Index of posts

Giga thoughts has been approved and included by ScienceSeeker.org

TEDx Talks
1. My TEDx talk on ” The Internet of Things”

Publications (IP.com)
1. Architecting a cloud based IP Multimedia System (IMS)
2. Designing a Social Web Portal
3.  A method for optimal bandwidth usage by auctioning available bandwidth using the OpenFlow Protocol

Quantum computing
1.Venturing into IBM’s Quantum Experience!
2. Going deeper into IBM’s Quantum Experience!
3. A primer on Qubits, Quantum gates and Quantum Operations
4. Exploring Quantum Gate operations with QCSimulator
5. Introducing QCSimulator: A 5-qubit quantum computing simulator in R

Computer Vision – OpenCV
1. Re-working the Lucy-Richardson Algorithm in OpenCV
2. Deblurring with OpenCV: Wiener filter reloaded
3. Dabbling with Wiener filter using OpenCV
4.Experiments with deblurring using OpenCV
5. De-blurring revisited with Wiener filter using OpenCV
6. Installing and using OpenCV with Visual Studio 2010 express
7. Computer Vision: Ramblings on derivatives, contours and histograms
8. Hand-detection through haar training : A hands-on approach
9. OpenCV: Haartraing and all that jazz!
10. Computer Vision: Getting started with OpenCV
11. OpenCV: Fun with filters and convolution
12. Assistive technology and OpenCV
13. Computer Vision: Ramblings on derivatives, histograms and contours

Neural Networks & Deep Learning
1. Neural Networks: On Perceptrons and Sigmoid Neurons
2. Neural Networks: The mechanics of backpropagation

My ideas
1. Architecting a cloud based IP Multimedia System (IMS)
2. Designing a Social Web Portal
3.  A method for optimal bandwidth usage by auctioning available bandwidth using the OpenFlow Protocol
4. Maximizing enterprise bandwidth by sharing global enterprise-wide network resources
5.  Envisioning a Software Defined IP Multimedia System
6.  A method to crowd source pothole marking on (Indian) roads

Thinking Web Scale
1. Reducing to the Map-Reduce paradigm- Thinking Web Scale – Part 1
2. Perils & pitfalls of Big Data
3. Pregelian philosophy: Thinking Web Scale – Part 2
4. Natural selection of database technology through the years
5. Thinking Web Scale (TWS-3): Map-Reduce – Bring compute to data
6. TWS-4: Gossip protocol: Epidemics and rumors to the rescue
7.TWS-5: Google’s Page Rank : Predicting the movements of a random web walker

Cloud Computing
1. Profiting from a cloud deployment
2. To Hadoop or not to Hadoop
3. The Future is C-cubed
4. Designing a scalable architecture for the cloud
5. Working with Amazon EBS, ELB and Route 53
6. Latency, throughput implications for the cloud
7. Cloud computing: Show me the money!
8. Managing multi-region deployments
9. Design principles of scalable, distributed systems
10. The many faces of latency
11. Getting started with memcached-libmemcached
12. The business of cloud computing
13. Designing for cloud worthiness
14. Optimal Cloud Computing
15. Scaling out
16. Cloud Computing – Design Consideration
17. Latency, throughput implications for the cloud
18.  Getting started with memcached – libmemcached
19. Cloud Computing – Show me the money!
20. The Business of Cloud Computing
21. Architecting a cloud based IP Multimedia System (IMS)
22. Envisioning a Software Defined IP Multimedia System
23. Future calls. Visualizing a IMS based future
24. Dissecting the Cloud – Part 1
25. Dissecting the cloud – Part 2
26. Introducing the Software Defined Computing Pattern
27. Where is the Cloud Computing bus going?

