Learning comes from experience, that's a fact. But what kind of experience makes you want to learn and helps you achieve your goals? And how do you design such an experience?

That's wat Learning Experience Design is about!

But what is it exactly? Here's the definition of Niels Floor, director of Shapers:

"Learning Experience Design (LX design) is the process of creating learning experiences that enable the learner to achieve the desired learning outcome in a human centered and goal oriented way." - Niels Floor

That's a lot to take in, so this video breaks the definition down into 3 parts:

- Experience: What you create is an experience.
- Design: LX design is a design discipline.
- Learning: The experience that you design serves a clear purpose, learning!

And there you have it. This is Learning Experience Design.

Want to know more about Learning Experience Design?
Check out www.learningexperiencedesign.com

Or do you need some help with your LX design?
Shapers would love to help out. Check www.shapers.eu

Matthew Peterson, CEO of MIND Research Institute, speaks at TEDx Orange Coast, explaining how words are great barriers to learning for a majority of students. His own struggles with dyslexia and inspiration from Albert Einstein led him to ask the question: can we teach math without words?

MIND Research Institute has created a visual approach to learning and teaching math with its ST Math Software. Through visual math games that are interactive with visual feedback, students learn math with amazing results. ST Math software utilizes years of neuroscience research that teaches kids how to excel in math problem solving utilizing the students spatial temporal reasoning abilities in a language independent visually driven software platform.

Matthew's cutting-edge teaching methods are currently benefiting over 1,200,000 students in 3,200 schools across the United States.

Learn more and play ST Math: https://www.stmath.com/

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About MIND:
MIND Research Institute is a social benefit organization dedicated to ensuring that all students are mathematically equipped to solve the world's most challenging problems.

Learn more about MIND Research Institute: http://www.mindresearch.org

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Join the learning community on social media!
MIND Twitter: https://twitter.com/mind_research
ST Math Twitter: https://twitter.com/jijimath
Facebook: https://www.facebook.com/JiJiMath/
Pinterest: https://www.pinterest.com/jijimath/
LinkedIn: https://www.linkedin.com/compa....ny/mind-research-ins

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng lectures on linear regression, gradient descent, and normal equations and discusses how they relate to machine learning.

This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.

Complete Playlist for the Course:
http://www.youtube.com/view_pl....ay_list?p=A89DCFA6AD

CCS 229 Course Website:
http://www.stanford.edu/class/cs229/

Stanford University:
http://www.stanford.edu/

Stanford University Channel on YouTube:
http://www.youtube.com/stanford

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng provides an overview of the course in this introductory meeting.

This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.

Complete Playlist for the Course:
http://www.youtube.com/view_pl....ay_list?p=A89DCFA6AD

CS 229 Course Website:
http://www.stanford.edu/class/cs229/

Stanford University:
http://www.stanford.edu/

Stanford University Channel on YouTube:
http://www.youtube.com/stanford

Listen to podcast version here: https://goo.gl/pBtTm0 - Good Life Project founder, Jonathan Fields, interviews The First 20 Hours author, Josh Kaufman about accelerated learning and getting good at any skill in 20 hours.

If you'd prefer to listen to this and the entire library of Good Life Project as a podcast, just go to http://bit.do/goodlife to subscribe at iTunes.

Source - http://serious-science.org/videos/1136

Harvard University Prof. Eric Mazur on difficulties of beginners, teaching each other, and making sense of information

http://CppCon.org
—
Presentation Slides, PDFs, Source Code and other presenter materials are available at: https://github.com/CppCon/CppCon2017
—
We – attendees at CppCon – are all teachers. Some teach for a living; many occasionally teach a course or give a lecture; essentially all give advice about how to learn C++ or how to use C++. The communities we address are incredibly diverse.

What do we teach, and why? Who do we teach, and how? What is “modern C++”? How do we avoid pushing our own mistakes onto innocent learners?

Teaching C++ implies a view of what C++ is; there is no value-neutral teaching. What teaching tools and support do we need? Consider libraries, compiler support, and tools for learners. This talk asks a lot of questions and offers a few answers. Its aim is to start a discussion, so the Q&A will be relatively long.
—
Bjarne Stroustrup - Managing Director,, Morgan Stanley
C++: history, design, use, standardization, future; performance, reliability; software developer education; | distributed systems
—
Videos Filmed & Edited by Bash Films: http://www.BashFilms.com

Introduces the key ideas behind Deep Learning (machine learning + distributed representations). Part 1 of a series on AI. Part 2 is here: https://www.youtube.com/watch?v=yLAwDEfzqRw support this program: https://www.patreon.com/artoftheproblem

Discusses what active learning is and provides examples of how active learning can be used in both face to face and online classes.

