SlideShare a Scribd company logo
2
“The question of whether a computer can think is no more
interesting than the question of whether a submarine can swim.”
- Edsger W. Dijkstra
4
5
6
7
8
9
11
1. Define problem
2. Prepare data
a. Collection
b. Cleaning
c. Transformation
d. Feature Engineering
3. Choose learning algorithm
4. Train candidate model
5. Evaluate performance
6. Improve results
7. Deploy chosen model
8. Feedback loop
12
1. Define problem (object classification: pixels -> object class)
2. Prepare data
a. Collection (web scraping)
b. Cleaning (remove images missing labels)
c. Transformation (reduce resolution)
d. Feature Engineering (normalize pixel values)
3. Choose learning algorithm (convolutional neural network)
4. Train candidate model (gradient descent)
5. Evaluate performance (loss/accuracy)
6. Improve results (hyperparameter optimization)
7. Deploy chosen model (Google Cloud Platform)
8. Feedback loop (keep prototyping with alternative variations of the model)
13
15
16
17
18
19
20
21
23
24
25

More Related Content

Similar to Towards Machine Intelligence

(CMP305) Deep Learning on AWS Made EasyCmp305
(CMP305) Deep Learning on AWS Made EasyCmp305(CMP305) Deep Learning on AWS Made EasyCmp305
(CMP305) Deep Learning on AWS Made EasyCmp305
Amazon Web Services
 
Azure machine learning service
Azure machine learning serviceAzure machine learning service
Azure machine learning service
Ruth Yakubu
 
Thesis Defense (Gwendal DANIEL) - Nov 2017
Thesis Defense (Gwendal DANIEL) - Nov 2017Thesis Defense (Gwendal DANIEL) - Nov 2017
Thesis Defense (Gwendal DANIEL) - Nov 2017
Gwendal Daniel
 
Unsupervised Aspect Based Sentiment Analysis at Scale
Unsupervised Aspect Based Sentiment Analysis at ScaleUnsupervised Aspect Based Sentiment Analysis at Scale
Unsupervised Aspect Based Sentiment Analysis at Scale
Aaron (Ari) Bornstein
 
Gpudigital lab for english partners
Gpudigital lab for english partnersGpudigital lab for english partners
Gpudigital lab for english partners
Oleg Gubanov
 
Informs 2019 - Flexible Network Design Utilizing Non Strict Modeling Approaches
Informs 2019  - Flexible Network Design Utilizing Non Strict Modeling ApproachesInforms 2019  - Flexible Network Design Utilizing Non Strict Modeling Approaches
Informs 2019 - Flexible Network Design Utilizing Non Strict Modeling Approaches
Fabion Kauker
 
Android Malware 2020 (CCCS-CIC-AndMal-2020)
Android Malware 2020 (CCCS-CIC-AndMal-2020)Android Malware 2020 (CCCS-CIC-AndMal-2020)
Android Malware 2020 (CCCS-CIC-AndMal-2020)
Indraneel Dabhade
 
Viktor Tsykunov: Azure Machine Learning Service
Viktor Tsykunov: Azure Machine Learning ServiceViktor Tsykunov: Azure Machine Learning Service
Viktor Tsykunov: Azure Machine Learning Service
Lviv Startup Club
 
Data Science Challenge presentation given to the CinBITools Meetup Group
Data Science Challenge presentation given to the CinBITools Meetup GroupData Science Challenge presentation given to the CinBITools Meetup Group
Data Science Challenge presentation given to the CinBITools Meetup Group
Doug Needham
 
Cloudera Data Science Challenge
Cloudera Data Science ChallengeCloudera Data Science Challenge
Cloudera Data Science Challenge
Mark Nichols, P.E.
 
HP - Jerome Rolia - Hadoop World 2010
HP - Jerome Rolia - Hadoop World 2010HP - Jerome Rolia - Hadoop World 2010
HP - Jerome Rolia - Hadoop World 2010
Cloudera, Inc.
 
