Thursday, October 5, 2017

Machine Learning - Introduction



What is machine learning?

Machine learning, as a type of artificial intelligence (AI), enables computers to learn without being explicitly programmed, and to improve their functions when exposed to new data. By analyzing patterns in this data, the machine learning algorithms are self-adjusting based on a set of design rules.

http://www.softvision.com/blog/what-is-machine-learning/

An article in Medium

https://medium.com/@ageitgey/machine-learning-is-fun-80ea3ec3c471#.w55suff6b






How To Become A Machine Learning Engineer: Learning Path
Aug 19, 2017
https://hackernoon.com/learning-path-for-machine-learning-engineer-a7d5dc9de4a4


Machine learning - Notes
http://www.holehouse.org/mlclass/


Updated 6 October 2017,  23 August 2017, 30 July 2016

Monday, September 18, 2017

IBM IoT - Products and Systems



http://www.ibm.com/internet-of-things/



2017
Download the August 2017 Report by Aberdeen on IoT and Analytics: Better Manufacturing Decisions in the Era of Industry 4.0

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Uploaded 19 September 2017


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5 Sep 2017


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25 August 2017


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15 August 2017




http://www.ibm.com/internet-of-things/

http://www.ibm.com/internet-of-things/raspberry-pi.html

http://www.ibm.com/internet-of-things/iot-platform.html

http://www.ibm.com/internet-of-things/watson-iot.html


Cognitive IoT is the use of cognitive computing technologies in combination with data generated by connected devices and the actions those devices can perform.
http://www.ibmbigdatahub.com/blog/what-cognitive-iot?



https://watson.analytics.ibmcloud.com/product



Get started with Watson Analytics
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3 June 2016  uploaded





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IBM wants to replace the spreadsheet with Watson Analytics
http://www.pcworld.com/article/2684332/ibm-wants-to-replace-the-spreadsheet-with-watson-analytics.html


IBM Bluemix


What is Bluemix
The cloud platform powered by the world’s most popular open source projects
http://www.ibm.com/cloud-computing/bluemix/what-is-bluemix/


Products on Bluemix


Compute
Network
Storage
Data and analytics
Watson
Integration
DevOps
Security
Application services
Mobile
Internet of Things

Bluemix and Internet of Things


Experience a fully managed, cloud-hosted service designed to simplify and derive value from your IoT devices. See how companies are using Watson Internet of Things to transform their business from the inside out.
How it all fits together
Connect your device, send data to our cloud, set up and manage your devices, and use APIs to connect apps to your device data.

Start with your device – whether it’s a sensor, gateway, or something else – and let us help you connect it with one of our recipes.
MQTT and HTTP

Your device data is always secure when you connect to the cloud using open, lightweight MQTT messaging protocol or HTTP.
IBM Watson IoT Platform

The hub of the IBM IoT approach – set up and manage your connected devices so your apps can access live and historical data.

REST and real-time APIs
Use our secure APIs to connect your apps with data from your devices.

Your application and analytics
Create applications within IBM Bluemix, another cloud, or your own servers to interpret data.


How much will it (IBM Watson IoT Platform)  cost?



The IBM Watson IoT Platform charges on three metrics.

Devices
The average number of devices you connect over the month. You get 20 free devices a month with each plan.

Data
The amount of data that devices exchange with the IBM Watson IoT Platform and associated applications.
You get free 100 MB data traffic a month with each plan (equivalent to 50,000 messages)
*assuming an average message size of 200 bytes

Storage
The amount of data stored in a historical database.
You get 1 GB free storage a month with each plan
http://www.ibm.com/cloud-computing/bluemix/internet-of-things/



Updated 20 September 2017,  20 September 2016,  24 March 2016

Monday, August 21, 2017

The Evolution of Internet of Things (IoT) - Developments in IoT





IoT Evolution Expo Las Vegas 2017

August 16, 2017
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IoT Evolution


Cisco Keynote at IoT Evolution Expo Ft Lauderdale 2017
14 Mar 2017
Maciej Kranz of Cisco speaks at IoT Evolution Expo Ft Lauderdale 2017
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Cisco Live 2016: The Business of IoT: Go Fast and Grow Fast Now
13 Jul 2016
The Business of IoT: Go Fast and Grow Fast Now - Rowan Trollope, Jahangir Mohammed, and Sandy Hogan.

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Cisco



2013

A white paper on IoT evolution by Texas Instruments
http://www.ti.com/lit/ml/swrb028/swrb028.pdf

Monday, July 31, 2017

Recent Books - IoT - Industry 4.0



E-Technologies: Embracing the Internet of Things: 7th International Conference, MCETECH 2017, Ottawa, ON, Canada, May 17-19, 2017, Proceedings


Esma Aïmeur, Umar Ruhi, Michael Weiss
Springer, 09-Jun-2017 - Computers - 319 pages


This book constitutes the refereed proceedings of the 7th International Conference on E-Technologies, MCETECH 2017, held in Ottawa, ON, Canada, in May 2017.
This year’s conference drew special attention to the ever-increasing role of the Internet of Things (IoT); and the contributions span a variety of application domains such as e-Commerce, e-Health, e-Learning, and e-Justice, comprising research from models and architectures, methodology proposals, prototype implementations, and empirical validation of theoretical models.

