Friday, July 21, 2017

DUOLINGO: Providing free, global, language education - 150 million learners




WHAT IS DUOLINGO1
·         Duolingo is a freemium language-learning platform that includes a language learning website and app, as well as a digital language proficiency assessment exam. (A freemium is a pricing strategy by which a product or service is provided free of charge, but money (premium) is charged for advanced features, functionality, or related products and services2);
·         Duolingo offers all its language courses free of charge;
·         The language-learning website and app offers 68 different language courses across 23 languages – with 22 additional courses in development (as of Nov 2016);
·         The app is available on iOS, Android and Windows 8 and 10 platforms with about 150 million registered learners/users across the world.

HISTORY1
·         The Duolingo project was started at the end of 2009 in Pittsburgh, U.S., by Carnegie Mellon University professor Luis von Ahn and his graduate student, Severin Hacker;
·         One important inspiration for Duolingo came from von Ahn himself.  He was born in Guatemala and saw how expensive it was for people in his community to learn English;
·         Both co-founders believe that “free education will really change the world” and wanted to supply the people an outlet to do so – hence the initiation of Duolingo.

WHAT DUOLINGO SAY ABOUT THEMSELVES3
·         Duolingo is a great way to learn a language. It provides personalized education as people learn in different ways;
·         It’s addictive – split into bite-sized units that feel like games – gamification poured into every lesson;
·         Learners can study during breaks, commute, while waiting in line;
·         Duolingo teaches learners to read, write, listen, and speak a language;
·         Extremely effective;
·         Completely free so it is universally accessible: no annoying ads, no misleading In-App purchases, no subscription fees;
·         They know it’s hard to stay motivated when learning online, so they made Duolingo so fun that people would prefer picking up new skills over playing a game.


WHAT PEOPLE SAY ABOUT DUOLINGO
·         According to Dave (studying French from the English platform):
o   Duolingo is “so much better, not to mention free” – compared to the ones he tried before;
o   Duolingo is “a lot more interactive and much smarter at becoming progressively yet appropriately more difficult as you learn things;”
·          According to Mary (studying German from the English platform):
o   “Duolingo makes learning German language fun and I look forward to my next 10 minutes a day.”
·         I opened and did the Basic French lessons from English platform, and saw that:
o   Daily goal options for the lessons are “only” 5, 10, 15 and 20 minutes/day.  These "short" lesson durations look very doable and encouraging.  The fact that learners do these "short" lessons daily will ensure that they will build up substantial expertise over time;
o   The lesson activities are “fun” and “interesting.”

LANGUAGES ON OFFER AT DUOLINGO3

Danish
Hindi  
Spanish
Dutch
Hungarian
Swahili
English
Italian
Swedish
Esperanto*
Irish
Turkish
French
Norwegian
Ukrainian
German
Polish
Vietnamese
Greek
Portuguese
Welsh
Hebrew
Romanian  
---
High Valyrian*
Russian
---


*Created language 


TAKING DUOLINGO LESSONS
·         Create a profile to save your progress;
·         To start on a Duolingo lesson, please click here.

REFERENCES




Posted by: Dr. Nat Tuivavalagi




Wednesday, July 12, 2017

Deep Learning – Moving Machines Closer to Attaining Artificial Intelligence




introduction
·         In recent years, Deep Learning has become an area of active research, and has also become a buzzword that has been tossed around a lot;
·         Deep Learning is becoming increasingly important these days because it teaches computers the skills our brains do naturally e.g. recognizing handwritten digits;
·         This posting is focused primarily to those with little or no understanding of Deep Learning.  The main goal is to increase understanding of this topic through simple language, and to point out some free online courses and resources that are available for a better understanding of Deep Learning.

DEEP LEARNING & MACHINE LEARNING
·         “Deep Learning” comes under the broader field of “Machine Learning” which was covered in the previous post; 
·         Deep Learning is a specific type of Machine Learning, focusing on high order statistics1;
·         Deep Learning is a new area of Machine Learning research. It was introduced with the objective of moving Machine Learning closer to one of its original goals, Artificial Intelligence2;
·         Please consider having another quick look at the previous post on Machine Learning (July 1, 2017 – see below).

WHAT IS DEEP LEARNING?  We hope to arrive at some understanding of Deep Learning by considering how this activity has been described in various publications:
·         Deep Learning has been described as follows3:
o   Deep Learning is a paradigm (example/ pattern/ model) for performing Machine Learning;
o  Deep Learning is a form of Machine Learning that uses a model that’s very much inspired by the structure of the brain's computing.  (Hence we call this model a neural network); and
o   Deep Learning is an extremely powerful tool for modern Machine Learning;
·         Deep Learning is a branch of Machine Learning that uses algorithms* to do things like recognizing objects and understanding human speech4. [*Algorithms = Processes or set of rules to be followed in solving problems];
·         Deep Learning refers to  learning tasks of artificial neural networks (ANNs) that contain more than one hidden layer. (Deep learning is part of a broader family of Machine Learning methods based on learning data representations, as opposed to task specific algorithms)5;
·        In Deep Learning, a software attempts to mimic the brain activity in layers of neurons in the neocortex, (the wrinkly 80 percent of the brain where thinking occurs).  The software learns in a very real sense, to recognize patterns in digital representations of sounds, images, and other data6. [In the human brain, the neocortex has six layers and contains between 10 and 14 billion neurons];
·         Deep Learning employs numerous, similar, yet distinct, deep neural network architectures to solve various problems in natural language processing, computer vision, bioinformatics, and many other fields7.

