SlideShare a Scribd company logo

Finding needles in haystacks with deep neural networks

Calvin Giles
Calvin Giles
Calvin GilesData Scientist

PyData London 2016 talk on detecting duplicate products at Lyst

Finding needles in haystacks with deep neural networks

1 of 92
Download to read offline
Finding needles in haystacks
with deep neural networks
1
Calvin Giles
calvin.giles@gmail.com
@calvingiles
2
Calvin Giles
calvin.giles@gmail.com
@calvingiles
Who am I
Data scientist at Lyst
PyData organiser
Recovering Nuclear Physicist
Started learning ML 7 years ago
Trained my first neural network 6 months ago
Needles
We have 30 million products
in our database.
We would like to find pairs
of these products that are
the same.
3
Haystacks
A brute force search of our
database would find one
correct pair in every 1,000.
4
One product
Eight retailers
Users like duplicate grouping
Machine learning tasks are
easier on merged data
Why bother?
5
But first, a tangent into perspective…
(Or why some perspectives are better than others)
6

Recommended

Fashion product de-duplication with image similarity and LSH
Fashion product de-duplication with image similarity and LSHFashion product de-duplication with image similarity and LSH
Fashion product de-duplication with image similarity and LSHEddie Bell
 
Ai in 45 minutes
Ai in 45 minutesAi in 45 minutes
Ai in 45 minutes昉达 王
 
John Maxwell, Data Scientist, Nordstrom at MLconf Seattle 2017
John Maxwell, Data Scientist, Nordstrom at MLconf Seattle 2017 John Maxwell, Data Scientist, Nordstrom at MLconf Seattle 2017
John Maxwell, Data Scientist, Nordstrom at MLconf Seattle 2017 MLconf
 
Dm part03 neural-networks-homework
Dm part03 neural-networks-homeworkDm part03 neural-networks-homework
Dm part03 neural-networks-homeworkokeee
 
Computer Vision: Correlation, Convolution, and Gradient
Computer Vision: Correlation, Convolution, and GradientComputer Vision: Correlation, Convolution, and Gradient
Computer Vision: Correlation, Convolution, and GradientAhmed Gad
 
InfoGAN and Generative Adversarial Networks
InfoGAN and Generative Adversarial NetworksInfoGAN and Generative Adversarial Networks
InfoGAN and Generative Adversarial NetworksZak Jost
 
Hitchhiker Trees - Strangeloop 2016
Hitchhiker Trees - Strangeloop 2016Hitchhiker Trees - Strangeloop 2016
Hitchhiker Trees - Strangeloop 2016David Greenberg
 

More Related Content

What's hot

Machine Learning Tutorial Part - 2 | Machine Learning Tutorial For Beginners ...
Machine Learning Tutorial Part - 2 | Machine Learning Tutorial For Beginners ...Machine Learning Tutorial Part - 2 | Machine Learning Tutorial For Beginners ...
Machine Learning Tutorial Part - 2 | Machine Learning Tutorial For Beginners ...Simplilearn
 
Chapter3 bp
Chapter3   bpChapter3   bp
Chapter3 bpkumar tm
 
Support Vector Machine and Implementation using Weka
Support Vector Machine and Implementation using WekaSupport Vector Machine and Implementation using Weka
Support Vector Machine and Implementation using WekaMacha Pujitha
 
Introduction to Machine Learning
Introduction to Machine LearningIntroduction to Machine Learning
Introduction to Machine LearningNandita Naik
 
Basics of Machine Learning
Basics of Machine LearningBasics of Machine Learning
Basics of Machine LearningPranav Challa
 
Support Vector Machine (Classification) - Step by Step
Support Vector Machine (Classification) - Step by StepSupport Vector Machine (Classification) - Step by Step
Support Vector Machine (Classification) - Step by StepManish nath choudhary
 

What's hot (7)

Machine Learning Tutorial Part - 2 | Machine Learning Tutorial For Beginners ...
Machine Learning Tutorial Part - 2 | Machine Learning Tutorial For Beginners ...Machine Learning Tutorial Part - 2 | Machine Learning Tutorial For Beginners ...
Machine Learning Tutorial Part - 2 | Machine Learning Tutorial For Beginners ...
 
