This is a presentation made for our Intro to Machine Learning class. As a result it focuses more on the use of logit regression as a classifier as opposed to statistical applications. Many of the slides are based on Stanford's Open Course in machine learning.
* Introduction
* Logistic Regression
* Log-Linear Model
* Linear-Chain CRF
* Example: Part of Speech (POS) Tagging
* CRF Training and Testing
* Example: Part of Speech (POS) Tagging
* Example: Speech Disfluency Detection
ESL 4.4.3-4.5: Logistic Reression (contd.) and Separating HyperplaneShinichi Tamura
The presentation material for the reading club of Element of Statistical Learning by Hastie et al.
The contents of the sections cover
- Properties of logistic regression compared to least square s fitting
- Difference between logistic regression vs. linear discriminant analysis
- Rosenblatt's perceptron algorithm
- Derivation of optimal hyperplane, which offers the basis for SVM
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研究室での『統計学習の基礎』(Hastieら著)の輪講用発表資料(ぜんぶ英語)です。
担当範囲は
・最小二乗法との類推で見るロジスティック回帰の特徴
・ロジスティック回帰と線形判別分析の比較
・ローゼンブラットのパーセプトロンアルゴリズム
・SVMの基礎となる最適分離超平面の導出
An introduction to logistic regression for physicians, public health students and other health workers. Logistic regression is a way to look at effect of a numeric independent variable on a binary (yes-no) dependent variable. For example, you can analyze or model the effect of birth weight on survival.
Binary outcome models are widely used in many real world application. We can used Probit and Logit models to analysis this type of data. Specially, dose response data can be analyze using these two models.
This is a presentation made for our Intro to Machine Learning class. As a result it focuses more on the use of logit regression as a classifier as opposed to statistical applications. Many of the slides are based on Stanford's Open Course in machine learning.
* Introduction
* Logistic Regression
* Log-Linear Model
* Linear-Chain CRF
* Example: Part of Speech (POS) Tagging
* CRF Training and Testing
* Example: Part of Speech (POS) Tagging
* Example: Speech Disfluency Detection
ESL 4.4.3-4.5: Logistic Reression (contd.) and Separating HyperplaneShinichi Tamura
The presentation material for the reading club of Element of Statistical Learning by Hastie et al.
The contents of the sections cover
- Properties of logistic regression compared to least square s fitting
- Difference between logistic regression vs. linear discriminant analysis
- Rosenblatt's perceptron algorithm
- Derivation of optimal hyperplane, which offers the basis for SVM
-------------------------------------------------------------------------
研究室での『統計学習の基礎』(Hastieら著)の輪講用発表資料(ぜんぶ英語)です。
担当範囲は
・最小二乗法との類推で見るロジスティック回帰の特徴
・ロジスティック回帰と線形判別分析の比較
・ローゼンブラットのパーセプトロンアルゴリズム
・SVMの基礎となる最適分離超平面の導出
An introduction to logistic regression for physicians, public health students and other health workers. Logistic regression is a way to look at effect of a numeric independent variable on a binary (yes-no) dependent variable. For example, you can analyze or model the effect of birth weight on survival.
Binary outcome models are widely used in many real world application. We can used Probit and Logit models to analysis this type of data. Specially, dose response data can be analyze using these two models.
The characteristics of the Ideal Source for practicing Evidence-Based Medicine are:-
Located in the clinical setting
Easy to use
Fast, reliable connection
Comprehensive /Full Text
Provides primary data
Open Science is a movement to make scientific research, its data and dissemination accessible to all levels of society. This movement considers aspects such as Open Access, Open Data, Reproducible Research and Open Software.
Each of these aspects presents discreteness that need to be evaluated and discussed by the scientific community so that guidelines are established that facilitate the dissemination of scientific information.
The great challenge is to establish effective and efficient practices that allow journals to add these demands in their editorial processes, so as not only to allow data, software and methods to be accessible, but also to encourage the community to do so.
Considering these questions, this panel has as a proposal to discuss important aspects about the advancement of research communication. Some of these aspects are placed in the SciELO indexing criteria, as is the case of referencing research materials in favor of transparency and reproducibility.
Syllabus
FAIR criteria, concepts and implementation; challenges for the publication of data and methods; institutional policies for open data; adoption of TOP guidelines (Transparency and Openness Promotion); software repositories; thematic areas data repositories.
Presentation on various developments in rapid reviews, inlcuding Cochrane Response and TRIP Rapid Reviews.
Presented at WEB&Z meeting (Dutch medical information specilists) November 28, 2013
Introduction to the Cochrane Injuries Group based at the London School of Hygiene and Tropical Medicine. The introduction was originally written by Katherine Ker, Carolyn DiGuiseppi and Rebecca Ivers.
This Assessment constitutes a Performance Task in which you GrazynaBroyles24
This Assessment constitutes a Performance Task in which you are asked to evaluate various sources of information related to a topic in the early childhood field. You will choose a topic from the options below, evaluate the credibility of both scholarly and Internet sources, analyze ethical guidelines, and synthesize your findings.
Access the following to complete this Assessment:
· University of British Columbia. (n.d.). Evaluating information sources. Retrieved from http://help.library.ubc.ca/evaluating-and-citing-sources/evaluating-information-sources/
· University of California Berkeley Library. (2012). Evaluating web pages: Techniques to apply and questions to ask. Retrieved from http://www.lib.berkeley.edu/TeachingLib/Guides/Internet/Evaluate.html
· Kent State University. (2021). Criteria for evaluating web resources. Retrieved from https://www.library.kent.edu/criteria-evaluating-web-resources
· Society for Research in Child Development (SRCD). (2007). Ethical standards in research. Retrieved from http://www.srcd.org/about-us/ethical-standards-research
· Performance Task Submission Template
You will evaluate research related to your chosen topic and write a 5- to 7-page response using the template provided.
