Introduction to Latent Dirichlet Allocation (LDA). We cover the basic ideas necessary to understand LDA then construct the model from its generative process. Intuitions are emphasized but little guidance is given for fitting the model which is not very insightful.
Automatic text summarization is the process of reducing the text content and retaining the
important points of the document. Generally, there are two approaches for automatic text summarization:
Extractive and Abstractive. The process of extractive based text summarization can be divided into two
phases: pre-processing and processing. In this paper, we discuss some of the extractive based text
summarization approaches used by researchers. We also provide the features for extractive based text
summarization process. We also present the available linguistic preprocessing tools with their features,
which are used for automatic text summarization. The tools and parameters useful for evaluating the
generated summary are also discussed in this paper. Moreover, we explain our proposed lexical chain
analysis approach, with sample generated lexical chains, for extractive based automatic text summarization.
We also provide the evaluation results of our system generated summary. The proposed lexical chain
analysis approach can be used to solve different text mining problems like topic classification, sentiment
analysis, and summarization.
The Text Classification slides contains the research results about the possible natural language processing algorithms. Specifically, it contains the brief overview of the natural language processing steps, the common algorithms used to transform words into meaningful vectors/data, and the algorithms used to learn and classify the data.
To learn more about RAX Automation Suite, visit: www.raxsuite.com
Explore detailed Topic Modeling via LDA Laten Dirichlet Allocation and their steps.
Thanks, for your time, if you enjoyed this short video there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Naive Bayes is a kind of classifier which uses the Bayes Theorem. It predicts membership probabilities for each class such as the probability that given record or data point belongs to a particular class.
Abstractive text summarization is nowadays one of the most important research topics in NLP. However, getting a deep understanding of what it is and also how it works requires a series of base pieces of knowledge that build on top of each other. This is the reason why this presentation will give audiences an overview of sequence-to-sequence with the acceleration of various versions of attention over the past few years. In addition, natural language generation (NLG) with the focusing on decoder techniques and its relevant problems will be reviewed, as a supportive factor to the light of the success of automatic summarization. Finally, the abstractive text summarization will be represented with potential approaches to tackle some hot issues in some latest research papers.
A Simple Introduction to Word EmbeddingsBhaskar Mitra
In information retrieval there is a long history of learning vector representations for words. In recent times, neural word embeddings have gained significant popularity for many natural language processing tasks, such as word analogy and machine translation. The goal of this talk is to introduce basic intuitions behind these simple but elegant models of text representation. We will start our discussion with classic vector space models and then make our way to recently proposed neural word embeddings. We will see how these models can be useful for analogical reasoning as well applied to many information retrieval tasks.
What is the Expectation Maximization (EM) Algorithm?Kazuki Yoshida
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
Exploratory data analysis data visualization:
Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to
Maximize insight into a data set.
Uncover underlying structure.
Extract important variables.
Detect outliers and anomalies.
Test underlying assumptions.
Develop parsimonious models.
Determine optimal factor settings
Crowdsourcing Series: LinkedIn. By Vitaly Gordon & Patrick Philips. Hakka Labs
Better data beats better algorithms, but better data can be hard to come by. In this talk, Vitaly Gordon, Senior Data Scientist at LinkedIn, and Patrick Philips, Crowdsourcing Expert at LinkedIn, will show how the LinkedIn data science team hacks data science using sophisticated data mining and crowdsourcing techniques to leverage the data they already have and create the data that's missing.
Automatic text summarization is the process of reducing the text content and retaining the
important points of the document. Generally, there are two approaches for automatic text summarization:
Extractive and Abstractive. The process of extractive based text summarization can be divided into two
phases: pre-processing and processing. In this paper, we discuss some of the extractive based text
summarization approaches used by researchers. We also provide the features for extractive based text
summarization process. We also present the available linguistic preprocessing tools with their features,
which are used for automatic text summarization. The tools and parameters useful for evaluating the
generated summary are also discussed in this paper. Moreover, we explain our proposed lexical chain
analysis approach, with sample generated lexical chains, for extractive based automatic text summarization.
We also provide the evaluation results of our system generated summary. The proposed lexical chain
analysis approach can be used to solve different text mining problems like topic classification, sentiment
analysis, and summarization.
The Text Classification slides contains the research results about the possible natural language processing algorithms. Specifically, it contains the brief overview of the natural language processing steps, the common algorithms used to transform words into meaningful vectors/data, and the algorithms used to learn and classify the data.
To learn more about RAX Automation Suite, visit: www.raxsuite.com
Explore detailed Topic Modeling via LDA Laten Dirichlet Allocation and their steps.
Thanks, for your time, if you enjoyed this short video there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
Naive Bayes is a kind of classifier which uses the Bayes Theorem. It predicts membership probabilities for each class such as the probability that given record or data point belongs to a particular class.
