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introduce typical methods used for feature selection, including filter, wrapper, subset selection
Feature selection
Feature selection
Dong Guo
Methods wrapper evaluate subsets of variables which allows, unlike filter approaches, to detect the possible interactions between variables.
Wrapper feature selection method
Wrapper feature selection method
Amir Razmjou
K-Folds cross-validation is one method that attempts to maximize the use of the available data for training and then testing a model. It is particularly useful for assessing model performance, as it provides a range of accuracy scores across (somewhat) different data sets.
K-Folds Cross Validation Method
K-Folds Cross Validation Method
SHUBHAM GUPTA
Data Analytics & Machine Learning - Feature Selection with Trajan Simulator in Forward Selection Approach, Backward Selection Approach and Genetic Algorithms
Feature Selection in Machine Learning
Feature Selection in Machine Learning
Upekha Vandebona
Feature selection concepts and methods
Feature selection concepts and methods
Reza Ramezani
Machine learning algorithm ( KNN ) for classification and regression . Lazy learning , competitive and Instance based learning.
K - Nearest neighbor ( KNN )
K - Nearest neighbor ( KNN )
Mohammad Junaid Khan
A brief introduction to Logistic Regression Analysis, its assumptions, and its application.
Logistic Regression Analysis
Logistic Regression Analysis
COSTARCH Analytical Consulting (P) Ltd.
Review of Do and Batzoglou. "What is the expectation maximization algorith?" Nat. Biotechnol. 2008;26:897. Also covers the Data Augmentation and Stan implementation. Resources at https://github.com/kaz-yos/em_da_repo
What is the Expectation Maximization (EM) Algorithm?
What is the Expectation Maximization (EM) Algorithm?
Kazuki Yoshida
Recommandé
introduce typical methods used for feature selection, including filter, wrapper, subset selection
Feature selection
Feature selection
Dong Guo
Methods wrapper evaluate subsets of variables which allows, unlike filter approaches, to detect the possible interactions between variables.
Wrapper feature selection method
Wrapper feature selection method
Amir Razmjou
K-Folds cross-validation is one method that attempts to maximize the use of the available data for training and then testing a model. It is particularly useful for assessing model performance, as it provides a range of accuracy scores across (somewhat) different data sets.
K-Folds Cross Validation Method
K-Folds Cross Validation Method
SHUBHAM GUPTA
Data Analytics & Machine Learning - Feature Selection with Trajan Simulator in Forward Selection Approach, Backward Selection Approach and Genetic Algorithms
Feature Selection in Machine Learning
Feature Selection in Machine Learning
Upekha Vandebona
Feature selection concepts and methods
Feature selection concepts and methods
Reza Ramezani
Machine learning algorithm ( KNN ) for classification and regression . Lazy learning , competitive and Instance based learning.
K - Nearest neighbor ( KNN )
K - Nearest neighbor ( KNN )
Mohammad Junaid Khan
A brief introduction to Logistic Regression Analysis, its assumptions, and its application.
Logistic Regression Analysis
Logistic Regression Analysis
COSTARCH Analytical Consulting (P) Ltd.
Review of Do and Batzoglou. "What is the expectation maximization algorith?" Nat. Biotechnol. 2008;26:897. Also covers the Data Augmentation and Stan implementation. Resources at https://github.com/kaz-yos/em_da_repo
What is the Expectation Maximization (EM) Algorithm?
What is the Expectation Maximization (EM) Algorithm?
Kazuki Yoshida
A presentation on KNN Algorithm.
KNN
KNN
West Virginia University
Linear Regression is one of the basic and fundamental algorithm which is used in machine learning
Machine Learning-Linear regression
Machine Learning-Linear regression
kishanthkumaar
An introduction to using linear discriminant analysis as a dimensionality reduction technique.
Introduction to Linear Discriminant Analysis
Introduction to Linear Discriminant Analysis
Jaclyn Kokx
This slide focuses on working procedure of some famous classification based machine learning algorithms
Classification Based Machine Learning Algorithms
Classification Based Machine Learning Algorithms
Md. Main Uddin Rony
Machine Learning seminar Convex Optimization Gradient Descent Method by Sanghyuk Chun
Gradient descent method
Gradient descent method
Sanghyuk Chun
This slide first introduces the sequential pattern mining problem and also presents some required definitions in order to understand GSP algorithm. At then end there is a brief introduction of GSP algorithm and some practical constraints which it supports.