Is Learning Feasible? - Can we generalize from a limited sample to the entire space? Relationship between in-sample and out-of-sample. Lecture 2 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes.apple.com/us/co....urse/machine-learnin and on the course website - http://work.caltech.edu/telecourse.html

Produced in association with Caltech Academic Media Technologies under the Attribution-NonCommercial-NoDerivs Creative Commons License (CC BY-NC-ND). To learn more about this license, http://creativecommons.org/licenses/by-nc-nd/3.0/

This lecture was recorded on April 5, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA.

This talk is presented by Zach Miller, Senior Data Scientist at Metis


Learn more: https://www.ideassn.org/
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In this edition of Learning for the Future, we take a look at professional learning communities, or PLCs. PLCs allow teachers and administrators to examine the way they work and to focus on developing a system of ongoing, job-embedded professional development. This episode shows PLCs in action at Montgomery Blair High School and Shady Grove Middle School.

AlphaGo beat the Go World Champion 4-1. Why do the creators not know how? Brais Martinez is a Research Fellow & Deep Learning expert at the University of Nottingham.

http://www.facebook.com/computerphile
https://twitter.com/computer_phile

This video was filmed and edited by Sean Riley.

Computer Science at the University of Nottingham: http://bit.ly/nottscomputer

Computerphile is a sister project to Brady Haran's Numberphile. More at http://www.bradyharan.com

http://www.DistanceLearningPortal.com helps students to find and compare any open, online and distance learning study, while helping universities to effectively promote their study programmes worldwide.

Imagine you could study at the best institutes in the world -- without leaving your country. Would you be interested?

The idea of distance learning is not new, but the offer is increasing rapidly: There are traditional universities, open universities, and online institutes.
So what makes distance education different? Well apart from a much higher degree of freedom it is very much like traditional education.

It covers all degree levels and subjects, often delivered online. You will be taught by a lecturer, receive guidance by tutors, discuss with your classmates, and take exams. All however without the constraints of physical presence and strict schedules.
With distance learning you can study at your own speed, from anywhere and at any time. So why not start today?
Check http://www.distancelearningportal.com and find your dream education today.

TEDxEnola: February 1st, 2012
Dr. Martha S. Burns - "The New Brain Science of Learning"
Enola, Pennsylvania

In the spirit of ideas worth spreading, TEDx is a program of local, self-organized events that bring people together to share a TED-like experience. At a TEDx event, TEDTalks video and live speakers combine to spark deep discussion and connection in a small group. These local, self-organized events are branded TEDx, where x = independently organized TED event. The TED Conference provides general guidance for the TEDx program, but individual TEDx events are self-organized.* (*Subject to certain rules and regulations)

Our robot system, Dactyl, has learned to manipulate objects with unprecedented dexterity. Learn more: https://blog.openai.com/learning-dexterity/


Music: "Handsome Devil" by Gyom

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Watch the full episode here: https://londonreal.tv/e/robert....-oberst-strong-and-p

Robert Oberst is the professional strongman known as “The American Monster”.

He has competed in Strongman all around the world since 2012, including America’s Strongest Man, the Arnold Classic and five times in the World’s Strongest Man, twice placing as a finalist.

Today Robert stars on the History Channel television show The Strongest Man In History, alongside Brian Shaw, Nick Best and Eddie Hall, while travelling the world promoting strongman with his catchphrase “Strong and Pretty”.


ROBERT OBERST:

Instagram: https://www.instagram.com/robertoberst/

Robert’s Clothing: https://www.bunkerbranding.com/pages/robert-oberst


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#LondonReal #Motivation #TransformYourself

Yann LeCun
Director of AI Research at Facebook, Professor of Computer Science, New York University

November 18, 2016

Abstract
The rapid progress of AI in the last few years are largely the result of advances in deep learning and neural nets, combined with the availability of large datasets and fast GPUs. We now have systems that can recognize images with an accuracy that rivals that of humans. This will lead to revolutions in several domains such as autonomous transportation and medical image understanding. But all of these systems currently use supervised learning in which the machine is trained with inputs labeled by humans. The challenge of the next several years is to let machines learn from raw, unlabeled data, such as video or text. This is known as unsupervised learning. AI systems today do not possess "common sense", which humans and animals acquire by observing the world, acting in it, and understanding the physical constraints of it. Some of us see unsupervised learning as the key towards machines with common sense. Approaches to unsupervised learning will be reviewed. This presentation assumes some familiarity with the basic concepts of deep learning.