Abstractive text summarization is nowadays one of the most important research topics in NLP. However, getting a deep understanding of what it is and also how it works requires a series of base pieces of knowledge that build on top of each other. This is the reason why this presentation will give audiences an overview of sequence-to-sequence with the acceleration of various versions of attention over the past few years. In addition, natural language generation (NLG) with the focusing on decoder techniques and its relevant problems will be reviewed, as a supportive factor to the light of the success of automatic summarization. Finally, the abstractive text summarization will be represented with potential approaches to tackle some hot issues in some latest research papers.
A Simple Introduction to Word EmbeddingsBhaskar Mitra
In information retrieval there is a long history of learning vector representations for words. In recent times, neural word embeddings have gained significant popularity for many natural language processing tasks, such as word analogy and machine translation. The goal of this talk is to introduce basic intuitions behind these simple but elegant models of text representation. We will start our discussion with classic vector space models and then make our way to recently proposed neural word embeddings. We will see how these models can be useful for analogical reasoning as well applied to many information retrieval tasks.
What is the Expectation Maximization (EM) Algorithm?Kazuki Yoshida
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
Exploratory data analysis data visualization:
Exploratory Data Analysis (EDA) is an approach/philosophy for data analysis that employs a variety of techniques (mostly graphical) to
Maximize insight into a data set.
Uncover underlying structure.
Extract important variables.
Detect outliers and anomalies.
Test underlying assumptions.
Develop parsimonious models.
Determine optimal factor settings
Crowdsourcing Series: LinkedIn. By Vitaly Gordon & Patrick Philips. Hakka Labs
Better data beats better algorithms, but better data can be hard to come by. In this talk, Vitaly Gordon, Senior Data Scientist at LinkedIn, and Patrick Philips, Crowdsourcing Expert at LinkedIn, will show how the LinkedIn data science team hacks data science using sophisticated data mining and crowdsourcing techniques to leverage the data they already have and create the data that's missing.
Examples, techniques, and lessons learned building data products over the last 4 years at LinkedIn.
Pete Skomoroch is a Principal Data Scientist at LinkedIn where he leads a team focused on building data products leveraging LinkedIn's powerful identity and reputation data.
The talk describes some techniques and best practices applied to develop products like LinkedIn Skills & Endorsements.
This talk was presented at the SF Data Science Meetup on September 19th, 2013
Examples, techniques, and lessons learned building data products over the last 3 years at LinkedIn.
Pete Skomoroch is a Principal Data Scientist at LinkedIn where he leads a team focused on building data products leveraging LinkedIn's powerful identity and reputation data.
The talk describes some techniques and best practices applied to develop products like LinkedIn Skills & Endorsements.
This was the inaugural UberData Tech Talk, held in SF at Uber HQ.
These are slides for a guest talk I gave for course 15.S14: Global Business of Artificial Intelligence and Robotics (GBAIR) taught in Spring 2017. Here is the YouTube video (filmed in 360/VR): https://youtu.be/s3MuSOl1Rog
Relationships Matter: Using Connected Data for Better Machine LearningNeo4j
Relationships are highly predictive of behavior, yet most data science models overlook this information because it's difficult to extract network structure for use in machine learning (ML).
With graphs, relationships are embedded in the data itself, making it practical to add these predictive capabilities to your existing practices.
That’s why we’re presenting and demoing the use of graph-native ML to make breakthrough predictions. This will cover:
- Different approaches to graph feature engineering, from queries and algorithms to embeddings
- How ML techniques leverage everything from classical network science to deep learning and graph convolutional neural networks
- How to generate representations of your graph using graph embeddings, create ML models for link prediction or node classification, and apply these models to add missing information to an existing graph/incoming data
- Why no-code visualization and prototyping is important
Presentation on an overview of LinkedIn data driven products and infrastructure given on 26 Oct 2012 in the big-data symposium given in honor of the retirement of my PhD advisor Dr Martin H. Schultz.
Open Source Data Visualization for Resource Sharing: An Ivy Plus Libraries Pr...Heidi Nance
https://sched.co/GB4S
Presentation by Heidi Nance and Joe Zucca.
In order to better understand scholarly use of a vast collective collection - both within and without our 13-library partnership - Ivy Plus Libraries is leveraging MetriDoc, an open-source framework devised by a library for libraries, to create a generalizable data analysis infrastructure and visualization service. MetriDoc gathers, normalizes, and presents BorrowDirect consortial Resource Sharing data as well as ILLiad (interlibrary loan + document delivery) data from all 13 Ivy Plus Libraries—more than 500,000 transactions, annually. It integrates seamlessly with Tableau or other commodity statistical applications, thus allowing staff in any functional area (Assessment, User Services, Collections, IT, Technical Services, User Experience, Research & Instruction, etc.) to query, download, and interpret resource sharing data to support a variety of one-time or ongoing assessment projects.