IBM’s Bluemix (Cloud Computing continued …)
1. Get your feet wet with IBM’s Bluemix
2. Getting started with Mobile Cloud with Bluemix
3. Test driving Push notification in Bluemix
4. Mixing Twilio with IBM Bluemix
5. Brewing a potion with Bluemix, PostgreSQL & Node.js in the cloud
6. A Bluemix recipe with MongoDB and Node.js
7. Spicing up IBM Bluemix with MongoDB and NodeExpress
8. A Cloud Medley with IBM’s Bluemix, Cloudant and Node.js
9. Rock N’ Roll with Bluemix, Cloudant & NodeExpress
10. Revisiting Bluemix with Twilio
11.  What’s up Watson? Using IBM Watson’s QAAPI with Bluemix, NodeExpress – Part 1
12. Revisiting Whats up, Watson – Using Watson’s Question and Answer service – Part 2
13.  Bend it like Bluemix, MongoDB with autoscaling – Part 1
14.  Bend it like Bluemix, MongoDB with autoscaling – Part 2
15. Bend it like Bluemix, MongoDB with autoscaling – Part 3

Essays
1. The mind of the programmer
2. .Introducing the Software Defined Computing Pattern
3. The story of virtualization
4. Programming languages in layman’s language
5. How to program – Some essential tips
6. Programming Zen and now – Some essential tips -2 
7. The common alphabet of programming languages

Future Scape
1. Towards an auction-based internet
2. The next frontier
3. Re-imagining the Web portal
4. The case for a cloud based IMS deployment
5. Adding the OpenFlow variable to the IMS equation
6. The emergence of Social Software as a Service
7. The data center paradox
8. Technology hurdles: 2012 and beyond
9. Technologies to watch: 2012 and beyond
10. The future is C-cubed
11. Software Defined Networks – A glimpse of tomorrow
12. Smart Grids – Heralding a smart future
13. When NoSQL makes better sense than MySQL
14. The moving edge of computing
15. The dark side of the Internet
16. A method for optimal bandwidth usage by auctioning available bandwidth using the OpenFlow protocol
17. Designing a Social Web Portal
18. Maximizing enterprise bandwidth by sharing global enterprise-wide network resources
19. Envisioning a Software Defined IP Multimedia System
20. Future calls. Visualizing a IMS based future
21. ‘The Search’ is not over yet!
22. Close encounters with the future
23. Unraveling the mysteries of life
24. A method to crowd source pothole marking on (Indian) roads
25. The thing about the Internet of Things
26. The brave, new frontiers of computing
27. Where is the Cloud Computing bus going?

Science Fiction – Short Story
1. Singularity
2. The Anomaly
3. Sea-shells on the  seashore

Distributed Systems
1.Eliminating the performance drag
2. To Hadoop, or not to Hadoop
3.Design principles of scalable, distributed systems
4.Designing a scalable architecture for the cloud
5. Cache-22

Android
1. Toying with Android apps
2. Unity – My first android app
3. Unity (full) android app – With bells and whistles
4. Android soup with SQLiteDatabase, ListActivty and Options Menu
5. Train Spotting android app – Nuts & bolts
6. Getting started with AndEngine
7. Creating a simple game using AndEngine
8. The making of Dino Pong android game
9. Simulating the domino effect using Box2D and AndEngine
10. Bull in a china shop – Behind the scenes in android
11. Creating a blob in Android using  Box2D physics Engine & AndEngine
12. Blob with an attitude(stiffness) in Android
13. The making of Total Control Android game
14. Simulating an Edge Shape in Android
15. Simulating a Web Joint in Android
16. Modeling a Car in Android
17. Fun simulation of a Chain in Android
18. Simulating an oscillating revoluteJoint in Android
19. “Is it animal? Is it an insect?” in Android
20. A closer look at “Robot horse on a Trot! in Android”

AndEngine & Box2D simulations
1. Simulating the domino effect using Box2D & AndEngine
2. Bull in a china shop
3. Creating a blob in Android
4. Creating a blob with an attitude (stiffness) in Android
5. Simulating an Edge Shape in Android
6. Simulating a Web Joint in Android
7. Modeling a Car in Android
8. Fun simulation of a Chain in Android
9. Simulating an oscillating revoluteJoint in Android
10. “Is it animal? Is it an insect?” in Android
11.  A closer look at “Robot horse on a Trot! in Android”