Chapter3 bp
Chapter3   bpChapter3   bp
Chapter3 bp
 
Support Vector Machine and Implementation using Weka
Support Vector Machine and Implementation using WekaSupport Vector Machine and Implementation using Weka
Support Vector Machine and Implementation using Weka
 
Analytical Study of AES and Proposed Variant with Enhance Block Length and Ke...
Analytical Study of AES and Proposed Variant with Enhance Block Length and Ke...Analytical Study of AES and Proposed Variant with Enhance Block Length and Ke...
Analytical Study of AES and Proposed Variant with Enhance Block Length and Ke...
 
Introduction to Machine Learning
Introduction to Machine LearningIntroduction to Machine Learning
Introduction to Machine Learning
 
Basics of Machine Learning
Basics of Machine LearningBasics of Machine Learning
Basics of Machine Learning
 
Support Vector Machine (Classification) - Step by Step
Support Vector Machine (Classification) - Step by StepSupport Vector Machine (Classification) - Step by Step
Support Vector Machine (Classification) - Step by Step
 

Viewers also liked

Working with Fashion Models - PyDataLondon 2016
Working with Fashion Models - PyDataLondon 2016Working with Fashion Models - PyDataLondon 2016
Working with Fashion Models - PyDataLondon 2016Eddie Bell
 
LSH for
 Prediction Problem in Recommendation
LSH for
 Prediction Problem in RecommendationLSH for
 Prediction Problem in Recommendation
LSH for
 Prediction Problem in RecommendationMaruf Aytekin
 
Дмитрий Селиванов, OK.RU. Finding Similar Items in high-dimensional spaces: L...
Дмитрий Селиванов, OK.RU. Finding Similar Items in high-dimensional spaces: L...Дмитрий Селиванов, OK.RU. Finding Similar Items in high-dimensional spaces: L...
Дмитрий Селиванов, OK.RU. Finding Similar Items in high-dimensional spaces: L...Mail.ru Group
 
Primodels review basics of fashion modeling
Primodels review basics of fashion modelingPrimodels review basics of fashion modeling
Primodels review basics of fashion modelingPrimodels
 
High Fashion Jewelry Catalog 09 10
High  Fashion Jewelry Catalog 09 10High  Fashion Jewelry Catalog 09 10
High Fashion Jewelry Catalog 09 10krice
 
R&R Trends
R&R TrendsR&R Trends
R&R Trendshegdesm
 
Primodels Review Modeling and Fashion
Primodels Review Modeling and FashionPrimodels Review Modeling and Fashion
Primodels Review Modeling and FashionPrimodels
 
Tips for fashion modelling
Tips for fashion modellingTips for fashion modelling
Tips for fashion modellingsumita sikka
 
Modelling 101
Modelling 101   Modelling 101
Modelling 101 Xyne Dizon
 
Primodels Scam- Features to Become a Model in Fashion Modeling
Primodels Scam- Features to Become a Model in Fashion ModelingPrimodels Scam- Features to Become a Model in Fashion Modeling
Primodels Scam- Features to Become a Model in Fashion ModelingPrimodels
 
All about Fashion Modelling
All about Fashion ModellingAll about Fashion Modelling
All about Fashion ModellingPrimodels
 
Models & Modeling Categories
Models & Modeling CategoriesModels & Modeling Categories
Models & Modeling CategoriesCini Mathew
 
[2A6]web & health 2.0. 회사에서의 data science란?
[2A6]web & health 2.0. 회사에서의 data science란?[2A6]web & health 2.0. 회사에서의 data science란?
[2A6]web & health 2.0. 회사에서의 data science란?NAVER D2
 
Mining of massive datasets using locality sensitive hashing (LSH)
Mining of massive datasets using locality sensitive hashing (LSH)Mining of massive datasets using locality sensitive hashing (LSH)
Mining of massive datasets using locality sensitive hashing (LSH)J Singh
 
3 - Finding similar items
3 - Finding similar items3 - Finding similar items
3 - Finding similar itemsViet-Trung TRAN
 
A Gentle Introduction to Locality Sensitive Hashing with Apache Spark
A Gentle Introduction to Locality Sensitive Hashing with Apache SparkA Gentle Introduction to Locality Sensitive Hashing with Apache Spark
A Gentle Introduction to Locality Sensitive Hashing with Apache SparkFrançois Garillot
 
