Техника. Технические науки -- Энергетика. Радиоэлектроника -- Радиоэлектроника -- Вычислительная техника -- Вычислительные машины электронные цифровые -- Программирование -- Языки программирования -- Python $2 rubbk
=852 \\ $a РГБ $b FB $j 3 19-60/157 $x 90
=852 7\ $a РГБ $b CZ2 $h З973.2-018/П37 $x 83
=852 \\ $a РГБ $b FB $j 3 20-32/18 $x 90
=852 \\ $a РГБ $b ORF $x 82

元数据中的注释

Пер.: Plas, Jake Vander Python data science handbook Beijing [etc.]: O'Reilly 978-1491912058

元数据中的注释

Russian State Library [rgb] MARC:
=001 009883865
=005 20190214131403.0
=008 190211s2018\\\\ru\\\\\\\\\\\\000\|\rus|d
=017 \\ $a КН-П-19-007117 $b RuMoRKP
=020 \\ $a 978-5-4461-0914-2
=040 \\ $a RuMoRGB $b rus $e rcr
=041 1\ $a rus $h eng
=044 \\ $a ru
=084 \\ $a З973.2-018.19Python,0 $2 rubbk
=100 1\ $a Плас, Джейк Вандер
=245 00 $a Python для сложных задач: наука о данных и машинное обучение $h [Текст] $c Дж. Вандер Плас ; [перевела с английского И. Пальти]
=260 \\ $a Санкт-Петербург [и др.] $b Питер $c 2018
=300 \\ $a 572, [1] с. $b ил., табл. $c 23 см
=336 \\ $a текст (text) $b txt $2 rdacontent
=337 \\ $a неопосредованный (unmediated) $b n $2 rdamedia
=338 \\ $a том (volume) $b nc $2 rdacarrier
=490 0\ $a Бестселлеры O'Reilly
=534 \\ $p Пер.: $a Plas, Jake Vander $t Python data science handbook $c Beijing [etc.]: O'Reilly $z 978-1491912058
=650 \7 $a Вычислительная техника -- Вычислительные машины электронные цифровые -- Программирование -- Языки программирования -- Python $2 rubbk
=852 \\ $a РГБ $b FB $j 3 19-6/167 $x 90
=852 \\ $a РГБ $b ORF $x 82

元数据中的注释

Предм. указ.: с. 408-417
Пер.: Raschka, Sebastian Python machine learning Birmingham ; Mumbai : Packt, cop. 2016 978-1-78355-513-0

元数据中的注释

Russian State Library [rgb] MARC:
=001 010416772
=005 20200929115449.0
=008 200713s2017\\\\ru\\\\\\\\\\\\|||\|\rus\d
=017 \\ $a 7086-20 $b RuMoRGB
=020 \\ $a 978-5-97060-409-0 $c 200 экз.
=040 \\ $a RuMoRGB $b rus $e rcr
=041 1\ $a rus $h eng
=044 \\ $a ru
=084 \\ $a З973.236-018.19Python,07 $2 rubbk
=100 1\ $a Рашка, Себастьян
=245 00 $a Python и машинное обучение : $b наука и искусство построения алгоритмов, которые извлекают знания из данных $c Себастьян Рашка ; перевод с англ. А. В. Логунова
=260 \\ $a Москва $b ДМК Пресс $c 2017
=300 \\ $a 417 с. $b ил., цв. ил., табл. $c 25 см
=336 \\ $a Текст (визуальный)
=337 \\ $a непосредственный
=490 0\ $a Цветное издание
=500 \\ $a Предм. указ.: с. 408-417
=534 \\ $p Пер.: $a Raschka, Sebastian $t Python machine learning $c Birmingham ; Mumbai : Packt, cop. 2016 $z 978-1-78355-513-0
=650 \7 $a Техника. Технические науки -- Энергетика. Радиоэлектроника -- Энергетика -- Вычислительная техника -- Вычислительные машины электронные цифровые -- Обучающие машины -- Языки программирования -- Python -- Пособие для специалистов $2 rubbk
=852 \\ $a РГБ $b FB $j 2 20-44/151 $x 90