PREREQUISITES
·         The prerequisites for understanding and applying Deep Learning are8:
o   Linear algebra;
o   Calculus;
o   Statistics;
o   Programming; and
o   Some Machine Learning.

APPLICATIONS
·         Deep Learning is responsible for recent advances in4:
o   Speech recognition;
o   Computer vision;
o   Natural language processing; and
o   Audio recognition.
·         Apart from speech recognition, Deep Learning methods have dramatically improved the state-of-the-art in9:
o   Visual object recognition;
o   Object detection;
o   Drug discovery;
o   Genomics; and
o   Many other domains.
·         Some inspirational examples of Deep Learning include10:
o   Colorization of Black and White Images;
o   Adding Sounds to Silent Movies;
o   Automatic Machine Translation;
o   Object Classification in Photographs;
o   Automatic Handwriting Generation;
o   Image Caption Generation; and
o   Automatic Game Playing.

FREE ONLINE COURSES & RESOURCES
·         For free online courses and resources relating to Deep Learning, please click here.

REFERENCES
2.       http://deeplearning.net/



Posted by Dr. Nat Tuivavalagi




Saturday, July 1, 2017

Machine Learning – One of the most popular MOOCs of all time with over one million enrollments




INTRODUCTION
“Machine Learning,”  has been reported1 as one of the most popular MOOCs of all time. A program from Stanford University on Coursera, with a total enrollment of 1,122,031.  With such a high interest in “Machine Learning,” one wonders: 1) What is Machine Learning? 2) How does Machine Learning help us in our daily life? 3) Why should we study Machine Learning? 4) How do we get started studying Machine Learning? 5) What's out there for  Machine Learning MOOCs and Other MOOCs?

WHAT IS MACHINE LEARNING
First we need to describe an “algorithm” –  defined2 as a procedure for solving a problem based on conducting a sequence of specified actions. 

In Machine Learning (ML)3, an ML algorithm is given a “teaching set” of data, then asked to use that data to answer a question.  For example, you might provide a computer a “teaching set” of photographs some of which say “this is a cat” and some of which say, “this is not a cat.”  Then you could show the computer a series of new photos and the computer would identify which photos were of cats.

Every photo identified by the computer gets added to the “teaching set,” and the program effectively gets “smarter” and more efficient at completing its task over time.

HOW MACHINE LEARNING HELP US IN OUR DAILY LIFE3
Machine Learning has helped us in many ways including the 10 examples below:

1.       Helps us improve data security, e.g., by identifying and reporting anomalies that could predict security breaches;
2.       Helps us improve personal security, e.g., by speeding up security screenings at airports, stadiums, concerts, and other venues;
3.       Assists us in financial trading, e.g., with better predictions of what the stock markets will do on any given day;
4.       Helps us with healthcare, e.g., with early detection of breast cancer;
5.       Helps businesses make more sales by marketing personalization, i.e., by helping businesses understand more about their customers;
6.       Assist us in improving fraud detection, e.g., PayPal is using Machine Learning to fight money laundering;
7.       Assists services like Amazon in making accurate/appropriate recommendations regarding what we might want to purchase;
8.       Assist Google in making smart/appropriate responses to our online searches – perhaps the most famous use of Machine Learning;
9.       Assist in translating obscure legalese in contracts into plain language and help attorneys sort through large volumes of information to prepare for a case;
10.   Assist in the functioning of smart cars which are expected to be on the road by 2025.


WHY SHOULD WE STUDY MACHINE LEARNING4
1.       It’s a big deal: Machine Learning is the rave of the moment;
2.       It’s closely linked to data science: Just as humans learn from experience, Machine Learning systems learn from data;
3.       To become unwary of the dangers of Artificial Intelligence: There is likely to be a positive demand of engineers, come what may.

HOW TO GET STARTED ON STUDYING MACHINE LEARNING4
·         Learn a programming language, e.g., Python
·         Get a high-end PC
·         Learn the prerequisites: Basic Statistics; Basic Linear Algebra; Basic Calculus
·         Read academic papers on Machine Learning
·         Learn from YouTube Videos
·         Read Blogs and Follow Online Communities
·         Practice: Try your hands at Machine Learning projects and participate in contests hosted on Kaggle and similar sites

MOOCs ON MACHINE LEARNING & OTHER SUBJECTS
·         To explore the Machine Learning MOOC on Coursera by Stanford University, please click here
·         To learn how you can get a world-class machine learning education for free, please click here
·         For free, online machine learning courses and MOOCs, please click here
·         To explore Dr. Jason Brownlee’s website on Machine Learning, please click here
·         To explore other MOOCs, please click here

REFERENCES
1.       http://www.onlinecoursereport.com/the-50-most-popular-moocs-of-all-time/
2.       http://whatis.techtarget.com/definition/algorithm
3.       https://www.forbes.com/sites/bernardmarr/2016/09/30/what-are-the-top-10-use-cases-for-machine-learning-and-ai/#5799b85c94c9A
4.       http://www.business2community.com/tech-gadgets/heres-learn-machine-learning-01818308#ChG2wuXsdvbXYEVU.97



Posted by: Dr. Nat Tuivavalagi