Speaker Biography
Yann LeCun is Director of AI Research at Facebook, and Silver Professor of Data Science, Computer Science, Neural Science, and Electrical Engineering at New York University, affiliated with the NYU Center for Data Science, the Courant Institute of Mathematical Science, the Center for Neural Science, and the Electrical and Computer Engineering Department. He received the Electrical Engineer Diploma from Ecole Superieure d'Ingenieurs en Electrotechnique et Electronique (ESIEE), Paris in 1983, and a PhD in Computer Science from Universite Pierre et Marie Curie (Paris) in 1987. After a postdoc at the University of Toronto, he joined AT&T Bell Laboratories in Holmdel, NJ in 1988. He became head of the Image Processing Research Department at AT&T Labs-Research in 1996, and joined NYU as a professor in 2003, after a brief period as a Fellow of the NEC Research Institute in Princeton. From 2012 to 2014 he directed NYU's initiative in data science and became the founding director of the NYU Center for Data Science. He was named Director of AI Research at Facebook in late 2013 and retains a part-time position on the NYU faculty. His current interests include AI, machine learning, computer perception, mobile robotics, and computational neuroscience. He has published over 180 technical papers and book chapters on these topics as well as on neural networks, handwriting recognition, image processing and compression, and on dedicated circuits and architectures for computer perception. The character recognition technology he developed at Bell Labs is used by several banks around the world to read checks and was reading between 10 and 20% of all the checks in the US in the early 2000s. His image compression technology, called DjVu, is used by hundreds of web sites and publishers and millions of users to access scanned documents on the Web. Since the late 80's he has been working on deep learning methods, particularly the convolutional network model, which is the basis of many products and services deployed by companies such as Facebook, Google, Microsoft, Baidu, IBM, NEC, AT&T and others for image and video understanding, document recognition, human-computer interaction, and speech recognition. LeCun has been on the editorial board of IJCV, IEEE PAMI, and IEEE Trans. Neural Networks, was program chair of CVPR'06, and is chair of ICLR 2013 and 2014. He is on the science advisory board of Institute for Pure and Applied Mathematics, and has advised many large and small companies about machine learning technology, including several startups he co-founded. He is the lead faculty at NYU for the Moore-Sloan Data Science Environment, a $36M initiative in collaboration with UC Berkeley and University of Washington to develop data-driven methods in the sciences. He is the recipient of the 2014 IEEE Neural Network Pioneer Award.

After devoting himself to learning a new skill every single week for 52 weeks, Stephen shares invaluable advice to learning anything. In this comedic talk, Stephen explores how we can motivate ourselves to pursue our ambitions and ultimately, follow through on our commitments.


A native to Edmonton, Stephen is currently pursuing a degree in psychology from the University of Alberta. In addition to his studies, Stephen has led a variety of entrepreneurial ventures and passion projects to learn from direct experience. 52skillz is a project where he commits himself to learning a new skill every week of the year, for an entire year. The goal of the project is to inspire individuals of all ages to take action in learning and developing new skills in an entertaining and humour filled way.

This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx

Best Toy Food Learning Video With Baby Shimmer's High Chair Shimmer and Shine with Toy Genie. This is the best toy food learning video. What did baby Shimmer eat? Learn fruits, vegetables, and desserts using Shopkins.

Aprender alimentos en inglés video para niños pequeños y preescolar. Comidas, frutas, y vegetales.

Subscribe Here: http://www.youtube.com/subscri....ption_center?add_use


Here are some links to my other videos that you may like:

Best Learning Video Colors and Numbers Shrek Eats Too Much Candy Rotten Teeth: https://youtu.be/BnRiQDDNqdk

LEARN COLORS Shrek Eats Gumballs Brush Teeth: https://youtu.be/DrCP6BnurTw

Gumball Banks LEARN Colors and Numbers with Gumballs: https://youtu.be/ydfqEG8uXO0

Here are more of my videos in playlists:

Mickey Mouse Club House Friends and Disney Tsum Tsum Playlist: https://www.youtube.com/watch?v=H-dN4zNFmaM&list=PL8yauLj_sBJCRdwghIqXPNosIriV7WPjH

Disney Princess and Disney Toys Playlist: https://www.youtube.com/watch?v=LUquV7eqQak&list=PL8yauLj_sBJBReHXHVFlmtlRt_Ba1lCUl

My Little Pony Surprises, Playsets, and Dolls Playlist: https://www.youtube.com/watch?v=yMFEc1P7_OM&list=PL8yauLj_sBJDqN2e0kB3AHJS9EpaD16fI