备用描述

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book <ul> <li>Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization </li> <li>Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms </li> <li>Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets </li></ul>Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn <ul> <li>Explore how to use different machine learning models to ask different questions of your data </li> <li>Learn how to build neural networks using Keras and Theano </li> <li>Find out how to write clean and elegant Python code that will optimize the strength of your algorithms </li> <li>Discover how to embed your machine learning model in a web application for increased accessibility </li> <li>Predict continuous target outcomes using regression analysis </li> <li>Uncover hidden patterns and structures in data with clustering </li> <li>Organize data using effective pre-processing techniques </li> <li>Get to grips with sentiment analysis to delve deeper into textual and social media data </li></ul>In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approach Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.

备用描述

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analyticsAbout This BookLeverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualizationLearn effective strategies and best practices to improve and optimize machine learning systems and algorithmsAsk - and answer - tough questions of your data with robust statistical models, built for a range of datasetsWho This Book Is ForIf you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning - whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will LearnExplore how to use different machine learning models to ask different questions of your dataLearn how to build neural networks using Pylearn 2 and TheanoFind out how to write clean and elegant Python code that will optimize the strength of your algorithmsDiscover how to embed your machine learning model in a web application for increased accessibilityPredict continuous target outcomes using regression analysisUncover hidden patterns and structures in data with clusteringOrganize data using effective pre-processing techniquesGet to grips with sentiment analysis to delve deeper into textual and social media dataIn DetailMachine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data - its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Pylearn2, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approachPython Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models

备用描述

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analyticsKey FeaturesLeverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualizationLearn effective strategies and best practices to improve and optimize machine learning systems and algorithmsAsk – and answer – tough questions of your data with robust statistical models, built for a range of datasetsBook DescriptionMachine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. What you will learnExplore how to use different machine learning models to ask different questions of your dataLearn how to build neural networks using Keras and TheanoFind out how to write clean and elegant Python code that will optimize the strength of your algorithmsDiscover how to embed your machine learning model in a web application for increased accessibilityPredict continuous target outcomes using regression analysisUncover hidden patterns and structures in data with clusteringOrganize data using effective pre-processing techniquesGet to grips with sentiment analysis to delve deeper into textual and social media dataWho this book is forIf you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource.

备用描述

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask -- and answer -- tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning -- whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Pylearn 2 and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data -- its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Pylearn2, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer som..

备用描述

Link to the GitHub Repository containing the code examples and additional material: (https://github.com/rasbt/python-machine-learning-book) https://github.com/rasbt/python-machi...
Many of the most innovative breakthroughs and exciting new technologies can be attributed to applications of machine learning. We are living in an age where data comes in abundance, and thanks to the self-learning algorithms from the field of machine learning, we can turn this data into knowledge. Automated speech recognition on our smart phones, web search engines, e-mail spam filters, the recommendation systems of our favorite movie streaming services machine learning makes it all possible.
Thanks to the many powerful open-source libraries that have been developed in recent years, machine learning is now right at our fingertips. Python provides the perfect environment to build machine learning systems productively.
This book will teach you the fundamentals of machine learning and how to utilize these in real-world applications using Python. Step-by-step, you will expand your skill set with the best practices for transforming raw data into useful information, developing learning algorithms efficiently, and evaluating results.
You will discover the different problem categories that machine learning can solve and explore how to classify objects, predict continuous outcomes with regression analysis, and find hidden structures in data via clustering. You will build your own machine learning system for sentiment analysis and finally, learn how to embed your model into a web app to share with the world

备用描述

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.
You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Muller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.
With this book, you'll learn:
Fundamental concepts and applications of machine learning
Advantages and shortcomings of widely used machine learning algorithms
How to represent data processed by machine learning, including which data aspects to focus on
Advanced methods for model evaluation and parameter tuning
The concept of pipelines for chaining models and encapsulating your workflow
Methods for working with text data, including text-specific processing techniques
Suggestions for improving your machine learning and data science skills