Sequential Pattern Mining and GSP
Sequential Pattern Mining and GSP
Hamidreza Mahdavipanah
lazy learners
lazy learners and other classication methods
lazy learners and other classication methods
rajshreemuthiah
You will learn the basic concepts of machine learning classification and will be introduced to some different algorithms that can be used. This is from a very high level and will not be getting into the nitty-gritty details.
Introduction to Machine Learning Classifiers
Introduction to Machine Learning Classifiers
Functional Imperative
Machine learning is the subfield of computer science that gives computers the ability to learn without being programmed. Logistic Regression is a type of classification algorithm, based on linear regression to evaluate output and to minimize the error.
Machine Learning With Logistic Regression
Machine Learning With Logistic Regression
Knoldus Inc.
Data mining and warehousing (jntuh syllabus)
Association rule mining
Association rule mining
Acad
This Logistic Regression Presentation will help you understand how a Logistic Regression algorithm works in Machine Learning. In this tutorial video, you will learn what is Supervised Learning, what is Classification problem and some associated algorithms, what is Logistic Regression, how it works with simple examples, the maths behind Logistic Regression, how it is different from Linear Regression and Logistic Regression applications. At the end, you will also see an interesting demo in Python on how to predict the number present in an image using Logistic Regression. Below topics are covered in this Machine Learning Algorithms Presentation: 1. What is supervised learning? 2. What is classification? what are some of its solutions? 3. What is logistic regression? 4. Comparing linear and logistic regression 5. Logistic regression applications 6. Use case - Predicting the number in an image What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. - - - - - - - - About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. - - - - - - - Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. - - - - - - What skills will you learn from this Machine Learning course? By the end of this Machine Learning course, you will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems - - - - - - -
Logistic Regression | Logistic Regression In Python | Machine Learning Algori...
Logistic Regression | Logistic Regression In Python | Machine Learning Algori...
Simplilearn
In this talk, Dmitry shares his approach to feature engineering which he used successfully in various Kaggle competitions. He covers common techniques used to convert your features into numeric representation used by ML algorithms.
Feature Engineering
Feature Engineering
Sri Ambati
Slide explaining the distinction between bagging and boosting while understanding the bias variance trade-off. Followed by some lesser known scope of supervised learning. understanding the effect of tree split metric in deciding feature importance. Then understanding the effect of threshold on classification accuracy. Additionally, how to adjust model threshold for classification in supervised learning. Note: Limitation of Accuracy metric (baseline accuracy), alternative metrics, their use case and their advantage and limitations were briefly discussed.
Understanding Bagging and Boosting
Understanding Bagging and Boosting
Mohit Rajput
Logistic regression
Logistic regression
Venkata Reddy Konasani
Description of performance metrics used for various supervised and unsupervised machine learning algorithms.
Performance Metrics for Machine Learning Algorithms
Performance Metrics for Machine Learning Algorithms
Kush Kulshrestha
Data Mining and warehousing
Classification and prediction
Classification and prediction
Acad
machine learning workshop at NIT patna basics :day 2:(slide :2/5)
Logistic regression
Logistic regression
YashwantGahlot1
Intro to the very popular optimization Technique(Gradient descent) with linear regression . Linear regression with Gradient descent on www.landofai.com
Linear regression with gradient descent
Linear regression with gradient descent
Suraj Parmar
Machine learning (ML) technique use for Dimension reduction, feature extraction and analyzing huge amount of data are Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are easily and interactively explained with scatter plot graph , 2D and 3D projection of Principal components(PCs) for better understanding.
Principal Component Analysis (PCA) and LDA PPT Slides
Principal Component Analysis (PCA) and LDA PPT Slides
AbhishekKumar4995
Supervised learning techniques , Decision tree algorithms for Machine learning . Classification and Regression trees.
Decision trees in Machine Learning
Decision trees in Machine Learning
Mohammad Junaid Khan
This is the presentation for my library instruction session for Dr. Thomas Joyce's IMAG 4850 Senior Design class at the College of Engineering and Applied Sciences, at Western Michigan University.
IMAG 4850 Library Presentation
IMAG 4850 Library Presentation
edward.eckel
Presentation Of My Skills
Presentation Of My Skills
Mfoghama
Contenu connexe
Tendances
A presentation on KNN Algorithm.
KNN
KNN
West Virginia University
Linear Regression is one of the basic and fundamental algorithm which is used in machine learning
Machine Learning-Linear regression
Machine Learning-Linear regression
kishanthkumaar
An introduction to using linear discriminant analysis as a dimensionality reduction technique.