Best Learning Video ABC123 Colors and Numbers Kids Preschool Learn With Toys and Play Doh Playlist: https://www.youtube.com/watch?v=nqYEXjvQMNg&list=PL8yauLj_sBJCKxWXXyG11aD5VpIqn8Jbv

Paw Patrol Surprises with Play Doh and Learning Playlist: https://www.youtube.com/watch?v=nqYEXjvQMNg&list=PL8yauLj_sBJC94Mr-kxqznpPSUP12-9DW

Blind Bags, Blind Baskets, Mystery Boxes, Surprise Eggs Playlist: https://www.youtube.com/watch?v=hiAH0iLOJq4&list=PL8yauLj_sBJAzX_EqBbiphR-mzobRgBCO

Fashems and Mashems Playlist: https://www.youtube.com/watch?v=tedYS4WATGE&list=PL8yauLj_sBJA7pYu8JQY8UZ7E3ZaJggDq


Please share this video of Best Toy Food Learning Video With Baby Shimmer's High Chair Shimmer and Shine: https://youtu.be/X0LZpPTqv9c

Music: YouTube

Thank you for watching my videos,

Toy Genie Surprises

Please support my channel by liking, subscribing, and sharing my videos. :)

With plenty of machine learning tools currently available, why would you ever choose an artificial neural network over all the rest? This clip and the next could open your eyes to their awesome capabilities! You'll get a closer look at neural nets without any of the math or code - just what they are and how they work. Soon you'll understand why they are such a powerful tool!

Deep Learning TV on
Facebook: https://www.facebook.com/DeepLearningTV/
Twitter: https://twitter.com/deeplearningtv

Deep Learning is primarily about neural networks, where a network is an interconnected web of nodes and edges. Neural nets were designed to perform complex tasks, such as the task of placing objects into categories based on a few attributes. This process, known as classification, is the focus of our series.

Classification involves taking a set of objects and some data features that describe them, and placing them into categories. This is done by a classifier which takes the data features as input and assigns a value (typically between 0 and 1) to each object; this is called firing or activation; a high score means one class and a low score means another. There are many different types of classifiers such as Logistic Regression, Support Vector Machine (SVM), and Naïve Bayes. If you have used any of these tools before, which one is your favorite? Please comment.

Neural nets are highly structured networks, and have three kinds of layers - an input, an output, and so called hidden layers, which refer to any layers between the input and the output layers. Each node (also called a neuron) in the hidden and output layers has a classifier. The input neurons first receive the data features of the object. After processing the data, they send their output to the first hidden layer. The hidden layer processes this output and sends the results to the next hidden layer. This continues until the data reaches the final output layer, where the output value determines the object's classification. This entire process is known as Forward Propagation, or Forward prop. The scores at the output layer determine which class a set of inputs belongs to.

Links:
Michael Nielsen's book - http://neuralnetworksanddeeplearning.com/
Andrew Ng Machine Learning - https://www.coursera.org/learn/machine-learning
Andrew Ng Deep Learning - https://www.coursera.org/speci....alizations/deep-lear

Have you worked with neural nets before? If not, is this clear so far? Please comment.
Neural nets are sometimes called a Multilayer Perceptron or MLP. This is a little confusing since the perceptron refers to one of the original neural networks, which had limited activation capabilities. However, the term has stuck - your typical vanilla neural net is referred to as an MLP.

Before a neuron fires its output to the next neuron in the network, it must first process the input. To do so, it performs a basic calculation with the input and two other numbers, referred to as the weight and the bias. These two numbers are changed as the neural network is trained on a set of test samples. If the accuracy is low, the weight and bias numbers are tweaked slightly until the accuracy slowly improves. Once the neural network is properly trained, its accuracy can be as high as 95%.


Credits:
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelan....cers/~0147b8991909b2
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

Join Gecko in Gecko's Real Vehicles with this awesome educational compilation of all your favourite emergency vehicles!

Love Toddler Fun Learning and Gecko's Garage? You can watch all of our videos over on our app. Watch ad free and download for offline viewing. Click here to download http://apple.co/2t5dDla

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Toddler Fun Learning makes fun, free and educational videos, nursery rhymes, stories and songs for toddlers all over the world.

For the past 2 years we have experimented with a new format - Created communication and dialogue tools by drawing - AND filming. With over 25 client and project videos out, we are now proud to present our two latest videos. They aim at explaining our own field: Visual Thinking and Graphic Facilitation.

They are short introductions to different aspects of our methodology.

the first video explains 7 basic elements on how you can learn to draw almost anything when working with people.

The second video show what tools we suggest you bring along when facilitating graphically.

Stay tuned for more.