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.

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.

Thursday, July 27, 2017

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

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
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

Intro to Deep Learning

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

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).

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

There are instructions on this website for how to install all the necessary programs at - if you have questions, you can email us or put them in the github issues tracker where they might help another student.

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.

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

Thursday, July 20, 2017

Data Mining - Data Analysis - Credit Scoring

Asurvey of applying machine learning techniques existing models and open issues
Neural information processing

Monday, July 17, 2017

Computer Science - Information Board

MIT’s Daniela Rus is leading a robotics revolution
Posted Jul 11, 2017 by Brian Heater (@bheater)

Saturday, July 15, 2017

Adoption of Cloud Computing


According to IDC, spending on public cloud computing alone will likely increase 24.4 percent in 2017 to reach $122.5 billion. And the same firm forecasts that spending on private cloud infrastructure will grow 16.6 percent this year.

For the fourth quarter of 2016, the survey found that the average organization uses 1, 427 different cloud services,

Cloud Adoption Is Growing But Forecasts Differ on How Much
Barb Darrow
Feb 22, 2017
Overall demand for cloud computing in all its forms will grow 18% this year to $246.8 billion in total worldwide revenue from $209.2 billion, according to a new forecast from market research firm Gartner.

The subset of services called public cloud infrastructure is expected to grow at 36.8% this year to $34.6 billion in revenue worldwide.
Software-as-a-Service (SaaS),  should grow about 20% to $46.3 billion.

IDC estimated that overall cloud services will grow 24.4% year over year to $122.5 billion with SaaS accounting for  60% of total.

Leaders and laggards in enterprise cloud infrastructure adoption
By Nagendra Bommadevara, James Kaplan, and Irina Starikova
October 2016

To address those frustrations, between 2014 and 2016 we surveyed senior business and technology leaders in more than 50 large organizations (Most of them in Fortune 100 in Europe and North America to find out about their adoption of cloud and next-generation infrastructure.1

Despite their high-priority, highly visible, multiyear efforts to implement cloud programs, half of the participants in our survey say they have moved no more than 5 percent of their x86 processing workloads to cloud environments (private or public).

Enterprise Cloud Services


Consumer Cloud Services

Google Drive