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
Introduction to Natural Language ProcessingPranav Gupta
the presentation gives a gist about the major tasks and challenges involved in natural language processing. In the second part, it talks about one technique each for Part Of Speech Tagging and Automatic Text Summarization
This Machine Learning presentation is ideal for beginners to learn Machine Learning from scratch. By the end of this presentation, you will learn why Machine Learning is so important in our lives, what is Machine Learning, the various types of Machine Learning (Supervised, Unsupervised and Reinforcement learning), how do we choose the right Machine Learning solution, what are the different Machine Learning algorithms and how do they work (with simple examples and use-cases).
This Machine Learning presentation will cover the following topics:
1. Life without Machine Learning
2. Life with Machine Learning
3. What is Machine Learning
4. Machine Learning Process
5. Types of Machine Learning
6. Supervised Vs Unsupervised
7. The right Machine Learning solutions
8. Machine Learning Algorithms
9. Use case - Predicting the price of a house using Linear Regression
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.
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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.
- - - - - - -
Who should take this Machine Learning Training Course?
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
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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
Introduction to Natural Language ProcessingPranav Gupta
the presentation gives a gist about the major tasks and challenges involved in natural language processing. In the second part, it talks about one technique each for Part Of Speech Tagging and Automatic Text Summarization
This Machine Learning presentation is ideal for beginners to learn Machine Learning from scratch. By the end of this presentation, you will learn why Machine Learning is so important in our lives, what is Machine Learning, the various types of Machine Learning (Supervised, Unsupervised and Reinforcement learning), how do we choose the right Machine Learning solution, what are the different Machine Learning algorithms and how do they work (with simple examples and use-cases).
This Machine Learning presentation will cover the following topics:
1. Life without Machine Learning
2. Life with Machine Learning
3. What is Machine Learning
4. Machine Learning Process
5. Types of Machine Learning
6. Supervised Vs Unsupervised
7. The right Machine Learning solutions
8. Machine Learning Algorithms
9. Use case - Predicting the price of a house using Linear Regression
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.
- - - - - - -
Who should take this Machine Learning Training Course?
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
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Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
This talk is about how we applied deep learning techinques to achieve state-of-the-art results in various NLP tasks like sentiment analysis and aspect identification, and how we deployed these models at Flipkart
Simply put, semantic analysis is the process of drawing meaning from text. It allows computers to understand and interpret sentences, paragraphs, or whole documents, by analyzing their grammatical structure, and identifying relationships between individual words in a particular context.
Machine Learning and Real-World ApplicationsMachinePulse
This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.
This talk is about how we applied deep learning techinques to achieve state-of-the-art results in various NLP tasks like sentiment analysis and aspect identification, and how we deployed these models at Flipkart
Simply put, semantic analysis is the process of drawing meaning from text. It allows computers to understand and interpret sentences, paragraphs, or whole documents, by analyzing their grammatical structure, and identifying relationships between individual words in a particular context.
Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things. Lecture related to machine learning. Here you can read multiple things.
... two decades of correlation, hierarchies, networks and clustering in financial markets
Summary of some of my past research work at Complex Networks 2022.
The study of correlations, hierarchies, networks and communities (or clustering) has more than 20 years of history in econophysics.
However, for the practitioner, it seems that these tools are not fully ready yet:
Many questions around their proper use for trading or risk monitoring are left unanswered.
Deep Learning might help solve some hard problems such as finding more reliably communities (or clusters) and their number.
Running large simulations (based on GANs, VAEs or realistic market simulators) could also help understand when complex networks methods can give wrong insights (e.g. not enough data, or not stationary enough; too low correlations).
Conference: Complex Networks 2022 in Palermo, Sicily, Italy.
Automated machine learning lectures given at the Advanced Course on Data Science & Machine Learning. AutoML, hyperparameter optimization, Bayesian optimization, Neural Architecture Search, Meta-learning, MAML
Applied Stochastic Processes, Chaos Modeling, and Probabilistic Properties of...e2wi67sy4816pahn
This book is intended for professionals in data science, computer science, operations research, statistics, machine learning, big data, and mathematics. In 100 pages, it covers many new topics, offering a fresh perspective on the subject. It is accessible to practitioners with a two-year college-level exposure to statistics and probability. The compact and tutorial style, featuring many applications (Blockchain, quantum algorithms, HPC, random number generation, cryptography, Fintech, web crawling, statistical testing) with numerous illustrations, is aimed at practitioners, researchers and executives in various quantitative fields.
New ideas, advanced topics, and state-of-the-art research are discussed in simple English, without using jargon or arcane theory. It unifies topics that are usually part of different fields (data science, operations research, dynamical systems, computer science, number theory, probability) broadening the knowledge and interest of the reader in ways that are not found in any other book. This short book contains a large amount of condensed material that would typically be covered in 500 pages in traditional publications. Thanks to cross-references and redundancy, the chapters can be read independently, in random order.
Big Data Day LA 2015 - Lessons Learned Designing Data Ingest Systemsaaamase
During my time working on attribution and ingest systems, I've encountered several different approaches to solving the simple question: "How do I get data from A to B". In this session, I'd like to share some of the problems I've encountered and how to effectively solve them.
This slide gives brief overview of supervised, unsupervised and reinforcement learning. Algorithms discussed are Naive Bayes, K nearest neighbour, SVM,decision tree, Markov model.