Topic Options: choose only one from below.
· The use of technology in early childhood programs
· Dual language instruction in early childhood programs
· Inclusion within the early childhood environment
· Childhood obesity prevention strategies
Part I: Evaluating Credible Scholarly Sources
Select three peer-reviewed journal articles that relate to your chosen topic, at least one of which contains a research study. For each article you select, enter the following information in the template:
· APA citation
· Summary of the article including a description of the research topic and an overview of the research findings (1 paragraph)
· Evaluation regarding the validity and credibility of the source, explaining how you evaluated the source to determine whether it is valid and credible (1 paragraph)
APA citation for Article 1:
Enter Your Response Here
Summary of Article 1:
Enter Your Response Here
Evaluation of the validity and credibility of Article 1:
Enter Your Response Here
APA citation for Article 2:
Enter Your Response Here
Summary of Article 2:
Enter Your Response Here
Evaluation of the validity and credibility of Article 2:
Enter Your Response Here Enter Your Response Here
APA citation for Article 3:
Enter Your Response Here
Summary of Article 3:
Enter Your Response Here
Evaluation of the validity and credibility of Article 3:
Enter Your Response Here
Part II: Evaluating Credible Internet Sources
Select two Internet sources that relate to your chosen topic. For each Internet source you select, enter the following information in the template:
· APA citation
· Summary of each internet source, a description of the topic and an overview of the information presented (1 paragraph)
· Evaluation regarding the validity ...
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...2023240532
Quantitative data Analysis
Overview
Reliability Analysis (Cronbach Alpha)
Common Method Bias (Harman Single Factor Test)
Frequency Analysis (Demographic)
Descriptive Analysis
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
6. Transparent Repor(ng
& Systema(c Reviews
• “Systema)c reviews seek to collate all evidence that fits pre-specified
eligibility criteria in order to address a specific research ques)on
• Systema)c reviews aim to minimize bias by using explicit, systema)c
methods
• The Cochrane Collabora)on prepares, maintains and promotes systema)c
reviews to inform healthcare decisions: Cochrane Reviews”
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hkp://
community.cochrane.or
g/about-us/evidence-
based-health-care
8. CONSORT Checklist
Introduc(on
TITLE & ABSTRACT
• 1a. Iden)fica)on as a randomised trial in the )tle
• 1b. Structured summary of trial design, methods, results, and conclusions (for
specific guidance see CONSORT for abstracts)
BACKGROUND & OBJECTIVES
• 2a. Scien)fic background and explana)on of ra)onale
• 2b. Specific objec)ves or hypotheses
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11. CONSORT Checklist:
Methods
RANDOMIZATION: SEQUENCE GENERATION
• 8a. Method used to generate the random alloca)on sequence
• 8b. Type of randomisa)on; details of any restric)on (such as blocking and
block size)
RANDOMIZATION: ALLOCATION CONCEALMENT
• 9. Mechanism used to implement the random alloca)on sequence (such as
sequen)ally numbered containers), describing any steps taken to conceal the
sequence un)l interven)ons were assigned
RANDOMIZATION: IMPLEMENTATION
• 10. Who generated the random alloca)on sequence, who enrolled
par)cipants, and who assigned par)cipants to interven)ons
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13. CONSORT Checklist:
Results
PARTICIPANT FLOW
(DIAGRAM
RECOMMENDED)
• 13a. For each group, the
numbers of par)cipants
who were randomly
assigned, received
intended treatment, and
were analysed for the
primary outcome
• 13b. For each group,
losses and exclusions
ajer randomisa)on,
together with reasons
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CONSORT 2010 Flow Diagram
Assessed for eligibility (n= )
Excluded (n= )
¨ Not meeting inclusion criteria (n= )
¨ Declined to participate (n= )
¨ Other reasons (n= )
Analysed (n= )
¨ Excluded from analysis (give reasons) (n= )
Lost to follow-up (give reasons) (n= )
Discontinued intervention (give reasons) (n= )
Allocated to intervention (n= )
¨ Received allocated intervention (n= )
¨ Did not receive allocated intervention (give
reasons) (n= )
Lost to follow-up (give reasons) (n= )
Discontinued intervention (give reasons) (n= )
Allocated to intervention (n= )
¨ Received allocated intervention (n= )
¨ Did not receive allocated intervention (give
reasons) (n= )
Analysed (n= )
¨ Excluded from analysis (give reasons) (n= )
Allocation
Analysis
Follow-Up
Randomized (n= )
Enrollment
14. CONSORT Checklist:
Results
RECRUITMENT
• 14a. Dates defining the periods of recruitment and follow-up
• 14b. Why the trial ended or was stopped
BASELINE DATA
• 15. A table showing baseline demographic and clinical characteris)cs for each
group
NUMBER ANALYZED
• 16. For each group, number of par)cipants (denominator) included in each
analysis and whether the analysis was by original assigned groups
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17. CONSORT Checklist:
Other Informa(on
REGISTRATION
• 23. Registra)on number and name of trial registry
PROTOCOL
• 24. Where the full trial protocol can be accessed, if available
FUNDING
• 25. Sources of funding and other support (such as supply of drugs), role of
funders
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