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An introduction to statistical learning (using R)下载
weixin_39822095
2020-06-21 12:30:25
一本很好的R语言统计学习书籍。对统计学有深入简出的讲解,并以R语言为例列举了如何实现。
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An introduction to statistical learning (using R)下载
一本很好的R语言统计学习书籍。对统计学有深入简出的讲解,并以R语言为例列举了如何实现。 相关下载链接://download.csdn.net/download/weixin_39082272/10482155?utm_source=bbsseo
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An
int
roduct
ion
to
stat
isti
ca
l l
ear
ning
(
using
R)
一本很好的R语言统计学习书籍。对统计学有深入简出的讲解,并以R语言为例列举了如何实现。
An
Int
roduct
ion
to
Stat
isti
ca
l L
ear
ning
with Appli
ca
t
ion
in R (1)
Stat
isti
ca
l l
ear
ning
refers to a set of tools for modeling and understanding complex datasets. It is a recently developed area in
stat
isti
cs, and blends with parallel developments in computer science, and in particular machine l
ear
ning
. The field encompasses many methods such as the lasso and sparse regress
ion
, classifi
ca
t
ion
and regress
ion
trees, and boosting and support vector machines. With the explos
ion
of “Big Data” problems
stat
isti
ca
l l
ear
ning
has be- come a very hot field in many scientific areas as well as marketing, finance and other business disciplines. People with
stat
isti
ca
l l
ear
ning
skills are in high demand. One of the first books in this area — The Elements of
Stat
isti
ca
l L
ear
n- ing (ESL) (Hastie, Tibshirani, and Friedman) — was published in 2001, with a second edit
ion
in 2009. ESL has become a popular text not only in
stat
isti
cs but also in related fields. One of the reasons for ESL’s popu- larity is its relatively accessible style. But ESL is
int
ended for individuals with advanced trai
ning
in the mathemati
ca
l sciences. An
Int
roduct
ion
to
Stat
isti
ca
l L
ear
ning
(ISL) arose from the perceived need for a broader and less techni
ca
l treatment of these topics. In this new book, we cover many of the same topics as ESL, but we concentrate more on the appli
ca
t
ion
s of the methods and less on the mathemati
ca
l details. We have created labs illustrating how to implement each of the
stat
isti
ca
l l
ear
ning
methods
using
the popular
stat
isti
ca
l software package R . These labs provide the reader with valuable hands-on experience. This book is appropriate for advanced undergraduates or master’s stu- dents in
Stat
isti
cs or related quantitative fields, or for individuals in other disciplines who wish to use
stat
isti
ca
l l
ear
ning
tools to analyze their data. It
ca
n be used as a textbook for a course span
ning
one or two semesters. We would like to thank several readers for valuable comments on prelim- inary drafts of this book: Pallavi Basu, Alexandra Chouldechova, Patrick Danaher, Will Fithian, Luella Fu, Sam Gross, Max Grazier G’Sell, Court- ney Paulson, Xinghao Qiao, Elisa Sheng, Noah Simon, Kean Ming Tan, Xin Lu Tan. It’s tough to make predict
ion
s, especially about the future. -Yogi Berra
Int
roduct
ion
to Deep L
ear
ning
Using
R
Int
roduct
ion
to Deep L
ear
ning
Using
R: A Step-by-Step Guide to L
ear
ning
and Implementing Deep L
ear
ning
Models
Using
R By Taweh Beysolow II English | PDF| 2017 | 240 Pages | ISBN : 1484227336 Understand deep l
ear
ning
, the nuances of its different models, and where these models
ca
n be applied. The a
Int
roduct
ion
to Deep L
ear
ning
Using
R(Apress,2017)
Understand deep l
ear
ning
, the nuances of its different models, and where these models
ca
n be applied. The abundance of data and demand for superior p
roduct
s/services have driven the development of advanced computer science techniques, among them image and speech recognit
ion
.
Int
roduct
ion
to Deep L
ear
ning
Using
Rprovides a theoreti
ca
l and practi
ca
l understanding of the models that perform these tasks by building upon the fundamentals of data science through machine l
ear
ning
and deep l
ear
ning
. This step-by-step guide will help you understand the disciplines so that you
ca
n apply the methodology in a variety of contexts. All examples are taught in the R
stat
isti
ca
l language, allowing students and profess
ion
als to implement these techniques
using
open source tools. What You’ll L
ear
n Understand the
int
uit
ion
and mathematics that power deep l
ear
ning
models Utilize various algorithms
using
the R programming language and its packages Use best practices for experimental design and variable select
ion
Practice the methodology to approach and effectively solve problems as a data scientist Evaluate the effectiveness of algorithmic solut
ion
s and enhance their predictive power Who This Book Is For Students, res
ear
chers, and data scientists who are familiar with programming
using
R. This book also is also of use for those who wish to l
ear
n how to appropriately deploy these algorithms in appli
ca
t
ion
s where they would be most useful.
Int
roduct
ion
to Deep L
ear
ning
Using
R 原版PDF by Beysolow II
It is assumed that all readers have at least an elementary understanding of
stat
isti
ca
l or computer programming, specifi
ca
lly with respect to the R programming language. Those who do not will find it much more difficult to follow the sect
ion
s of this book which give examples of code to use, and it is suggested that they return to this text upon gai
ning
that informat
ion
.
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