备用描述

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.With this book, you'll learn:Fundamental concepts and applications of machine learningAdvantages and shortcomings of widely used machine learning algorithmsHow to represent data processed by machine learning, including which data aspects to focus onAdvanced methods for model evaluation and parameter tuningThe concept of pipelines for chaining models and encapsulating your workflowMethods for working with text data, including text-specific processing techniquesSuggestions for improving your machine learning and data science skills

备用描述

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them allIPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, youll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

备用描述

For Many Researchers, Python Is A First-class Tool Mainly Because Of Its Libraries For Storing, Manipulating, And Gaining Insight From Data. Several Resources Exist For Individual Pieces Of This Data Science Stack, But Only With The Python Data Science Handbook Do You Get Them All—ipython, Numpy, Pandas, Matplotlib, Scikit-learn, And Other Related Tools. Working Scientists And Data Crunchers Familiar With Reading And Writing Python Code Will Find This Comprehensive Desk Reference Ideal For Tackling Day-to-day Issues: Manipulating, Transforming, And Cleaning Data; Visualizing Different Types Of Data; And Using Data To Build Statistical Or Machine Learning Models. Quite Simply, This Is The Must-have Reference For Scientific Computing In Python.-- Ipython: Beyond Normal Python -- Introduction To Numpy -- Data Manipulation With Pandas -- Visualization With Matplatlib -- Machine Learning. Jake Vanderplas. Includes Index.

备用描述

Giving computers the ability to learn from data
Training machine learning algorithms for classification
A tour of machine learning classifiers using Scikit-learn
Building good training sets : data preprocessing
Compressing data via dimensionality reduction
Learning best practices for model evaluation and hyperparameter tuning
Combining different models for ensemble learning
Applying machine learning to sentiment analysis
Embedding a machine learning model into a web application
Predicting continuous target variables with regression analysis
Working with unlabeled data : clustering analysis
Training artificial neural networks for image recognition
Parallelizing neural network training with Theano.

备用描述

**Revision History**
December 2016: First Edition
2016-11-17: First Release

开源日期

2020-02-15

更多信息……

🚀 快速下载

成为会员以支持书籍、论文等的长期保存。为了感谢您对我们的支持，您将获得高速下载权益。❤️

🐢 低速下载

由可信的合作方提供。更多信息请参见常见问题解答。（可能需要验证浏览器——无限次下载！）

低速服务器（合作方提供） #1 （稍快但需要排队）
低速服务器（合作方提供） #2 （稍快但需要排队）
低速服务器（合作方提供） #3 （稍快但需要排队）
低速服务器（合作方提供） #4 （稍快但需要排队）
低速服务器（合作方提供） #5 （无需排队，但可能非常慢）
低速服务器（合作方提供） #6 （无需排队，但可能非常慢）
低速服务器（合作方提供） #7 （无需排队，但可能非常慢）
低速服务器（合作方提供） #8 （无需排队，但可能非常慢）
低速服务器（合作方提供） #9 （无需排队，但可能非常慢）
下载后：在我们的查看器中打开

所有选项下载的文件都相同，应该可以安全使用。即使这样，从互联网下载文件时始终要小心。例如，确保您的设备更新及时。

显示外部下载

对于大文件，我们建议使用下载管理器以防止中断。
推荐的下载管理器：JDownloader
您将需要一个电子书或 PDF 阅读器来打开文件，具体取决于文件格式。
推荐的电子书阅读器：Anna的档案在线查看器、ReadEra和Calibre
使用在线工具进行格式转换。
推荐的转换工具：CloudConvert和PrintFriendly
您可以将 PDF 和 EPUB 文件发送到您的 Kindle 或 Kobo 电子阅读器。
推荐的工具：亚马逊的“发送到 Kindle”和djazz 的“发送到 Kobo/Kindle”
支持作者和图书馆
✍️ 如果您喜欢这个并且能够负担得起，请考虑购买原版，或直接支持作者。
📚 如果您当地的图书馆有这本书，请考虑在那里免费借阅。

📂 文件质量

通过反馈此文件的质量来改进社区！ 🙌