Deep Learning for NLP: An Introduction to Neural Word Embeddings
Deep Learning for NLP: An Introduction to Neural Word EmbeddingsDeep Learning for NLP: An Introduction to Neural Word Embeddings
Deep Learning for NLP: An Introduction to Neural Word EmbeddingsRoelof Pieters
 
Introduction to Chainer: A Flexible Framework for Deep Learning
Introduction to Chainer: A Flexible Framework for Deep LearningIntroduction to Chainer: A Flexible Framework for Deep Learning
Introduction to Chainer: A Flexible Framework for Deep LearningSeiya Tokui
 

Viewers also liked (20)

Working with Fashion Models - PyDataLondon 2016
Working with Fashion Models - PyDataLondon 2016Working with Fashion Models - PyDataLondon 2016
Working with Fashion Models - PyDataLondon 2016
 
LSH for
 Prediction Problem in Recommendation
LSH for
 Prediction Problem in RecommendationLSH for
 Prediction Problem in Recommendation
LSH for
 Prediction Problem in Recommendation
 
Дмитрий Селиванов, OK.RU. Finding Similar Items in high-dimensional spaces: L...
Дмитрий Селиванов, OK.RU. Finding Similar Items in high-dimensional spaces: L...Дмитрий Селиванов, OK.RU. Finding Similar Items in high-dimensional spaces: L...
Дмитрий Селиванов, OK.RU. Finding Similar Items in high-dimensional spaces: L...
 
LSH
LSHLSH
LSH
 
lsh
lshlsh
lsh
 
Primodels review basics of fashion modeling
Primodels review basics of fashion modelingPrimodels review basics of fashion modeling
Primodels review basics of fashion modeling
 
High Fashion Jewelry Catalog 09 10
High  Fashion Jewelry Catalog 09 10High  Fashion Jewelry Catalog 09 10
High Fashion Jewelry Catalog 09 10
 
R&R Trends
R&R TrendsR&R Trends
R&R Trends
 
Primodels Review Modeling and Fashion
Primodels Review Modeling and FashionPrimodels Review Modeling and Fashion
Primodels Review Modeling and Fashion
 
Tips for fashion modelling
Tips for fashion modellingTips for fashion modelling
Tips for fashion modelling
 
Modelling 101
Modelling 101   Modelling 101
Modelling 101
 
Primodels Scam- Features to Become a Model in Fashion Modeling
Primodels Scam- Features to Become a Model in Fashion ModelingPrimodels Scam- Features to Become a Model in Fashion Modeling
Primodels Scam- Features to Become a Model in Fashion Modeling
 
All about Fashion Modelling
All about Fashion ModellingAll about Fashion Modelling
All about Fashion Modelling
 
Models & Modeling Categories
Models & Modeling CategoriesModels & Modeling Categories
Models & Modeling Categories
 
[2A6]web & health 2.0. 회사에서의 data science란?
[2A6]web & health 2.0. 회사에서의 data science란?[2A6]web & health 2.0. 회사에서의 data science란?
[2A6]web & health 2.0. 회사에서의 data science란?
 
Mining of massive datasets using locality sensitive hashing (LSH)
Mining of massive datasets using locality sensitive hashing (LSH)Mining of massive datasets using locality sensitive hashing (LSH)
Mining of massive datasets using locality sensitive hashing (LSH)
 
3 - Finding similar items
3 - Finding similar items3 - Finding similar items
3 - Finding similar items
 
A Gentle Introduction to Locality Sensitive Hashing with Apache Spark
A Gentle Introduction to Locality Sensitive Hashing with Apache SparkA Gentle Introduction to Locality Sensitive Hashing with Apache Spark
A Gentle Introduction to Locality Sensitive Hashing with Apache Spark
 
Deep Learning for NLP: An Introduction to Neural Word Embeddings
Deep Learning for NLP: An Introduction to Neural Word EmbeddingsDeep Learning for NLP: An Introduction to Neural Word Embeddings
Deep Learning for NLP: An Introduction to Neural Word Embeddings
 
Introduction to Chainer: A Flexible Framework for Deep Learning
Introduction to Chainer: A Flexible Framework for Deep LearningIntroduction to Chainer: A Flexible Framework for Deep Learning
Introduction to Chainer: A Flexible Framework for Deep Learning
 