Node.js
1. Getting started with Node.js & MongoDB
2. Working with Node.js & PostgreSQL
3. Elements of CRUD with NodeExpress and MongoDB using Enide Studio

Applied Machine Learning
1. Informed choices through Machine Learning : Analyzing Kohli, Tendulkar and Dravid
2. Informed choices through Machine Learning-2: Pitting together Kumble, Kapil, Chandra
3. Applying the principles of Machine Learning
4. An Octave primer

Machine Learning
1.Simplifying Machine Learning Algorithms – Part 1
2. Simplifying ML: Logistic Regression – Part 2
3. Simplifying ML: Neural Networks – Part 3
4. Simplifying Machine Learning: Bias, Variance, regularization and odd facts – Part 4
5. Simplifying ML: Impact of degree of polynomial degree on bias & variance and other insights
6. Simplifying Machine Learning – K- Means clusters – Part 6
7. Simplifying ML: Recommender Systems – Part 7

Data Science
1.  IBM Data Science Experience: First experiences with yorkr

R language
1.  The language R
2. To R is human …
3.  Divining Twitterverse with R
4. Statistical learning with R: A look at literacy in Tamil Nadu
5. A peek into literacy in India:Statistical learning with R
6. A crime map of India in R: Crimes against women
7. Analyzing cricket’s batting legends – Through the mirage with R
8. Masters of spin: Unraveling the web with R
9. Mirror,mirror …best batsman of them all
10. Introducing cricketr!: An R package for analyzing performances of cricketers
11. Taking cricketr for a spin – Part 1
12. cricketr digs the Ashes
13. cricketr plays the ODIs
14. cricketr adapts to the Twenty20 International
15. Natural Processing Language : What would Shakespeare say?
16: Revisiting crimes against women in India
17. Sixer – R package cricketr’s new Shiny app
18. A short video tutorial on my R package cricketr
19. A video tutorial on R programming – The  essentials
20. Literacy in India – A deepR dive
21. The making of cricker package yorkr – Part 1
22. The making of cricket package yorkr – Part 2
23. The making of cricket package yorkr – Part 3
24. Introducing cricket package yorkr:Part 1- Beaten by sheer pace!
25.  Introducing cricket package yorkr:Part 2- Trapped leg before wicket!
26.  Introducing cricket package yorkr:Part 3- foxed by flight!
27. Introducing cricket package yorkr:Part 4-In the block hole!
28. yorkr pads up for the Twenty20s: Part 1- Analyzing team”s match performance
29. yorkr pads up for the Twenty20s: Part 2-Head to head confrontation between teams
30. yorkr pads up for the Twenty20s:Part 3:Overall team performance against all oppositions!
31. yorkr pads up for Twenty20s:Part 4- Individual batting and bowling performances!
32. yorkr crashes the IPL party ! – Part 1
33. yorkr crashes the IPL party! – Part 2
34. yorkr crashes the IPL party! – Part 3
35. yorkr crashes the IPL party! – Part 4
36. yorkr ranks IPL batsmen and bowlers
37. yorkr ranks T20 batsmen and bowlers
38. yorkr ranks ODI batsmen and bowlers
39. yorkr is generic!
40. Beaten by sheer pace – Cricket analytics with yorkr
41. Beaten by sheer pace! Cricket analytics with yorkr in paperback and Kindle versions
42. Re-introducing cricketr! : An R package to analyze performances of cricketers
43. IBM Data Science Experience: First experiences with yorkr
44. cricketr sizes up legendary all rounders of yesteryear
45. Analyzing World Bank data with WDI, googleVis Motion Charts
46. yorkr ranks IPL Players post 2016 season
47. Googly: An interactive app for analyzing IPL players, matches and teams using R package yorkr
48. GooglyPlus: yorkr analyzes IPL players, teams, matches with plots and tables
49. Inswinger: yorkr swings into International T20s
50. cricketr and yorkr books – Paperback now in Amazon
51. Analysis of International T20 matches with yorkr templates
52. Analysis of IPL T20 matches with yorkr templates
53. cricketr flexes new muscles: The final analysis
54. My 3 video presentations on “Essential R