Introduction to Linear Discriminant Analysis
Introduction to Linear Discriminant Analysis
Jaclyn Kokx
This slide focuses on working procedure of some famous classification based machine learning algorithms
Classification Based Machine Learning Algorithms
Classification Based Machine Learning Algorithms
Md. Main Uddin Rony
Machine Learning seminar Convex Optimization Gradient Descent Method by Sanghyuk Chun
Gradient descent method
Gradient descent method
Sanghyuk Chun
This slide first introduces the sequential pattern mining problem and also presents some required definitions in order to understand GSP algorithm. At then end there is a brief introduction of GSP algorithm and some practical constraints which it supports.
Sequential Pattern Mining and GSP
Sequential Pattern Mining and GSP
Hamidreza Mahdavipanah
lazy learners
lazy learners and other classication methods
lazy learners and other classication methods
rajshreemuthiah
You will learn the basic concepts of machine learning classification and will be introduced to some different algorithms that can be used. This is from a very high level and will not be getting into the nitty-gritty details.
Introduction to Machine Learning Classifiers
Introduction to Machine Learning Classifiers
Functional Imperative
Machine learning is the subfield of computer science that gives computers the ability to learn without being programmed. Logistic Regression is a type of classification algorithm, based on linear regression to evaluate output and to minimize the error.
Machine Learning With Logistic Regression
Machine Learning With Logistic Regression
Knoldus Inc.
Data mining and warehousing (jntuh syllabus)
Association rule mining
Association rule mining
Acad
This Logistic Regression Presentation will help you understand how a Logistic Regression algorithm works in Machine Learning. In this tutorial video, you will learn what is Supervised Learning, what is Classification problem and some associated algorithms, what is Logistic Regression, how it works with simple examples, the maths behind Logistic Regression, how it is different from Linear Regression and Logistic Regression applications. At the end, you will also see an interesting demo in Python on how to predict the number present in an image using Logistic Regression. Below topics are covered in this Machine Learning Algorithms Presentation: 1. What is supervised learning? 2. What is classification? what are some of its solutions? 3. What is logistic regression? 4. Comparing linear and logistic regression 5. Logistic regression applications 6. Use case - Predicting the number in an image What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. - - - - - - - - About Simplilearn Machine Learning course: A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning. - - - - - - - Why learn Machine Learning? Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. - - - - - - What skills will you learn from this Machine Learning course? By the end of this Machine Learning course, you will be able to: 1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling. 2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project. 3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning. 4. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more. 5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems - - - - - - -
Logistic Regression | Logistic Regression In Python | Machine Learning Algori...
Logistic Regression | Logistic Regression In Python | Machine Learning Algori...
Simplilearn
In this talk, Dmitry shares his approach to feature engineering which he used successfully in various Kaggle competitions. He covers common techniques used to convert your features into numeric representation used by ML algorithms.
Feature Engineering
Feature Engineering
Sri Ambati
Slide explaining the distinction between bagging and boosting while understanding the bias variance trade-off. Followed by some lesser known scope of supervised learning. understanding the effect of tree split metric in deciding feature importance. Then understanding the effect of threshold on classification accuracy. Additionally, how to adjust model threshold for classification in supervised learning. Note: Limitation of Accuracy metric (baseline accuracy), alternative metrics, their use case and their advantage and limitations were briefly discussed.
Understanding Bagging and Boosting
Understanding Bagging and Boosting
Mohit Rajput
Logistic regression
Logistic regression
Venkata Reddy Konasani
Description of performance metrics used for various supervised and unsupervised machine learning algorithms.
Performance Metrics for Machine Learning Algorithms
Performance Metrics for Machine Learning Algorithms
Kush Kulshrestha
Data Mining and warehousing
Classification and prediction
Classification and prediction
Acad
machine learning workshop at NIT patna basics :day 2:(slide :2/5)
Logistic regression
Logistic regression
YashwantGahlot1
Intro to the very popular optimization Technique(Gradient descent) with linear regression . Linear regression with Gradient descent on www.landofai.com
Linear regression with gradient descent
Linear regression with gradient descent
Suraj Parmar
Machine learning (ML) technique use for Dimension reduction, feature extraction and analyzing huge amount of data are Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are easily and interactively explained with scatter plot graph , 2D and 3D projection of Principal components(PCs) for better understanding.
Principal Component Analysis (PCA) and LDA PPT Slides
Principal Component Analysis (PCA) and LDA PPT Slides
AbhishekKumar4995
Supervised learning techniques , Decision tree algorithms for Machine learning . Classification and Regression trees.
