Invited Talk @ Google AI, New York City USA.
Talk includes: Neural Relation Extraction (AAAI-2019 paper) and Neural Topic Modeling (AAAI-2019 and ICLR-2019 papers).
textTOvec: DEEP CONTEXTUALIZED NEURAL AUTOREGRESSIVE TOPIC MODELS OF LANGUAGE...Pankaj Gupta, PhD
Unified neural model of topic and language modeling to introduce language structure in topic models for contextualized topic vectors
Representation learning for short text and long text documents
Generate contextualized topic vectors of variable document sizes, even in limited context settings.
Neural topic models with word embeddings
Neural Relation ExtractionWithin and Across Sentence BoundariesPankaj Gupta, PhD
AAAI-19 paper: Neural Relation ExtractionWithin and Across Sentence Boundaries
Authors: Pankaj Gupta, Subburam Rajaram, Hinrich Schuetze and Thomas Runkler
Lecture 07: Representation and Distributional Learning by Pankaj GuptaPankaj Gupta, PhD
Lecture on "Representation and Distributional Learning" at University of Munich (LMU), as part of "Deep Learning & AI" lecture series.
Includes: Fundamentals of representation learning, probabilistic graphical models, generative modeling, unsupervised learning, RBMs, RSM, DocNADE, etc.
Lecture 05: Recurrent Neural Networks / Deep Learning by Pankaj GuptaPankaj Gupta, PhD
Lecture on Recurrent Neural Network at University of Munich (LMU), as part of Deep Learning & AI lecture series.
Includes: Fundamentals of RNNs, Need for LSTM and GRU.
textTOvec: DEEP CONTEXTUALIZED NEURAL AUTOREGRESSIVE TOPIC MODELS OF LANGUAGE...Pankaj Gupta, PhD
Unified neural model of topic and language modeling to introduce language structure in topic models for contextualized topic vectors
Representation learning for short text and long text documents
Generate contextualized topic vectors of variable document sizes, even in limited context settings.
Neural topic models with word embeddings
Neural Relation ExtractionWithin and Across Sentence BoundariesPankaj Gupta, PhD
AAAI-19 paper: Neural Relation ExtractionWithin and Across Sentence Boundaries
Authors: Pankaj Gupta, Subburam Rajaram, Hinrich Schuetze and Thomas Runkler
Lecture 07: Representation and Distributional Learning by Pankaj GuptaPankaj Gupta, PhD
Lecture on "Representation and Distributional Learning" at University of Munich (LMU), as part of "Deep Learning & AI" lecture series.
Includes: Fundamentals of representation learning, probabilistic graphical models, generative modeling, unsupervised learning, RBMs, RSM, DocNADE, etc.
Lecture 05: Recurrent Neural Networks / Deep Learning by Pankaj GuptaPankaj Gupta, PhD
Lecture on Recurrent Neural Network at University of Munich (LMU), as part of Deep Learning & AI lecture series.
Includes: Fundamentals of RNNs, Need for LSTM and GRU.
Angular 4 Data Binding | Two Way Data Binding in Angular 4 | Angular 4 Tutori...Edureka!
This Angular 4 tutorial will introduce you to the Angular Data Binding concept.
To watch the YouTube videos in this Angular 4 tutorial playlist, click here: https://www.youtube.com/watch?v=R4wGCHzn6-Q&list=PL9ooVrP1hQOF4aDuqaWYWSuj1isPF6HHg.
From Linked Data to Semantic ApplicationsAndre Freitas
In this talk we will discuss how to build (today) semantically intelligent systems, i.e. systems with the ability to process and interpret information by its meaning. We will take a multidisciplinary perspective showing how recent advances in other computer science areas such as Information Retrieval and Natural Language Processing can enable, together with Linked Data and Semantic Web resources, the construction of the next generation of information systems. A summary of the core principles and available
resources from these areas will give a concrete understanding on how to jump-start your own semantic system.