Similar to Finding needles in haystacks with deep neural networks

Designing a neural network architecture for image recognition
Designing a neural network architecture for image recognitionDesigning a neural network architecture for image recognition
Designing a neural network architecture for image recognitionShandukaniVhulondo
 
Sippin: A Mobile Application Case Study presented at Techfest Louisville
Sippin: A Mobile Application Case Study presented at Techfest LouisvilleSippin: A Mobile Application Case Study presented at Techfest Louisville
Sippin: A Mobile Application Case Study presented at Techfest LouisvilleDawn Yankeelov
 
Barga Data Science lecture 4
Barga Data Science lecture 4Barga Data Science lecture 4
Barga Data Science lecture 4Roger Barga
 
Deep Learning for Computer Vision - PyconDE 2017
Deep Learning for Computer Vision - PyconDE 2017Deep Learning for Computer Vision - PyconDE 2017
Deep Learning for Computer Vision - PyconDE 2017Alex Conway
 
Machine Learning, Deep Learning and Data Analysis Introduction
Machine Learning, Deep Learning and Data Analysis IntroductionMachine Learning, Deep Learning and Data Analysis Introduction
Machine Learning, Deep Learning and Data Analysis IntroductionTe-Yen Liu
 
Machine learning in the life sciences with knime
Machine learning in the life sciences with knimeMachine learning in the life sciences with knime
Machine learning in the life sciences with knimeGreg Landrum
 
Neural Networks in Data Mining - “An Overview”
Neural Networks  in Data Mining -   “An Overview”Neural Networks  in Data Mining -   “An Overview”
Neural Networks in Data Mining - “An Overview”Dr.(Mrs).Gethsiyal Augasta
 
Report face recognition : ArganRecogn
Report face recognition :  ArganRecognReport face recognition :  ArganRecogn
Report face recognition : ArganRecognIlyas CHAOUA
 
Machine learning and vulnerabilities
Machine learning and vulnerabilitiesMachine learning and vulnerabilities
Machine learning and vulnerabilitiesgalazzo
 
Troubleshooting Deep Neural Networks - Full Stack Deep Learning
Troubleshooting Deep Neural Networks - Full Stack Deep LearningTroubleshooting Deep Neural Networks - Full Stack Deep Learning
Troubleshooting Deep Neural Networks - Full Stack Deep LearningSergey Karayev
 
Semi-Supervised Insight Generation from Petabyte Scale Text Data
Semi-Supervised Insight Generation from Petabyte Scale Text DataSemi-Supervised Insight Generation from Petabyte Scale Text Data
Semi-Supervised Insight Generation from Petabyte Scale Text DataTech Triveni
 
Barga Data Science lecture 9
Barga Data Science lecture 9Barga Data Science lecture 9
Barga Data Science lecture 9Roger Barga
 
Deep Learning for Computer Vision - ExecutiveML
Deep Learning for Computer Vision - ExecutiveMLDeep Learning for Computer Vision - ExecutiveML
Deep Learning for Computer Vision - ExecutiveMLAlex Conway
 
Python for Image Understanding: Deep Learning with Convolutional Neural Nets
Python for Image Understanding: Deep Learning with Convolutional Neural NetsPython for Image Understanding: Deep Learning with Convolutional Neural Nets
Python for Image Understanding: Deep Learning with Convolutional Neural NetsRoelof Pieters
 
B4UConference_machine learning_deeplearning
B4UConference_machine learning_deeplearningB4UConference_machine learning_deeplearning
B4UConference_machine learning_deeplearningHoa Le
 
Deep Learning: concepts and use cases (October 2018)
Deep Learning: concepts and use cases (October 2018)Deep Learning: concepts and use cases (October 2018)
Deep Learning: concepts and use cases (October 2018)Julien SIMON
 
Database Research Principles Revealed
Database Research Principles RevealedDatabase Research Principles Revealed
Database Research Principles Revealedinfoblog
 
Andrew NG machine learning
Andrew NG machine learningAndrew NG machine learning
Andrew NG machine learningShareDocView.com
 

Similar to Finding needles in haystacks with deep neural networks (20)

Designing a neural network architecture for image recognition
Designing a neural network architecture for image recognitionDesigning a neural network architecture for image recognition
Designing a neural network architecture for image recognition
 