Decision trees in Machine Learning
Decision trees in Machine Learning
Mohammad Junaid Khan
Tendances
(20)
KNN
KNN
Machine Learning-Linear regression
Machine Learning-Linear regression
Introduction to Linear Discriminant Analysis
Introduction to Linear Discriminant Analysis
Classification Based Machine Learning Algorithms
Classification Based Machine Learning Algorithms
Gradient descent method
Gradient descent method
Sequential Pattern Mining and GSP
Sequential Pattern Mining and GSP
lazy learners and other classication methods
lazy learners and other classication methods
Introduction to Machine Learning Classifiers
Introduction to Machine Learning Classifiers
Machine Learning With Logistic Regression
Machine Learning With Logistic Regression
Association rule mining
Association rule mining
Logistic Regression | Logistic Regression In Python | Machine Learning Algori...
Logistic Regression | Logistic Regression In Python | Machine Learning Algori...
Feature Engineering
Feature Engineering
Understanding Bagging and Boosting
Understanding Bagging and Boosting
Logistic regression
Logistic regression
Performance Metrics for Machine Learning Algorithms
Performance Metrics for Machine Learning Algorithms
Classification and prediction
Classification and prediction
Logistic regression
Logistic regression
Linear regression with gradient descent
Linear regression with gradient descent
Principal Component Analysis (PCA) and LDA PPT Slides
Principal Component Analysis (PCA) and LDA PPT Slides
Decision trees in Machine Learning
Decision trees in Machine Learning
En vedette
This is the presentation for my library instruction session for Dr. Thomas Joyce's IMAG 4850 Senior Design class at the College of Engineering and Applied Sciences, at Western Michigan University.
IMAG 4850 Library Presentation
IMAG 4850 Library Presentation
edward.eckel
Presentation Of My Skills
Presentation Of My Skills
Mfoghama
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chenhm
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AfHEA 2011 a framework to study the process of removing user fee in LIC
AfHEA 2011 a framework to study the process of removing user fee in LIC
David Hercot
My presentation at the 2012 Michigan Academy of Science, Arts, and Letters at Alma College, March 2, 2012.
Source Text Re-Use in Engineering Master's Theses and Doctoral Dissertations ...
Source Text Re-Use in Engineering Master's Theses and Doctoral Dissertations ...
edward.eckel
Short presentation of a review of the policy process of the removal of user fees in the health sector in six Sub Saharan Africa countries.
Removing User Fees In SSA D Hercot
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David Hercot
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Two apps that I built.
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Presentation by David Hercot on how to do a policy delphi for retrospective policy analysis, presented at the Beijing Emerging Voices preconference 2012.
Hercot How to do a policy delphi
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Sinagoga Neologa din Cluj-Napoca prezentare power point ppt bako gabor abrudean david
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IMAG 4850 Library Presentation
IMAG 4850 Library Presentation
Presentation Of My Skills
Presentation Of My Skills
Bilva
Bilva
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Emerging Voices for Global Health
Single Electron Spin Detection Slides For Uno Interview
Single Electron Spin Detection Slides For Uno Interview
AfHEA 2011 a framework to study the process of removing user fee in LIC
AfHEA 2011 a framework to study the process of removing user fee in LIC
Source Text Re-Use in Engineering Master's Theses and Doctoral Dissertations ...
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S I D A
S I D A
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Sports Programs
Hercot How to do a policy delphi
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Sinagoga Neologa din Cluj-Napoca
Sinagoga Neologa din Cluj-Napoca
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The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.
Artificial Intelligence: Facts and Myths
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As privacy and data protection regulations evolve rapidly, organizations operating in multiple jurisdictions face mounting challenges to ensure compliance and safeguard customer data. With state-specific privacy laws coming up in multiple states this year, it is essential to understand what their unique data protection regulations will require clearly. How will data privacy evolve in the US in 2024? How to stay compliant? Our panellists will guide you through the intricacies of these states' specific data privacy laws, clarifying complex legal frameworks and compliance requirements. This webinar will review: - The essential aspects of each state's privacy landscape and the latest updates - Common compliance challenges faced by organizations operating in multiple states and best practices to achieve regulatory adherence - Valuable insights into potential changes to existing regulations and prepare your organization for the evolving landscape
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A Domino Admins Adventures (Engage 2024)
Gabriella Davis
Slides from the presentation on Machine Learning for the Arts & Humanities seminar at the University of Bologna (Digital Humanities and Digital Knowledge program)
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed texts
Maria Levchenko
What is a good lead in your organisation? Which leads are priority? What happens to leads? When sales and marketing give different answers to these questions, or perhaps aren't sure of the answers at all, frustrations build and opportunities are left on the table. Join us for an illuminating session with Cian McLoughlin, HubSpot Principal Customer Success Manager, as we look at that crucial piece of the customer journey in which leads are transferred from marketing to sales.