SplunkLive! London 2017 - An End-To-End Approach: Detect via Behavious and Re...Splunk
Understanding your security impact enables you to be faster and smarter about how you approach security threats. Whether you're looking to reduce breaches, set up monitoring to anticipate attacks, build more predictive capabilities or need quality reporting for an audit, you will learn how to leverage Splunk's analytics-driven security platform to analyse your data by using the power of our Search Processing Language (SPL). We'll also present how to implement and up-level your security today with actionable searches that can immediately be put to use in your environment. In this session, you will learn how to: - Optimise and make Splunk search work for you, so you can quickly gain insights into your data to identify and describe security impacts and potential threats - Detect unusual and potentially malicious activity threats using Splunk Enterprise statistical and behavorial analysis capabilities - Find unusual activities (using expected alert volume)
SplunkLive! London 2017 - Build a Security Portfolio That Strengthens Your Se...Splunk
All data is security relevant – whether you are an IT or security professional, it is important to gain context into all your data to understand your environment, quickly hunt for and investigate potential threats in your environment, and take action to remediate. In this session, you will learn how to: - Leverage your data across silos with analytics-driven security - Operationalise all relevant data to gain greater visibility of your environment to make more informed decisions - Optimise incident response to more clearly understand an attack and the sequential relationship between events to quickly determine the appropriate next steps - Improve investigation and remediation times by automating decisions or by using human-assisted decisions with full context from adaptive response - Utilise Splunk User Behavior Analytics and verify privileged access and detect unusual activity by using UBA anomalies
Linked data for Enterprise Data IntegrationSören Auer
The Web evolves into a Web of Data. In parallel Intranets of large companies will evolve into Data Intranets based on the Linked Data principles. Linked Data has the potential to complement the SOA paradigm with a light-weight, adaptive data integration approach.
R is among the most popular programming languages among data science professionals. In this guide learn about the basic concepts and various functionalities it offers.
1 Exploratory Data Analysis (EDA) by Melvin Ott, PhD.docxhoney725342
1
Exploratory Data Analysis (EDA)
by Melvin Ott, PhD
September, 2017
Introduction
The Masters in Predictive Analytics program at Northwestern University offers
graduate courses that cover predictive modeling using several software products
such as SAS, R and Python. The Predict 410 course is one of the core courses and
this section focuses on using Python.
Predict 410 will follow a sequence in the assignments. The first assignment will ask
you to perform an EDA(See Ratner1 Chapters 1&2) for the Ames Housing Data
dataset to determine the best single variable model. It will be followed by an
assignment to expand to a multivariable model. Python software for boxplots,
scatterplots and more will help you identify the single variable. However, it is easy
to get lost in the programming and lose sight of the objective. Namely, which of
the variable choices best explain the variability in the response variable?
(You will need to be familiar with the data types and level of measurement. This
will be critical in determining the choice of when to use a dummy variable for model
building. If this topic is new to you review the definitions at Types of Data before
reading further.)
This report will help you become familiar with some of the tools for EDA and allow
you to interact with the data by using links to a software product, Shiny, that will
demonstrate and interact with you to produce various plots of the data. Shiny is
located on a cloud server and will allow you to make choices in looking at the plots
for the data. Study the plots carefully. This is your initial EDA tool and leads to
your model building and your overall understanding of predictive analytics.
Single Variable Linear Regression EDA
1. Become Familiar With the Data
2
Identify the variables that are categorical and the variables that are quantitative.
For the Ames Housing Data, you should review the Ames Data Description pdf file.
2. Look at Plots of the Data
For the variables that are quantitative, you should look at scatter plots vs the
response variable saleprice. For the categorical variables, look at boxplots vs
saleprice. You have sample Python code to help with the EDA and below are some
links that will demonstrate the relationships for the a different building_prices
dataset.
For the boxplots with Shiny:
Click here
For the scatterplots with Shiny:
Click here
3. Begin Writing Python Code
Start with the shell code and improve on the model provided.
http://melvin.shinyapps.io/SboxPlot
http://melvin.shinyapps.io/SScatter/
http://melvin.shinyapps.io/SScatter/
3
Single Variable Logistic Regression EDA
1. Become Familiar With the Data
In 411 you will have an introduction to logistic regression and again will ask you to
perform an EDA. See the file credit data for more info. Make sure you recognize
which variables are quantitative and which are catego ...
