Suche senden
Hochladen
test pdf
•
0 gefällt mir
•
177 views
nep_test_account
Folgen
pdf pdf
Weniger lesen
Mehr lesen
Melden
Teilen
Melden
Teilen
1 von 1
Empfohlen
test txt file
Text file
Text file
jbarevalo
My presentation
My presentation
My presentation
AmplexorDemo
Doc2pages
Doc2pages
nep_test_account
TEST
PDF TEST
PDF TEST
nep_test_account
database slide 1
database slide 1
database slide 1
nep_test_account
Doc2pages
Doc2pages
Doc2pages
nep_test_account
Database slide
Database slide
Database slide
nep_test_account
test
uat
uat
nep_test_account
Empfohlen
test txt file
Text file
Text file
jbarevalo
My presentation
My presentation
My presentation
AmplexorDemo
Doc2pages
Doc2pages
nep_test_account
TEST
PDF TEST
PDF TEST
nep_test_account
database slide 1
database slide 1
database slide 1
nep_test_account
Doc2pages
Doc2pages
Doc2pages
nep_test_account
Database slide
Database slide
Database slide
nep_test_account
test
uat
uat
nep_test_account
Boomba cha!
Baboom!
Baboom!
nep_test_account
Boomba cha!
Baboom!
Baboom!
nep_test_account
08 linear classification_2
08 linear classification_2
08 linear classification_2
nep_test_account
linear classification
linear classification
linear classification
nep_test_account
Any change in a system that allows it to perform better the second time on repetition of the same task or on another task drawn from the same population (Simon, 1983).
Lecture Notes in Machine Learning
Lecture Notes in Machine Learning
nep_test_account
The technology for building knowledge-based systems by inductive inference from examples has been demonstrated successfully in several practical applications. This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail. Results from recent studies show ways in which the methodology can be modified to deal with information that is noisy and/or incomplete. A reported shortcoming of the basic algorithm is discussed and two means of overcoming it are compared. The paper concludes with illustrations of current research directions.
Induction of Decision Trees
Induction of Decision Trees
nep_test_account
The success of data-driven solutions to dicult problems, along with the dropping costs of storing and processing mas- sive amounts of data, has led to growing interest in large- scale machine learning. This paper presents a case study of Twitter's integration of machine learning tools into its existing Hadoop-based, Pig-centric analytics platform. We begin with an overview of this platform, which handles \tra- ditional" data warehousing and business intelligence tasks for the organization. The core of this work lies in recent Pig extensions to provide predictive analytics capabilities that incorporate machine learning, focused specically on super- vised classication. In particular, we have identied stochas- tic gradient descent techniques for online learning and en- semble methods as being highly amenable to scaling out to large amounts of data. In our deployed solution, common machine learning tasks such as data sampling, feature gen- eration, training, and testing can be accomplished directly in Pig, via carefully crafted loaders, storage functions, and user-dened functions. This means that machine learning is just another Pig script, which allows seamless integration with existing infrastructure for data management, schedul- ing, and monitoring in a production environment, as well as access to rich libraries of user-dened functions and the materialized output of other scripts.
Large-Scale Machine Learning at Twitter
Large-Scale Machine Learning at Twitter
nep_test_account
Machine learning algorithms can figure out how to perform important tasks by generalizing from examples. This is often feasible and cost-effective where manual programming is not. As more data becomes available, more ambitious problems can be tackled. As a result, machine learning is widely used in computer science and other fields. However, developing successful machine learning applications requires a substantial amount of “black art” that is hard to find in textbooks. This article summarizes twelve key lessons that machine learning researchers and practitioners have learned. These include pitfalls to avoid, important issues to focus on, and answers to common questions.
A Few Useful Things to Know about Machine Learning
A Few Useful Things to Know about Machine Learning
nep_test_account
part 2
linear regression part 2
linear regression part 2
nep_test_account
Linear Regression continuous value prediction.