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
HampshireHUG
Read about the journey the Adobe Experience Manager team has gone through in order to become and scale API-first throughout the organisation.
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
Radu Cotescu
Cisco CCNA
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Service
giselly40
Enterprise Knowledge’s Urmi Majumder, Principal Data Architecture Consultant, and Fernando Aguilar Islas, Senior Data Science Consultant, presented "Driving Behavioral Change for Information Management through Data-Driven Green Strategy" on March 27, 2024 at Enterprise Data World (EDW) in Orlando, Florida. In this presentation, Urmi and Fernando discussed a case study describing how the information management division in a large supply chain organization drove user behavior change through awareness of the carbon footprint of their duplicated and near-duplicated content, identified via advanced data analytics. Check out their presentation to gain valuable perspectives on utilizing data-driven strategies to influence positive behavioral shifts and support sustainability initiatives within your organization. In this session, participants gained answers to the following questions: - What is a Green Information Management (IM) Strategy, and why should you have one? - How can Artificial Intelligence (AI) and Machine Learning (ML) support your Green IM Strategy through content deduplication? - How can an organization use insights into their data to influence employee behavior for IM? - How can you reap additional benefits from content reduction that go beyond Green IM?
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...
Enterprise Knowledge
Explore the leading Large Language Models (LLMs) and their capabilities with a comprehensive evaluation. Dive into their performance, architecture, and applications to gain insights into the state-of-the-art in natural language processing. Discover which LLM best suits your needs and stay ahead in the world of AI-driven language understanding.
Evaluating the top large language models.pdf
Evaluating the top large language models.pdf
ChristopherTHyatt
45-60 minute session deck from introducing Google Apps Script to developers, IT leadership, and other technical professionals.
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
wesley chun
How to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
naman860154
Breathing New Life into MySQL Apps With Advanced Postgres Capabilities
🐬 The future of MySQL is Postgres 🐘
🐬 The future of MySQL is Postgres 🐘
RTylerCroy
Discord is a free app offering voice, video, and text chat functionalities, primarily catering to the gaming community. It serves as a hub for users to create and join servers tailored to their interests. Discord’s ecosystem comprises servers, each functioning as a distinct online community with its own channels dedicated to specific topics or activities. Users can engage in text-based discussions, voice calls, or video chats within these channels. Understanding Discord Servers Discord servers are virtual spaces where users congregate to interact, share content, and build communities. Servers may revolve around gaming, hobbies, interests, or fandoms, providing a platform for like-minded individuals to connect. Communication Features Discord offers a range of communication tools, including text channels for messaging, voice channels for real-time audio conversations, and video channels for face-to-face interactions. These features facilitate seamless communication and collaboration. What Does NSFW Mean? The acronym NSFW stands for “Not Safe For Work,” indicating content that may be inappropriate for professional or public settings. NSFW Content NSFW content encompasses material that is sexually explicit, violent, or otherwise graphic in nature. It often includes nudity, profanity, or depictions of sensitive topics.
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
UK Journal
Dernier
(20)
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
ProductAnonymous-April2024-WinProductDiscovery-MelissaKlemke
Tech Trends Report 2024 Future Today Institute.pdf
Tech Trends Report 2024 Future Today Institute.pdf
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreter
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
08448380779 Call Girls In Diplomatic Enclave Women Seeking Men
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and Myths
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
TrustArc Webinar - Stay Ahead of US State Data Privacy Law Developments
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed texts
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
Scaling API-first – The story of a global engineering organization
Scaling API-first – The story of a global engineering organization
CNv6 Instructor Chapter 6 Quality of Service
CNv6 Instructor Chapter 6 Quality of Service
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...
Evaluating the top large language models.pdf
Evaluating the top large language models.pdf
Automating Google Workspace (GWS) & more with Apps Script
Automating Google Workspace (GWS) & more with Apps Script
How to convert PDF to text with Nanonets
How to convert PDF to text with Nanonets
🐬 The future of MySQL is Postgres 🐘
🐬 The future of MySQL is Postgres 🐘
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Intro to Model Selection
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Model Selection Huimin Chen Department of Electrical Engineering University of New Orleans New Orleans, LA 70148
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