Difference between regression and classification. difference between supervised and reinforcement, iterative functioning of Markov model and machine learning applications.
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.
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.”
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).
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
2. INFORMATION EXTRACTION
• Information extraction is the process of acquiring knowledge by
skimming a text and looking for occurrences of a particular class of
object and for relationships among objects.
• A typical task is to extract instances of addresses from Web pages, with
database fields for street, city, state, and zip code; or instances of storms
from weather reports, with fields for temperature, wind speed, and
precipitation.
• In a limited domain, this can be done with high accuracy. As the domain
gets more general, more complex linguistic models and more complex
learning techniques are necessary.
3. Finite-state automata for information extraction
• The simplest type of information extraction system is an attribute-
based extraction system that assumes that the entire text refers to a
single object and the task is to extract attributes of that object.
• the problem of extracting from the text
“IBM ThinkBook 970.Our price: $399.00”
• the set of attributes,
{Manufacturer=IBM, Model=ThinkBook970, Price=$399.00}
• We can address this problem by defining a template (also known as a
pattern) for each attribute we would like to extract.
4. Cont.,
• The template is defined by a finite state automaton, the simplest
example of which is the regular expression, or regex.
• Regular expressions are used in Unix commands such as grep, in
programming languages such as Perl, and in word processors such as
Microsoft Word.
• The details vary slightly from one tool to another and so are best
learned from the appropriate manual.
5. Cont.,
• If a regular expression for an attribute matches the text exactly once,
then we can pull out the portion of the text that is the value of the
attribute.
• If there is no match, all we can do is give a default value or leave the
attribute missing; but if there are several matches, we need a process
to choose among them.
• One strategy is to have several templates for each attribute, ordered
by priority.
• One step up from attribute-based extraction systems are relational
extraction systems, which deal with multiple objects and the relations
among them.
6. Cons.,
• A relational extraction system can be built as a series of cascaded
finite-state transducers.
• That is, the system consists of a series of small, efficient finite-state
automata (FSAs), where each automaton receives text as input,
transduces the text into a different format, and passes it along to the
next automaton.
7. Cons.,
• FASTUS consists of five stages:
• 1. Tokenization - which segments the stream of characters into tokens.
• 2. Complex-word handling - including collocations such as “set up”
• 3. Basic-group handling - meaning noun groups and verb groups. The
idea is to chunk these into units that will be
managed by the later stages.
• 4. Complex-phrase handling - combines the basic groups into complex
phrases. Again, the aim is to have rules that are
finite-state and thus can be processed quickly, and that
result in unambiguous (or nearly unambiguous) output phrases.
• 5. Structure merging
8. Probabilistic models for information extraction
• When information extraction must be attempted from noisy or varied
input, simple finite-state approaches fare poorly.
• It is too hard to get all the rules and their priorities right; it is better to
use a probabilistic model rather than a rule-based model.
• The simplest probabilistic model for sequences with hidden state is
the hidden Markov model, or HMM.
9. Conditional random fields for information extraction
• One issue with HMMs for the information extraction task is that they
model a lot of probabilities that we don’t really need.
• An HMM is a generative model; it models the full joint probability of
observations and hidden states, and thus can be used to generate
samples.
• All we need in order to understand a text is a discriminative model, one
that models the conditional probability of the hidden attributes given the
observations (the text).
• Given a text e1:N, the conditional model finds the hidden state sequence
X1:N that maximizes P(X1:N | e1:N)
10. Cont.,
• We don’t need the independence assumptions of the Markov
model—we can have an Xt that is dependent on X1.
• A framework for this type of model is the conditional random field,
or CRF, which models a conditional probability distribution of a set of
target variables given a set of observed variables.
• One common structure is the linear-chain conditional random field
for representing Markov dependencies among variables in a temporal
sequence.
11. Ontology extraction from large corpora
• So far we have thought of information extraction as finding a specific
set of relations (e.g., speaker, time, location) in a specific text (e.g., a
talk announcement).
• A different application of extraction technology is building a large
knowledge base or ontology of facts from a corpus.
12. Cont.,
This is different in three ways:
• First :
• it is open-ended—we want to acquire facts about all types of domains,
not just one specific domain.
• Second:
• With a large corpus, this task is dominated by precision, not recall—just as
with question answering on the Web.
• Third:
• The results can be statistical aggregates gathered from multiple sources,
rather than being extracted from one specific text.
13. Automated template construction
• The subcategory relation is so fundamental that is worthwhile to
handcraft a few templates to help identify instances of it occurring in
natural language text.
• But what about the thousands of other relations in the world? There
aren’t enough AI grad students in the world to create and debug
templates for all of them.
• Fortunately, it is possible to learn templates from a few examples,
then use the templates to learn more examples, from which more
templates can be learned, and so on.
14. Machine reading
• Automated template construction is a big step up from handcrafted
template construction, but it still requires a handful of labeled
examples of each relation to get started.
• To build a large ontology with many thousands of relations, even that
amount of work would be onerous;
• we would like to have an extraction system with no human input of
any kind—a system that could read on its own and build up its own
database.
15. Cont.,
• They behave less like a traditional information extraction system that
is targeted at a few relations and more like a human reader who
learns from the text itself;
• Because of this the field has been called machine reading.
• A representative machine-reading system is TEXTRUNNER (Banko and
Etzioni, 2008).
• TEXTRUNNER uses co-training to boost its performance, but it needs
something to bootstrap.