Linear Regression
Linear Regression
nep_test_account
Probability for Machine Learning
Probability
Probability
nep_test_account
Linear Algebra revision for the SML
Linear Algebra
Linear Algebra
nep_test_account
Statistical Machine Learning from ANU University
Introduction
Introduction
nep_test_account
Weitere ähnliche Inhalte
Mehr von nep_test_account
Boomba cha!
Baboom!
Baboom!
nep_test_account
Boomba cha!
Baboom!
Baboom!
nep_test_account
08 linear classification_2
08 linear classification_2
08 linear classification_2
nep_test_account
linear classification
linear classification
linear classification
nep_test_account
Any change in a system that allows it to perform better the second time on repetition of the same task or on another task drawn from the same population (Simon, 1983).
Lecture Notes in Machine Learning
Lecture Notes in Machine Learning
nep_test_account
The technology for building knowledge-based systems by inductive inference from examples has been demonstrated successfully in several practical applications. This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail. Results from recent studies show ways in which the methodology can be modified to deal with information that is noisy and/or incomplete. A reported shortcoming of the basic algorithm is discussed and two means of overcoming it are compared. The paper concludes with illustrations of current research directions.
Induction of Decision Trees
Induction of Decision Trees
nep_test_account
The success of data-driven solutions to dicult problems, along with the dropping costs of storing and processing mas- sive amounts of data, has led to growing interest in large- scale machine learning. This paper presents a case study of Twitter's integration of machine learning tools into its existing Hadoop-based, Pig-centric analytics platform. We begin with an overview of this platform, which handles \tra- ditional" data warehousing and business intelligence tasks for the organization. The core of this work lies in recent Pig extensions to provide predictive analytics capabilities that incorporate machine learning, focused specically on super- vised classication. In particular, we have identied stochas- tic gradient descent techniques for online learning and en- semble methods as being highly amenable to scaling out to large amounts of data. In our deployed solution, common machine learning tasks such as data sampling, feature gen- eration, training, and testing can be accomplished directly in Pig, via carefully crafted loaders, storage functions, and user-dened functions. This means that machine learning is just another Pig script, which allows seamless integration with existing infrastructure for data management, schedul- ing, and monitoring in a production environment, as well as access to rich libraries of user-dened functions and the materialized output of other scripts.
Large-Scale Machine Learning at Twitter
Large-Scale Machine Learning at Twitter
nep_test_account
Machine learning algorithms can figure out how to perform important tasks by generalizing from examples. This is often feasible and cost-effective where manual programming is not. As more data becomes available, more ambitious problems can be tackled. As a result, machine learning is widely used in computer science and other fields. However, developing successful machine learning applications requires a substantial amount of “black art” that is hard to find in textbooks. This article summarizes twelve key lessons that machine learning researchers and practitioners have learned. These include pitfalls to avoid, important issues to focus on, and answers to common questions.
A Few Useful Things to Know about Machine Learning
A Few Useful Things to Know about Machine Learning
nep_test_account
part 2
linear regression part 2
linear regression part 2
nep_test_account
Linear Regression continuous value prediction.
Linear Regression
Linear Regression
nep_test_account
Probability for Machine Learning
Probability
Probability
nep_test_account
Linear Algebra revision for the SML
Linear Algebra
Linear Algebra
nep_test_account
Statistical Machine Learning from ANU University
Introduction
Introduction
nep_test_account
Mehr von nep_test_account
(13)
Baboom!
Baboom!
Baboom!
Baboom!
08 linear classification_2
08 linear classification_2
linear classification
linear classification
Lecture Notes in Machine Learning
Lecture Notes in Machine Learning
Induction of Decision Trees
Induction of Decision Trees
Large-Scale Machine Learning at Twitter
Large-Scale Machine Learning at Twitter
A Few Useful Things to Know about Machine Learning
A Few Useful Things to Know about Machine Learning
linear regression part 2
linear regression part 2
Linear Regression
Linear Regression
Probability
Probability
Linear Algebra
Linear Algebra
Introduction
Introduction
test pdf
1.
This is to
upload to slideshare