Sippin: A Mobile Application Case Study presented at Techfest Louisville
Sippin: A Mobile Application Case Study presented at Techfest LouisvilleSippin: A Mobile Application Case Study presented at Techfest Louisville
Sippin: A Mobile Application Case Study presented at Techfest Louisville
 
Barga Data Science lecture 4
Barga Data Science lecture 4Barga Data Science lecture 4
Barga Data Science lecture 4
 
Deep Learning for Computer Vision - PyconDE 2017
Deep Learning for Computer Vision - PyconDE 2017Deep Learning for Computer Vision - PyconDE 2017
Deep Learning for Computer Vision - PyconDE 2017
 
Machine Learning, Deep Learning and Data Analysis Introduction
Machine Learning, Deep Learning and Data Analysis IntroductionMachine Learning, Deep Learning and Data Analysis Introduction
Machine Learning, Deep Learning and Data Analysis Introduction
 
Machine learning in the life sciences with knime
Machine learning in the life sciences with knimeMachine learning in the life sciences with knime
Machine learning in the life sciences with knime
 
Neural Networks in Data Mining - “An Overview”
Neural Networks  in Data Mining -   “An Overview”Neural Networks  in Data Mining -   “An Overview”
Neural Networks in Data Mining - “An Overview”
 
Report face recognition : ArganRecogn
Report face recognition :  ArganRecognReport face recognition :  ArganRecogn
Report face recognition : ArganRecogn
 
Machine learning and vulnerabilities
Machine learning and vulnerabilitiesMachine learning and vulnerabilities
Machine learning and vulnerabilities
 
Troubleshooting Deep Neural Networks - Full Stack Deep Learning
Troubleshooting Deep Neural Networks - Full Stack Deep LearningTroubleshooting Deep Neural Networks - Full Stack Deep Learning
Troubleshooting Deep Neural Networks - Full Stack Deep Learning
 
Semi-Supervised Insight Generation from Petabyte Scale Text Data
Semi-Supervised Insight Generation from Petabyte Scale Text DataSemi-Supervised Insight Generation from Petabyte Scale Text Data
Semi-Supervised Insight Generation from Petabyte Scale Text Data
 
Barga Data Science lecture 9
Barga Data Science lecture 9Barga Data Science lecture 9
Barga Data Science lecture 9
 
Deep learning
Deep learningDeep learning
Deep learning
 
Deep Learning for Computer Vision - ExecutiveML
Deep Learning for Computer Vision - ExecutiveMLDeep Learning for Computer Vision - ExecutiveML
Deep Learning for Computer Vision - ExecutiveML
 
Python for Image Understanding: Deep Learning with Convolutional Neural Nets
Python for Image Understanding: Deep Learning with Convolutional Neural NetsPython for Image Understanding: Deep Learning with Convolutional Neural Nets
Python for Image Understanding: Deep Learning with Convolutional Neural Nets
 
B4UConference_machine learning_deeplearning
B4UConference_machine learning_deeplearningB4UConference_machine learning_deeplearning
B4UConference_machine learning_deeplearning
 
Deep Learning: concepts and use cases (October 2018)
Deep Learning: concepts and use cases (October 2018)Deep Learning: concepts and use cases (October 2018)
Deep Learning: concepts and use cases (October 2018)
 
Database Research Principles Revealed
Database Research Principles RevealedDatabase Research Principles Revealed
Database Research Principles Revealed
 
Andrew NG machine learning
Andrew NG machine learningAndrew NG machine learning
Andrew NG machine learning
 
Blinkdb
BlinkdbBlinkdb
Blinkdb
 

Recently uploaded

IIBA Adl - Being Effective on Day 1 - Slide Deck.pdf
IIBA Adl - Being Effective on Day 1 - Slide Deck.pdfIIBA Adl - Being Effective on Day 1 - Slide Deck.pdf
IIBA Adl - Being Effective on Day 1 - Slide Deck.pdfAustraliaChapterIIBA
 
Big Data - large Scale data (Amazon, FB)
Big Data - large Scale data (Amazon, FB)Big Data - large Scale data (Amazon, FB)
Big Data - large Scale data (Amazon, FB)CUO VEERANAN VEERANAN
 