Adding intelligence to applications - AIM201 - Chicago AWS SummitAmazon Web Services
AI has already been integrated into many use cases, but we've just scratched the surface of what's possible. In this session, we cover how to use the AWS AI services to tackle three use cases that can deliver immediate value: 1) “voice of the customer” analytics to better understand what your customers are thinking and saying; 2) document analysis and processing to move beyond the limitations of traditional OCR; and 3) chatbots to improve in-app customer service and customer contact center experiences. We also discuss how to use AI in use cases within the media, healthcare, and financial services industries.
SplunkLive! London 2017 - Happy Apps, Happy UsersSplunk
No matter what business you’re in, your web applications are front-and-center for your customers. Downtime, or even bad performance not only creates a spike in costs, they often translate into loss of customers and revenue. You need immediate insight into the availability, performance and usage of your applications and the infrastructure your applications run on. In this session, you will learn why you need to take a platform approach to full stack application management, whether your applications reside on-premises or in the cloud. Second, we will show you how you can use Splunk to monitor the usage and performance of your applications, and quickly troubleshoot faults by stepping through some of the most common issues our customers experience. Third, we’ll contrast what Splunk does relative to other APM tools you may already have deployed, and even show you how you can bring APM data into Splunk to gain more insight into application performance.
textTOvec: Deep Contextualized Neural Autoregressive Topic Models of Language...Pankaj Gupta, PhD
ICLR 2019 conference paper.
Improving Topic Modeling with language structures (e.g., word ordering, local context, syntactic and semantic information); Neural Composite Generative Model: A Neural Topic + A Neural Language Model; Expressing both short-and long-range dependencies
Poster: Neural Relation ExtractionWithin and Across Sentence BoundariesPankaj Gupta, PhD
AAAI-19 paper: Neural Relation ExtractionWithin and Across Sentence Boundaries
Authors: Pankaj Gupta, Subburam Rajaram, Hinrich Schuetze and Thomas Runkler
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To watch the YouTube videos in this Angular 4 tutorial playlist, click here: https://www.youtube.com/watch?v=R4wGCHzn6-Q&list=PL9ooVrP1hQOF4aDuqaWYWSuj1isPF6HHg.
From Linked Data to Semantic ApplicationsAndre Freitas
In this talk we will discuss how to build (today) semantically intelligent systems, i.e. systems with the ability to process and interpret information by its meaning. We will take a multidisciplinary perspective showing how recent advances in other computer science areas such as Information Retrieval and Natural Language Processing can enable, together with Linked Data and Semantic Web resources, the construction of the next generation of information systems. A summary of the core principles and available
resources from these areas will give a concrete understanding on how to jump-start your own semantic system.
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Understanding your security impact enables you to be faster and smarter about how you approach security threats. Whether you're looking to reduce breaches, set up monitoring to anticipate attacks, build more predictive capabilities or need quality reporting for an audit, you will learn how to leverage Splunk's analytics-driven security platform to analyse your data by using the power of our Search Processing Language (SPL). We'll also present how to implement and up-level your security today with actionable searches that can immediately be put to use in your environment. In this session, you will learn how to: - Optimise and make Splunk search work for you, so you can quickly gain insights into your data to identify and describe security impacts and potential threats - Detect unusual and potentially malicious activity threats using Splunk Enterprise statistical and behavorial analysis capabilities - Find unusual activities (using expected alert volume)
SplunkLive! London 2017 - Build a Security Portfolio That Strengthens Your Se...Splunk
All data is security relevant – whether you are an IT or security professional, it is important to gain context into all your data to understand your environment, quickly hunt for and investigate potential threats in your environment, and take action to remediate. In this session, you will learn how to: - Leverage your data across silos with analytics-driven security - Operationalise all relevant data to gain greater visibility of your environment to make more informed decisions - Optimise incident response to more clearly understand an attack and the sequential relationship between events to quickly determine the appropriate next steps - Improve investigation and remediation times by automating decisions or by using human-assisted decisions with full context from adaptive response - Utilise Splunk User Behavior Analytics and verify privileged access and detect unusual activity by using UBA anomalies
Linked data for Enterprise Data IntegrationSören Auer
The Web evolves into a Web of Data. In parallel Intranets of large companies will evolve into Data Intranets based on the Linked Data principles. Linked Data has the potential to complement the SOA paradigm with a light-weight, adaptive data integration approach.
R is among the most popular programming languages among data science professionals. In this guide learn about the basic concepts and various functionalities it offers.