StackNet Meta-Modelling framework
StackNet Meta-Modelling frameworkStackNet Meta-Modelling framework
StackNet Meta-Modelling framework
Sri Ambati
 
Data herding
Data herdingData herding
Data herding
unbracketed
 
Data herding
Data herdingData herding
Data herding
unbracketed
 
Large-scale Recommendation Systems on Just a PC
Large-scale Recommendation Systems on Just a PCLarge-scale Recommendation Systems on Just a PC
Large-scale Recommendation Systems on Just a PC
Aapo Kyrölä
 
Data Science for Dummies - Data Engineering with Titanic dataset + Databricks...
Data Science for Dummies - Data Engineering with Titanic dataset + Databricks...Data Science for Dummies - Data Engineering with Titanic dataset + Databricks...
Data Science for Dummies - Data Engineering with Titanic dataset + Databricks...
Rodney Joyce
 
Identifying Auxiliary Web Images Using Combinations of Analyses
Identifying Auxiliary Web Images Using Combinations of AnalysesIdentifying Auxiliary Web Images Using Combinations of Analyses
Identifying Auxiliary Web Images Using Combinations of Analyses
Tewson Seeoun
 
Learning Predictive Modeling with TSA and Kaggle
Learning Predictive Modeling with TSA and KaggleLearning Predictive Modeling with TSA and Kaggle
Learning Predictive Modeling with TSA and Kaggle
Yvonne K. Matos
 
Efficient Model Selection for Deep Neural Networks on Massively Parallel Proc...
Efficient Model Selection for Deep Neural Networks on Massively Parallel Proc...Efficient Model Selection for Deep Neural Networks on Massively Parallel Proc...
Efficient Model Selection for Deep Neural Networks on Massively Parallel Proc...
inside-BigData.com
 
Web Performance Part 4 "Client-side performance"
Web Performance Part 4  "Client-side performance"Web Performance Part 4  "Client-side performance"
Web Performance Part 4 "Client-side performance"
Binary Studio
 

Similar to Towards Machine Intelligence (20)

(CMP305) Deep Learning on AWS Made EasyCmp305
(CMP305) Deep Learning on AWS Made EasyCmp305(CMP305) Deep Learning on AWS Made EasyCmp305
(CMP305) Deep Learning on AWS Made EasyCmp305
 
Azure machine learning service
Azure machine learning serviceAzure machine learning service
Azure machine learning service
 
Thesis Defense (Gwendal DANIEL) - Nov 2017
Thesis Defense (Gwendal DANIEL) - Nov 2017Thesis Defense (Gwendal DANIEL) - Nov 2017
Thesis Defense (Gwendal DANIEL) - Nov 2017
 
Unsupervised Aspect Based Sentiment Analysis at Scale
Unsupervised Aspect Based Sentiment Analysis at ScaleUnsupervised Aspect Based Sentiment Analysis at Scale
Unsupervised Aspect Based Sentiment Analysis at Scale
 
Gpudigital lab for english partners
Gpudigital lab for english partnersGpudigital lab for english partners
Gpudigital lab for english partners
 
Informs 2019 - Flexible Network Design Utilizing Non Strict Modeling Approaches
Informs 2019  - Flexible Network Design Utilizing Non Strict Modeling ApproachesInforms 2019  - Flexible Network Design Utilizing Non Strict Modeling Approaches
Informs 2019 - Flexible Network Design Utilizing Non Strict Modeling Approaches
 
Android Malware 2020 (CCCS-CIC-AndMal-2020)
Android Malware 2020 (CCCS-CIC-AndMal-2020)Android Malware 2020 (CCCS-CIC-AndMal-2020)
Android Malware 2020 (CCCS-CIC-AndMal-2020)
 
Viktor Tsykunov: Azure Machine Learning Service
Viktor Tsykunov: Azure Machine Learning ServiceViktor Tsykunov: Azure Machine Learning Service
Viktor Tsykunov: Azure Machine Learning Service
 
Data Science Challenge presentation given to the CinBITools Meetup Group
Data Science Challenge presentation given to the CinBITools Meetup GroupData Science Challenge presentation given to the CinBITools Meetup Group
Data Science Challenge presentation given to the CinBITools Meetup Group
 
Cloudera Data Science Challenge
Cloudera Data Science ChallengeCloudera Data Science Challenge
Cloudera Data Science Challenge
 
HP - Jerome Rolia - Hadoop World 2010
HP - Jerome Rolia - Hadoop World 2010HP - Jerome Rolia - Hadoop World 2010
HP - Jerome Rolia - Hadoop World 2010
 
StackNet Meta-Modelling framework
StackNet Meta-Modelling frameworkStackNet Meta-Modelling framework
StackNet Meta-Modelling framework
 
Data herding
Data herdingData herding
Data herding
 
Data herding
Data herdingData herding
Data herding
 
Large-scale Recommendation Systems on Just a PC
Large-scale Recommendation Systems on Just a PCLarge-scale Recommendation Systems on Just a PC
Large-scale Recommendation Systems on Just a PC
 
Data Science for Dummies - Data Engineering with Titanic dataset + Databricks...
Data Science for Dummies - Data Engineering with Titanic dataset + Databricks...Data Science for Dummies - Data Engineering with Titanic dataset + Databricks...
Data Science for Dummies - Data Engineering with Titanic dataset + Databricks...
 