My Shiny apps
1.  Predict Next Word
2. What would Shakespeare say?
3. Crimes against women in India
4. Literacy in India
5. Sixer

Telecom
1. Tomorrow’s wireless ecosystem
2. The case for a cloud based IMS deployment
3. Adding the OpenFlow variable to the IMS equation
4. Mobile smartphones: The new swiss knife
5. The future of telecom
6. Cloud,analytics key tools for today’s telcos
7. Monetizing mobile data traffic
8. Walking the 3G tightrope in India
9. The evolutionary road for the Indian telecom network
10. The Future is C-cubed
11. Spectrum: The big crunch is coming
12. Accelerating growth through m-banking & m-health
13. Architecting a cloud based IP Multimedia System (IMS)
14. Envisioning a Software Defined IP Multimedia System
15. Future calls. Visualizing a IMS based future
16. Presentation on “Intelligent Networks, CAMEL protocol, services & applications”
17. Presentation on ‘Evolution to LTE’
18. Presentation on Wireless Technologies – Part 1
19. Presentation on Wireless Technologies – Part 2

Technology roundup
1.Stacks of protocol stacks
2.The promise of predictive analytics
3.Big Data -Getting bigger
4.The story of virtualization
5.The future of programming languages
6.Programming languages in layman’s language
7. The rise of analytics
8. The Internet of Things
9. Stacks of protocol stacks – A primer
10. Roundup of Web technologies
11. Natural selection of database technology through the years
12.The language R
13. C language – The code of God

Technology 
1. Experiences with VMWare Workstation 8.0.3: The good, bad and the ugly
2. Installing and configuring dual boot Fedora 16 with Windows XP using a bootable USB
3. The Science of Innovation
4. Ramblings on Lisp
5. A Github primer
6. The computer is not a dumb machine!

Lisp & AI
1. Taking baby steps in Lisp
2. Working with binary trees in Lisp

Hadoop
1 Test driving Apache Hadoop: Standalone & pseudo-distributed mode

Presentations
1. My presentation  on the “Internet of Things” at TEDxBNMIT
2.Presentation on the “Design principles of scalable, distributed systems”
3. Presentation on “Intelligent Networks, CAMEL protocol, services & applications”
4. Presentation on ‘Evolution to LTE’
5. Presentation on Wireless Technologies – Part 1
6. Presentation on Wireless Technologies – Part 2
7. Video presentation on Machine Learning, Data Science, NLP and Big Data – Part 1
8. Video presentation on Machine Learning, Data Science, NLP and Big Data – Part 2
9. Video presentation on Machine Learning, Data Science, NLP and Big Data – Part 3
10. Video presentation on Machine Learning, Data Science, NLP and Big Data – Part 4

Windows Resource Management
1. Stir fry a VBA application quickly
2. Windows Resource Management :Technology choices
3.Building a respectable VBA with Excel application
4. Get your feet wet with Powershell GUI
5. Powershell GUI – Adding bells and whistles
6. Slicing and dicing with LogParser & VBA
7. Adventures in LogParser, HTA and charts.