In this session we will discuss the Ivy Plus project and goals, the framework’s IMLS-funded history, and basic architecture, myriad use cases, and creative opportunities for future extensibility and connections with third-party systems common to libraries. Come learn how you, too, can analyze the larger-than-you-might-expect Resource Sharing data universe.
Keynote at CIKM 2013 Workshop on Data-driven User Behavioral Modelling and Mining from Social Media
Social Search in a Professional Context
Daniel Tunkelang (LinkedIn)
Social networks bring a new dimension to search. Instead of looking for web pages or text documents, LinkedIn members search a world of entities connected by a rich graph of relationships. Search is a fundamental part of the LinkedIn ecosystem, as it helps our members find and be found. Unlike most search applications, LinkedIn's search experience is highly personalized: two LinkedIn members performing the same search query are likely to see completely different results. Delivering the right results to the right person depends on our ability to leverage our each member's unique professional identity and network. In this talk, I'll describe the kinds of search behavior we see on LinkedIn, and some of the approaches we've taken to help our members address their information needs.
Knowledge Graphs and Generative AI
Dr. Katie Roberts, Data Science Solutions Architect, Neo4j
It’s no secret that Large Language Models (LLMs) are popular right now, especially in the age of Generative AI. LLMs are powerful models that enable access to data and insights for any user, regardless of their technical background, however, they are not without challenges. Hallucinations, generic responses, bias, and a lack of traceability can give organizations pause when thinking about how to take advantage of this technology. Graphs are well suited to ground LLMs as they allow you to take advantage of relationships within your data that are often overlooked with traditional data storage and data science approaches. Combining Knowledge Graphs and LLMs enables contextual and semantic information retrieval from both structured and unstructured data sources. In this session, you’ll learn how graphs and graph data science can be incorporated into your analytics practice, and how a connected data platform can improve explainability, accuracy, and specificity of applications backed by foundation models.
Applied Data Science Course Part 1: Concepts & your first ML modelDataiku
In this first course of our Applied Data Science online course series, you'll learn about the mindset shift of going from small to big data, basic definitions and concepts, and an overview of the data science workflow.
Explaining the Basics of Mean Field Variational Approximation for StatisticiansWayne Lee
Explaining "Explaining Variational Approximation" by JT Ormerod and MP Wand (2010).
I wanted to learn variational methods since its speed for Bayesian inference is just so fast! Here's my condensed version of the paper without the cool examples...you should really try the examples out if you want a better understanding of this method!
This presentation assumes some knowledge or experience with Bayesian methods.
What is bayesian statistics and how is it different?Wayne Lee
Gentle intro to Bayesian Statistics and how it's different from classical frequentist statistics. Assumes you have basic statistical knowledge.
Why "Am I pregnant?" is a question more suitable for Bayesian techniques and not actually suitable at all for Frequentist techniques!
Overall, if you ask enough questions about the data, measure enough metrics, and/or fit enough models, you'll likely find one that moves in your favor. Data snooping is heavily tied to the problem of multiple testing which is elegantly demonstrated through this xkcd cartoon.
There is unfortunately no golden rule to prevent data snooping given the pressure to deploy new features, discover new results, and publish interesting findings. Asking product managers/scientists to formulate hypotheses before performing the analysis can be quite difficult. This is where a data scientist should step in and help iterate between the original hypotheses and data.
How would you deal with data snooping?
This is a crash course in A/B testing from the statistical view. Focus is placed on the overall idea and framework assuming very little experience/knowledge in statistics.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Take home: validation is difficult….no true answer here.
Clustering documents is difficult because many repeated words are used. Some documents may be similar to one another on different topics. So we might want to cluster allowing membership.
2 stage process
Example: the word usage of “professional” is probably higher in the topic of professional network than a social network.
2 stage process
2 stage process
2 stage process
2 stage process
2 stage process
Example: the word usage of “professional” is probably higher in the topic of professional network than a social network.
Example: the word usage of “professional” is probably higher in the topic of professional network than a social network.
Example: the word usage of “professional” is probably higher in the topic of professional network than a social network.
Example: the word usage of “professional” is probably higher in the topic of professional network than a social network.
Example: the word usage of “professional” is probably higher in the topic of professional network than a social network.
Example: the word usage of “professional” is probably higher in the topic of professional network than a social network.
Example: the word usage of “professional” is probably higher in the topic of professional network than a social network.
Example: the word usage of “professional” is probably higher in the topic of professional network than a social network.
Example: the word usage of “professional” is probably higher in the topic of professional network than a social network.