1 Exploratory Data Analysis (EDA) by Melvin Ott, PhD.docxhoney725342
1
Exploratory Data Analysis (EDA)
by Melvin Ott, PhD
September, 2017
Introduction
The Masters in Predictive Analytics program at Northwestern University offers
graduate courses that cover predictive modeling using several software products
such as SAS, R and Python. The Predict 410 course is one of the core courses and
this section focuses on using Python.
Predict 410 will follow a sequence in the assignments. The first assignment will ask
you to perform an EDA(See Ratner1 Chapters 1&2) for the Ames Housing Data
dataset to determine the best single variable model. It will be followed by an
assignment to expand to a multivariable model. Python software for boxplots,
scatterplots and more will help you identify the single variable. However, it is easy
to get lost in the programming and lose sight of the objective. Namely, which of
the variable choices best explain the variability in the response variable?
(You will need to be familiar with the data types and level of measurement. This
will be critical in determining the choice of when to use a dummy variable for model
building. If this topic is new to you review the definitions at Types of Data before
reading further.)
This report will help you become familiar with some of the tools for EDA and allow
you to interact with the data by using links to a software product, Shiny, that will
demonstrate and interact with you to produce various plots of the data. Shiny is
located on a cloud server and will allow you to make choices in looking at the plots
for the data. Study the plots carefully. This is your initial EDA tool and leads to
your model building and your overall understanding of predictive analytics.
Single Variable Linear Regression EDA
1. Become Familiar With the Data
2
Identify the variables that are categorical and the variables that are quantitative.
For the Ames Housing Data, you should review the Ames Data Description pdf file.
2. Look at Plots of the Data
For the variables that are quantitative, you should look at scatter plots vs the
response variable saleprice. For the categorical variables, look at boxplots vs
saleprice. You have sample Python code to help with the EDA and below are some
links that will demonstrate the relationships for the a different building_prices
dataset.
For the boxplots with Shiny:
Click here
For the scatterplots with Shiny:
Click here
3. Begin Writing Python Code
Start with the shell code and improve on the model provided.
http://melvin.shinyapps.io/SboxPlot
http://melvin.shinyapps.io/SScatter/
http://melvin.shinyapps.io/SScatter/
3
Single Variable Logistic Regression EDA
1. Become Familiar With the Data
In 411 you will have an introduction to logistic regression and again will ask you to
perform an EDA. See the file credit data for more info. Make sure you recognize
which variables are quantitative and which are catego ...
Adding intelligence to applications - AIM201 - Chicago AWS SummitAmazon Web Services
AI has already been integrated into many use cases, but we've just scratched the surface of what's possible. In this session, we cover how to use the AWS AI services to tackle three use cases that can deliver immediate value: 1) “voice of the customer” analytics to better understand what your customers are thinking and saying; 2) document analysis and processing to move beyond the limitations of traditional OCR; and 3) chatbots to improve in-app customer service and customer contact center experiences. We also discuss how to use AI in use cases within the media, healthcare, and financial services industries.
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No matter what business you’re in, your web applications are front-and-center for your customers. Downtime, or even bad performance not only creates a spike in costs, they often translate into loss of customers and revenue. You need immediate insight into the availability, performance and usage of your applications and the infrastructure your applications run on. In this session, you will learn why you need to take a platform approach to full stack application management, whether your applications reside on-premises or in the cloud. Second, we will show you how you can use Splunk to monitor the usage and performance of your applications, and quickly troubleshoot faults by stepping through some of the most common issues our customers experience. Third, we’ll contrast what Splunk does relative to other APM tools you may already have deployed, and even show you how you can bring APM data into Splunk to gain more insight into application performance.
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ICLR 2019 conference paper.
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Poster: Neural Relation ExtractionWithin and Across Sentence BoundariesPankaj Gupta, PhD
AAAI-19 paper: Neural Relation ExtractionWithin and Across Sentence Boundaries
Authors: Pankaj Gupta, Subburam Rajaram, Hinrich Schuetze and Thomas Runkler
PhD in Machine Learning / Deep Learning / Natural Language Processing
Profile: https://www.linkedin.com/in/pankaj-gupta-6b95bb17/
Research Contributions: https://scholar.google.com/citations?user=_YjIJF0AAAAJ&hl=en
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Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
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Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.