PredictuVu ProposalV1.pptx
PredictuVu ProposalV1.pptxPredictuVu ProposalV1.pptx
PredictuVu ProposalV1.pptxKapilSinghal47
 
chatgpt-prompts (1).pdf
chatgpt-prompts (1).pdfchatgpt-prompts (1).pdf
chatgpt-prompts (1).pdfMuntherMurjan1
 
SABARI PRIYAN's self introduction as a reference
SABARI PRIYAN's self introduction as a referenceSABARI PRIYAN's self introduction as a reference
SABARI PRIYAN's self introduction as a referencepriyansabari355
 
Generative AI Rennes Meetup with OVHcloud - WAICF highlights & how to deploy ...
Generative AI Rennes Meetup with OVHcloud - WAICF highlights & how to deploy ...Generative AI Rennes Meetup with OVHcloud - WAICF highlights & how to deploy ...
Generative AI Rennes Meetup with OVHcloud - WAICF highlights & how to deploy ...Thibaud Le Douarin
 
Morris H. DeGroot, Mark J. Schervish - Probability and Statistics (4th Editio...
Morris H. DeGroot, Mark J. Schervish - Probability and Statistics (4th Editio...Morris H. DeGroot, Mark J. Schervish - Probability and Statistics (4th Editio...
Morris H. DeGroot, Mark J. Schervish - Probability and Statistics (4th Editio...AkbarHidayatullah11
 
SABARI PRIYAN's self introduction as reference
SABARI PRIYAN's self introduction as referenceSABARI PRIYAN's self introduction as reference
SABARI PRIYAN's self introduction as referencepriyansabari355
 
[IRTalks@The University of Glasgow] A Topology-aware Analysis of Graph Collab...
[IRTalks@The University of Glasgow] A Topology-aware Analysis of Graph Collab...[IRTalks@The University of Glasgow] A Topology-aware Analysis of Graph Collab...
[IRTalks@The University of Glasgow] A Topology-aware Analysis of Graph Collab...Daniele Malitesta
 
Soil Health Policy Map Years 2020 to 2023
Soil Health Policy Map Years 2020 to 2023Soil Health Policy Map Years 2020 to 2023
Soil Health Policy Map Years 2020 to 2023stephizcoolio
 
Web 3.0 in Data Privacy and Security | Data Privacy |Blockchain Security| Cyb...
Web 3.0 in Data Privacy and Security | Data Privacy |Blockchain Security| Cyb...Web 3.0 in Data Privacy and Security | Data Privacy |Blockchain Security| Cyb...
Web 3.0 in Data Privacy and Security | Data Privacy |Blockchain Security| Cyb...Cyber Security Experts
 
AWS Identity and access management for users
AWS Identity and access management for usersAWS Identity and access management for users
AWS Identity and access management for usersStephenEfange3
 
Oppotus - Malaysians on Malaysia 4Q 2023.pdf
Oppotus - Malaysians on Malaysia 4Q 2023.pdfOppotus - Malaysians on Malaysia 4Q 2023.pdf
Oppotus - Malaysians on Malaysia 4Q 2023.pdfOppotus
 
data analytics and tools from in2inglobal.pdf
data analytics  and tools from in2inglobal.pdfdata analytics  and tools from in2inglobal.pdf
data analytics and tools from in2inglobal.pdfdigimartfamily
 
Lies and Myths in InfoSec - 2023 Usenix Enigma
Lies and Myths in InfoSec - 2023 Usenix EnigmaLies and Myths in InfoSec - 2023 Usenix Enigma
Lies and Myths in InfoSec - 2023 Usenix EnigmaAdrian Sanabria
 
Artificial Intelligence and its Impact on Society.pptx
Artificial Intelligence and its Impact on Society.pptxArtificial Intelligence and its Impact on Society.pptx
Artificial Intelligence and its Impact on Society.pptxVighnesh Shashtri
 
Industry 4.0 in IoT Transforming the Future.pptx
Industry 4.0 in IoT Transforming the Future.pptxIndustry 4.0 in IoT Transforming the Future.pptx
Industry 4.0 in IoT Transforming the Future.pptxMdRafiqulIslam403212
 

Recently uploaded (18)

IIBA Adl - Being Effective on Day 1 - Slide Deck.pdf
IIBA Adl - Being Effective on Day 1 - Slide Deck.pdfIIBA Adl - Being Effective on Day 1 - Slide Deck.pdf
IIBA Adl - Being Effective on Day 1 - Slide Deck.pdf
 