Identifying Auxiliary Web Images Using Combinations of Analyses
Identifying Auxiliary Web Images Using Combinations of AnalysesIdentifying Auxiliary Web Images Using Combinations of Analyses
Identifying Auxiliary Web Images Using Combinations of Analyses
 
Learning Predictive Modeling with TSA and Kaggle
Learning Predictive Modeling with TSA and KaggleLearning Predictive Modeling with TSA and Kaggle
Learning Predictive Modeling with TSA and Kaggle
 
Efficient Model Selection for Deep Neural Networks on Massively Parallel Proc...
Efficient Model Selection for Deep Neural Networks on Massively Parallel Proc...Efficient Model Selection for Deep Neural Networks on Massively Parallel Proc...
Efficient Model Selection for Deep Neural Networks on Massively Parallel Proc...
 
Web Performance Part 4 "Client-side performance"
Web Performance Part 4  "Client-side performance"Web Performance Part 4  "Client-side performance"
Web Performance Part 4 "Client-side performance"
 

Recently uploaded

Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxOcean lotus Threat actors project by John Sitima 2024 (1).pptx
Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
SitimaJohn
 
Choosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptxChoosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptx
Brandon Minnick, MBA
 
Project Management Semester Long Project - Acuity
Project Management Semester Long Project - AcuityProject Management Semester Long Project - Acuity
Project Management Semester Long Project - Acuity
jpupo2018
 
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUHCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
panagenda
 
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfUnlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Malak Abu Hammad
 
Webinar: Designing a schema for a Data Warehouse
Webinar: Designing a schema for a Data WarehouseWebinar: Designing a schema for a Data Warehouse
Webinar: Designing a schema for a Data Warehouse
Federico Razzoli
 
WeTestAthens: Postman's AI & Automation Techniques
WeTestAthens: Postman's AI & Automation TechniquesWeTestAthens: Postman's AI & Automation Techniques
WeTestAthens: Postman's AI & Automation Techniques
Postman
 
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success StoryDriving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Safe Software
 
GenAI Pilot Implementation in the organizations
GenAI Pilot Implementation in the organizationsGenAI Pilot Implementation in the organizations
GenAI Pilot Implementation in the organizations
kumardaparthi1024
 
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing InstancesEnergy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Alpen-Adria-Universität
 
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
saastr
 
Serial Arm Control in Real Time Presentation
Serial Arm Control in Real Time PresentationSerial Arm Control in Real Time Presentation
Serial Arm Control in Real Time Presentation
tolgahangng
 
Taking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdfTaking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdf
ssuserfac0301
 
Introduction of Cybersecurity with OSS at Code Europe 2024
Introduction of Cybersecurity with OSS  at Code Europe 2024Introduction of Cybersecurity with OSS  at Code Europe 2024
Introduction of Cybersecurity with OSS at Code Europe 2024
Hiroshi SHIBATA
 
TrustArc Webinar - 2024 Global Privacy Survey
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc Webinar - 2024 Global Privacy Survey
TrustArc Webinar - 2024 Global Privacy Survey
TrustArc
 
Artificial Intelligence for XMLDevelopment
Artificial Intelligence for XMLDevelopmentArtificial Intelligence for XMLDevelopment
Artificial Intelligence for XMLDevelopment
Octavian Nadolu
 
June Patch Tuesday
June Patch TuesdayJune Patch Tuesday
June Patch Tuesday
Ivanti
 
みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...
みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...
みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...
名前 です男
 
5th LF Energy Power Grid Model Meet-up Slides
5th LF Energy Power Grid Model Meet-up Slides5th LF Energy Power Grid Model Meet-up Slides
5th LF Energy Power Grid Model Meet-up Slides
DanBrown980551
 
How to Get CNIC Information System with Paksim Ga.pptx
How to Get CNIC Information System with Paksim Ga.pptxHow to Get CNIC Information System with Paksim Ga.pptx
How to Get CNIC Information System with Paksim Ga.pptx
danishmna97
 