The 19 papers presented were carefully reviewed and selected from 48 submissions. They were organized in topical sections named: pervasive computing and smart applications; security, privacy and trust; process modeling and adaptation; data analytics and machine learning; and e-health and e-commerce.

https://books.google.co.in/books?id=UWbTDgAAQBAJ


Industry X.0: Realizing Digital Value in Industrial Sectors


Eric Schaeffer
Kogan Page Publishers, 03-May-2017 - Business & Economics - 192 pages


Industry X.0 takes an insightful look at the business impact of the Internet of Things movement on the industrial sphere. Eric Schaeffer combines deep analysis with practical strategic guidance, and offers tangible and actionable recommendations on how to realise value in the current digital age. Based on extensive research and insights into the six core competencies that have been identified by Accenture, Industry X.0 explores critical aspects of the Industrial Internet of Things (IIoT), discussing and defining them in an engaging and accessible manner. These include managing smart data, handling digital product development, skilling up the workforce, mastering innovation, making the most of platforms and ecosystems, and much more.

Meticulously researched and clearly explained, Industry X.0 makes a stringent case for companies to actively shift mind-sets away from products, towards services, value and outcomes. Complemented by a wealth of case studies and real world examples, this book provides invaluable, practical 'how-to' advice for business organizations as they embark on their journeys into the era of the IIoT.

https://books.google.co.in/books?id=PNy_DgAAQBAJ

Thursday, July 27, 2017

AI, Machine Learning & Deep Learning - Education - Training Programs - USA







https://www.eventbrite.com/e/technical-introduction-to-ai-machine-learning-deep-learning-tickets-34671486349

https://www.facebook.com/events/1729161024041553



Technical Introduction to AI, Machine Learning & Deep Learning
Engineered Education
Friday, July 28, 2017 from 9:00 AM to 7:00 PM (PDT)
San Francisco, CA
TICKET TYPE SALES END PRICE FEE QUANTITY
Registration $495.00 $13.37
Team Discount (4 or more registrations)   $349.00 $9.72



This workshop will arm you with the tools to get started using machine learning in your day job and the resources to find additional help if you want to go deeper.
The course is expertly designed to leave you with the ability to take training data, do feature selection and actually build models for applications like content categorization, sentiment analysis, and image recognition. By the end of the day, students will be able to use models in their day-to-day work. You will also walk away with a high-level understanding of how common models such as Deep Neural Networks, SVMs, Logistic Regression and Naive Bayes work and when to use them.

Technologies Introduced

Intro to Machine Learning
Scikit-learn
Numpy
Pandas

Intro to Deep Learning
TensorFlow
Keras

Intro to Machine Learning Platforms
Google Cloud ML
Azure ML
Amazon ML

Prerequisites
We try to make this class as accessible as possible. Some proficiency with Python is necessary. If you can open up a Jupyter notebook and install requisite software that’s helpful but we’ll also cover how to do that quickly in the beginning.



What you Need to bring

You must also bring your own laptop (don’t forget your charger).

Preparation
It saves a lot of time if you can get your laptop setup in advance.  If you can't get everything setup, try to come early and we'll help you with the installation.

Download code for the class from https://github.com/lukas/ml-class.

There are instructions on this website for how to install all the necessary programs at https://github.com/lukas/ml-class/blob/master/README.md - if you have questions, you can email us or put them in the github issues tracker where they might help another student.

Teacher
Lukas Biewald:  Lukas Biewald is the founder of CrowdFlower, an Artificial Intelligence company that works with data science teams at Google, Bloomberg, Facebook and hundreds of other organizations to make machine learning work in the real world. Prior to that, Lukas was the first data scientist at Powerset (Acquired by Microsoft and rebranded as Bing) and a scientist at Yahoo!, Lukas was shipping machine learning algorithms to hundreds of millions of users.

Lukas frequently teaches invited Machine Learning workshops with Galvanize, O’Reilly and ODSC. He is a frequent contributor to Computerworld, Forbes and O’Reilly and has presented at the machine learning academic conferences such as AAAI, SIGIR, ACL and EMNLP. He was in Inc’s annual 30 under 30 and was also a finalist at TechCrunch Disrupt.

Curriculum
9:00 – 10:00 Breakfast and Intro to Machine Learning
We will assume no knowledge of Machine Learning, so we'll go over terminology and the history of Machine Learning and Artificial Intelligence.  We'll talk about the common use cases and how they fit in with the different Machine Learning algorithms.

10:00 – 12:00 Build a Sentiment Classifier From Scratch
Everyone builds a Twitter sentiment classifier using scikit-learn. We try multiple feature selection approaches and multiple model types. We learn some common tricks for actually making machine learning effective in the real world.

12:00-1:00 Lunch and Overview of State Machine Learning
Eat lunch and for your eating entertainment, Lukas will introduce a little math, stats and history of how machine learning got to where it is today.  We will go over the state of machine learning platforms today and how to get an entry-level job in machine learning for those that are interested.

1:00-2:30 Try the Common Machine Learning Platforms
These days, there are many excellent, scalable, low cost machine learning platforms. We will try rebuilding our sentiment classifier on two of the most common: Microsoft Azure ML and Amazon ML.

2:30-3:00 Break and Q&A
We can discuss other applications of this technology and look at how it might apply to real-world tasks that students may be working on.

3:00-5:00 Introduction to TensorFlow and Deep Neural Networks
We will learn how deep neural networks work and actually build one! If you bring a laptop with a GPU that supports CUDA (for example a MacBook with Mac OS X 10.11 or later), we’ll see if we can make it GPU accelerated.
We’ll all build a network to do handwritten digit recognition.

5:00-5:30 Wrap-up and Q&A
We will finish up and discuss how to apply this knowledge directly to problems that we actually face in our jobs.

5:30-7:00 Drinks & Networking
We’ll bring together top entrepreneurs, tech executives & engineers to connect with and learn from. Plus, this is a chance to meet your classmates and teachers in an informal and fun setting.




Sunday, July 23, 2017

Data Mining - Mining of Massive Data Sets

Standford Course Page of Mining of Massive Data Sets

You can download full book published by Cambridge Press

Jure Leskovec, Anand Rajaraman, Jeff Ullman

http://www.mmds.org/