Cricket & R
1. Informed choices through Machine Learning : Analyzing Kohli, Tendulkar and Dravid
2. Informed choices through Machine Learning-2: Pitting together Kumble, Kapil, Chandra
3. Analyzing cricket’s batting legends – Through the mirage with R
4. Masters of spin: Unraveling the web with R
5. Mirror,mirror …best batsman of them all
6. Introducing cricketr!: An R package for analyzing performances of cricketers
7. Taking cricketr for a spin – Part 1
8. cricketr digs the Ashes
9. cricketr plays the ODIs
10. cricketr adapts to the Twenty20 International
11. Sixer – R package cricketr’s new Shiny app
12. A short video tutorial on my R package cricketr
13. A video tutorial on R programming – The  essentials
14. Cricket analytics with cricketr!!!
15. Cricket analytics with cricketr in paperback and kindle versions
16. The making of cricker package yorkr – Part 1
17. The making of cricket package yorkr – Part 2
18. The making of cricket package yorkr – Part 3
19. Introducing cricket package yorkr:Part 1- Beaten by sheer pace!
20.  Introducing cricket package yorkr:Part 2- Trapped leg before wicket!
21.  Introducing cricket package yorkr:Part 3- foxed by flight!
22. Introducing cricket package yorkr:Part 4-In the block hole!
23. yorkr pads up for the Twenty20s: Part 1- Analyzing team”s match performance
24. yorkr pads up for the Twenty20s: Part 2-Head to head confrontation between teams
25. yorkr pads up for the Twenty20s:Part 3:Overall team performance against all oppositions!
26. yorkr pads up for Twenty20s:Part 4- Individual batting and bowling performances!
27. yorkr crashes the IPL party ! – Part 1
28. yorkr crashes the IPL party! – Part 2
29. yorkr crashes the IPL party ! – Part 1
30. yorkr crashes the IPL party! – Part 2
31. yorkr crashes the IPL party! – Part 3
32. yorkr crashes the IPL party! – Part 4
33. yorkr ranks IPL batsmen and bowlers
34. yorkr ranks T20 batsmen and bowlers
35. yorkr ranks ODI batsmen and bowlers
36. yorkr is generic!
37. Beaten by sheer pace – Cricket analytics with yorkr
38. Beaten by sheer pace! Cricket analytics with yorkr in paperback and Kindle versions
39. Re-introducing cricketr! : An R package to analyze performances of cricketers
40. cricketr sizes up legendary all rounders of yesteryear
41. yorkr ranks IPL Players post 2016 season
42. Googly: An interactive app for analyzing IPL players, matches and teams using R package yorkr
43. GooglyPlus: yorkr analyzes IPL players, teams, matches with plots and tables
44. Inswinger: yorkr swings into International T20s
45. cricketr and yorkr books – Paperback now in Amazon
46. Analysis of International T20 matches with yorkr templates
47. Analysis of IPL T20 matches with yorkr templates
48. cricketr flexes new muscles: The final analysis

VMWare & the cloud
1. Experiences with VMWare workstation 8.0.3: The good,bad & the ugly
2. Sneak preview of Windows 8 with VMWare workstation 8.0.3

Find me on Google+

15 thoughts on “Index of posts

  1. Pingback: Thinking Web Scale-1: Map-Reduce – Bring compute to data | Giga thoughts …

  2. Pingback: Exploring Quantum Gate operations with QCSimulator | Giga thoughts …

  3. Pingback: Taking a closer look at Quantum gates and their operations | Giga thoughts …

  4. Pingback: Introducing QCSimulator: A 5-qubit quantum computing simulator in R | Giga thoughts …

  5. Pingback: IBM Data Science Experience:  First steps with yorkr | Giga thoughts …

  6. Pingback: cricketr sizes up legendary All-rounders of yesteryear | Giga thoughts …

  7. Pingback: Analyzing World Bank data with WDI, googleVis Motion Charts | Giga thoughts …

  8. Pingback: Googly: An interactive app for analyzing IPL players, matches and teams using R package yorkr | Giga thoughts …

  9. Pingback: Googly: An interactive app for analyzing IPL players, matches and teams using R package yorkr - Use-R!Use-R!

  10. Pingback: Googly: An interactive app for analyzing IPL players, matches and teams using R package yorkr | A bunch of data

  11. Pingback: GooglyPlus: yorkr analyzes IPL players, teams, matches with plots and tables | Giga thoughts …

  12. Pingback: Neural Networks: The mechanics of backpropagation | Giga thoughts …

  13. Pingback: Inswinger: yorkr swings into International T20s | Giga thoughts …

  14. Pingback: cricketr flexes new muscles: The final analysis | Giga thoughts …

  15. Pingback: My 3 video presentations on “Essential R” | Giga thoughts …

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