Recently uploaded (20)

Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxOcean lotus Threat actors project by John Sitima 2024 (1).pptx
Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
 
Choosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptxChoosing The Best AWS Service For Your Website + API.pptx
Choosing The Best AWS Service For Your Website + API.pptx
 
Project Management Semester Long Project - Acuity
Project Management Semester Long Project - AcuityProject Management Semester Long Project - Acuity
Project Management Semester Long Project - Acuity
 
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUHCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
 
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfUnlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
 
Webinar: Designing a schema for a Data Warehouse
Webinar: Designing a schema for a Data WarehouseWebinar: Designing a schema for a Data Warehouse
Webinar: Designing a schema for a Data Warehouse
 
WeTestAthens: Postman's AI & Automation Techniques
WeTestAthens: Postman's AI & Automation TechniquesWeTestAthens: Postman's AI & Automation Techniques
WeTestAthens: Postman's AI & Automation Techniques
 
Driving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success StoryDriving Business Innovation: Latest Generative AI Advancements & Success Story
Driving Business Innovation: Latest Generative AI Advancements & Success Story
 
GenAI Pilot Implementation in the organizations
GenAI Pilot Implementation in the organizationsGenAI Pilot Implementation in the organizations
GenAI Pilot Implementation in the organizations
 
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing InstancesEnergy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
 
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
 
Serial Arm Control in Real Time Presentation
Serial Arm Control in Real Time PresentationSerial Arm Control in Real Time Presentation
Serial Arm Control in Real Time Presentation
 
Taking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdfTaking AI to the Next Level in Manufacturing.pdf
Taking AI to the Next Level in Manufacturing.pdf
 
Introduction of Cybersecurity with OSS at Code Europe 2024
Introduction of Cybersecurity with OSS  at Code Europe 2024Introduction of Cybersecurity with OSS  at Code Europe 2024
Introduction of Cybersecurity with OSS at Code Europe 2024
 
TrustArc Webinar - 2024 Global Privacy Survey
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc Webinar - 2024 Global Privacy Survey
TrustArc Webinar - 2024 Global Privacy Survey
 
Artificial Intelligence for XMLDevelopment
Artificial Intelligence for XMLDevelopmentArtificial Intelligence for XMLDevelopment
Artificial Intelligence for XMLDevelopment
 
June Patch Tuesday
June Patch TuesdayJune Patch Tuesday
June Patch Tuesday
 
みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...
みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...
みなさんこんにちはこれ何文字まで入るの?40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの?えこ...
 
5th LF Energy Power Grid Model Meet-up Slides
5th LF Energy Power Grid Model Meet-up Slides5th LF Energy Power Grid Model Meet-up Slides
5th LF Energy Power Grid Model Meet-up Slides
 
How to Get CNIC Information System with Paksim Ga.pptx
How to Get CNIC Information System with Paksim Ga.pptxHow to Get CNIC Information System with Paksim Ga.pptx
How to Get CNIC Information System with Paksim Ga.pptx
 

Towards Machine Intelligence

  • 1.
  • 2. 2
  • 3.
  • 4. “The question of whether a computer can think is no more interesting than the question of whether a submarine can swim.” - Edsger W. Dijkstra 4
  • 5. 5
  • 6. 6
  • 7. 7
  • 8. 8
  • 9. 9
  • 10.
  • 11. 11
  • 12. 1. Define problem 2. Prepare data a. Collection b. Cleaning c. Transformation d. Feature Engineering 3. Choose learning algorithm 4. Train candidate model 5. Evaluate performance 6. Improve results 7. Deploy chosen model 8. Feedback loop 12
  • 13. 1. Define problem (object classification: pixels -> object class) 2. Prepare data a. Collection (web scraping) b. Cleaning (remove images missing labels) c. Transformation (reduce resolution) d. Feature Engineering (normalize pixel values) 3. Choose learning algorithm (convolutional neural network) 4. Train candidate model (gradient descent) 5. Evaluate performance (loss/accuracy) 6. Improve results (hyperparameter optimization) 7. Deploy chosen model (Google Cloud Platform) 8. Feedback loop (keep prototyping with alternative variations of the model) 13
  • 14.
  • 15. 15
  • 16. 16
  • 17. 17
  • 18. 18
  • 19. 19
  • 20. 20
  • 21. 21
  • 22.
  • 23. 23
  • 24. 24
  • 25. 25