Big Data - large Scale data (Amazon, FB)
Big Data - large Scale data (Amazon, FB)Big Data - large Scale data (Amazon, FB)
Big Data - large Scale data (Amazon, FB)
 
2.pptx
2.pptx2.pptx
2.pptx
 
PredictuVu ProposalV1.pptx
PredictuVu ProposalV1.pptxPredictuVu ProposalV1.pptx
PredictuVu ProposalV1.pptx
 
chatgpt-prompts (1).pdf
chatgpt-prompts (1).pdfchatgpt-prompts (1).pdf
chatgpt-prompts (1).pdf
 
SABARI PRIYAN's self introduction as a reference
SABARI PRIYAN's self introduction as a referenceSABARI PRIYAN's self introduction as a reference
SABARI PRIYAN's self introduction as a reference
 
Generative AI Rennes Meetup with OVHcloud - WAICF highlights & how to deploy ...
Generative AI Rennes Meetup with OVHcloud - WAICF highlights & how to deploy ...Generative AI Rennes Meetup with OVHcloud - WAICF highlights & how to deploy ...
Generative AI Rennes Meetup with OVHcloud - WAICF highlights & how to deploy ...
 
Morris H. DeGroot, Mark J. Schervish - Probability and Statistics (4th Editio...
Morris H. DeGroot, Mark J. Schervish - Probability and Statistics (4th Editio...Morris H. DeGroot, Mark J. Schervish - Probability and Statistics (4th Editio...
Morris H. DeGroot, Mark J. Schervish - Probability and Statistics (4th Editio...
 
SABARI PRIYAN's self introduction as reference
SABARI PRIYAN's self introduction as referenceSABARI PRIYAN's self introduction as reference
SABARI PRIYAN's self introduction as reference
 
[IRTalks@The University of Glasgow] A Topology-aware Analysis of Graph Collab...
[IRTalks@The University of Glasgow] A Topology-aware Analysis of Graph Collab...[IRTalks@The University of Glasgow] A Topology-aware Analysis of Graph Collab...
[IRTalks@The University of Glasgow] A Topology-aware Analysis of Graph Collab...
 
Soil Health Policy Map Years 2020 to 2023
Soil Health Policy Map Years 2020 to 2023Soil Health Policy Map Years 2020 to 2023
Soil Health Policy Map Years 2020 to 2023
 
Web 3.0 in Data Privacy and Security | Data Privacy |Blockchain Security| Cyb...
Web 3.0 in Data Privacy and Security | Data Privacy |Blockchain Security| Cyb...Web 3.0 in Data Privacy and Security | Data Privacy |Blockchain Security| Cyb...
Web 3.0 in Data Privacy and Security | Data Privacy |Blockchain Security| Cyb...
 
AWS Identity and access management for users
AWS Identity and access management for usersAWS Identity and access management for users
AWS Identity and access management for users
 
Oppotus - Malaysians on Malaysia 4Q 2023.pdf
Oppotus - Malaysians on Malaysia 4Q 2023.pdfOppotus - Malaysians on Malaysia 4Q 2023.pdf
Oppotus - Malaysians on Malaysia 4Q 2023.pdf
 
data analytics and tools from in2inglobal.pdf
data analytics  and tools from in2inglobal.pdfdata analytics  and tools from in2inglobal.pdf
data analytics and tools from in2inglobal.pdf
 
Lies and Myths in InfoSec - 2023 Usenix Enigma
Lies and Myths in InfoSec - 2023 Usenix EnigmaLies and Myths in InfoSec - 2023 Usenix Enigma
Lies and Myths in InfoSec - 2023 Usenix Enigma
 
Artificial Intelligence and its Impact on Society.pptx
Artificial Intelligence and its Impact on Society.pptxArtificial Intelligence and its Impact on Society.pptx
Artificial Intelligence and its Impact on Society.pptx
 
Industry 4.0 in IoT Transforming the Future.pptx
Industry 4.0 in IoT Transforming the Future.pptxIndustry 4.0 in IoT Transforming the Future.pptx
Industry 4.0 in IoT Transforming the Future.pptx
 

Finding needles in haystacks with deep neural networks