A learning organization encourages personal mastery and cultivates open feedback to see problems and opportunities on all levels. Some argue that learning organizations attract and retain more talents. Others say that there is a competitive advantage for an organization whose people learn faster than the people of other organizations. Here are 6 characteristics most have in common:

They Cherish: An Open Culture.
Learning organizations encourage everyone to share information, admit to mistakes and practice giving and taking constructive criticism. Once the problem is found, they try to understand its root cause and fix it. To achieve such a culture: walls are removed, information is shared and leaders show their human sides.

They Design and Implement: Feedback Loops
Some establish 360 degrees surveys, in which people assess themselves, their peers and their bosses. Employees at 5-star hotels ask guests for their opinions. Top schools may videotape teachers so they can later study themselves. Some even make feedback a team effort. Before any new project, they all get together to kick things off. After the project they meet again to share and reflect on what has happened.

They Promote: Personal Mastery
Employees try to achieve personal mastery in their fields. Once they become experts, they feel proud of their work, they are motivated intrinsically and they can create positive change wherever they are. For example, a cleaner might come up with an idea on how to save water and an accountant on how to save bank fees. The job of the boss is to connect all experts and give directions.

They Plan for: Intelligent Fast Failure.
When they build something new, they don’t spend time to make assumptions on paper. Instead, they create what’s called Minimum Viable Product, a simple prototype with only the core functions. This is then presented to users as early as possible to test what they think. Because it is imperfect, even friends give their honest opinions. The goal: fail fast, but collect intelligent information so you can improve while going forward.

They Steal: Best Practices
Picasso apparently said that "good artists borrow, great artists steal". Learning organizations study others, steal best practices and then implement them fast. The newspaper The Economists took advice from George Orwell; its editors never use jargon if everyday English works. Printing manufacturers stole the razor-and-blades business model from Gillette, selling printers cheap but ink expensive.

They Cultivate: A Common Vision
A learning organization prospers when all members share a common vision. That way employees can understand the importance of their role, connect the dots and develop systems thinking. When goals are clear, regulations can be reduced and people can create their own personal benchmarks of success. This reduces bureaucracy, authority and corruption.

Salesmen and author Zig Ziglar once wrote: "the only thing worse than training people and having them leave, is not training them and having them stay." At a learning organization education happens as a side-product of working together, as everybody learns from each other to adapt to whatever the future might bring.

REMASTERED IN HD!

Music video by Tom Petty performing Learning To Fly. (C) 1991 UMG Recordings, Inc.

#TomPetty #LearningToFly #Remastered #Vevo #Rock #OfficialMusicVideo

Can an AI learn to play the perfect game of Snake?
Huge thanks to Brilliant.org for supporting this channel, check them out: https://www.brilliant.org/CodeBullet

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In this video, I learn how to backflip my full suspension mountain bike with the help of my friend, Phil.

I was lucky enough to have a facility, designed for learning tricks safely. If you want to learn flips on your own, first ask yourself if you're as badass as these guys:

https://www.youtube.com/watch?v=XzTcFgJ37Vo
https://www.youtube.com/watch?v=vTZlz4H73SQ
https://www.youtube.com/watch?v=eSUabZjauFo
https://www.youtube.com/watch?v=tQCGOuhfovQ
https://www.youtube.com/watch?v=IsCS8lCvYMk
https://www.youtube.com/watch?v=VALFtf5qTQw
https://www.youtube.com/watch?v=bTLki7ObAEk

All scenes in this video were filmed at Highland Mountain Bike Park in New Hampshire https://www.highlandmountain.com

Subscribe to Skills with Phil https://www.youtube.com/channe....l/UC0QuCui5pNF9k9fiN

#mtb #mountainbiking

An introductory lecture for MIT course 6.S094 on the basics of deep learning including a few key ideas, subfields, and the big picture of why neural networks have inspired and energized an entire new generation of researchers. For more lecture videos on deep learning, reinforcement learning (RL), artificial intelligence (AI & AGI), and podcast conversations, visit our website or follow TensorFlow code tutorials on our GitHub repo.

INFO:
Website: https://deeplearning.mit.edu
GitHub: https://github.com/lexfridman/mit-deep-learning
Slides: http://bit.ly/deep-learning-basics-slides
Playlist: http://bit.ly/deep-learning-playlist
Blog post: https://link.medium.com/TkE476jw2T

OUTLINE:
0:00 - Introduction
0:53 - Deep learning in one slide
4:55 - History of ideas and tools
9:43 - Simple example in TensorFlow
11:36 - TensorFlow in one slide
13:32 - Deep learning is representation learning
16:02 - Why deep learning (and why not)
22:00 - Challenges for supervised learning
38:27 - Key low-level concepts
46:15 - Higher-level methods
1:06:00 - Toward artificial general intelligence

CONNECT:
- If you enjoyed this video, please subscribe to this channel.
- Twitter: https://twitter.com/lexfridman
- LinkedIn: https://www.linkedin.com/in/lexfridman
- Facebook: https://www.facebook.com/lexfridman
- Instagram: https://www.instagram.com/lexfridman

A Google insider who anonymously leaked internal documents to Project Veritas made the decision to go public in an on-the-record video interview. The insider, Zachary Vorhies, decided to go public after receiving a letter from Google, and after he says Google allegedly called the police to perform a "wellness check" on him.

See the documents Zach leaked to Project Veritas in June: https://youtu.be/csP4z8dR6X0

Best Toy Learning Videos for Kids! Peppa Pig, Pororo, and Paw Patrol! In this educational preschool learning video for kids, let's learn colors, household objects, and more with some of the best toy characters around - Pororo the Little Penguin, Peppa Pig, and Paw Patrol! First, we'll join Pororo for a visit to his toy house, let's learn house hold item words for kids with this fun doll house toy! Then we'll play with Peppa Pig in her brand NEW giant toy house with four levels =). Finally, let's learn colors as Paw Patrol and the pups rescue Peppa Pig from a big, scary toy Dragon!



=======================================================
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=======================================================




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Here are some of our other fun kid & toddler learning videos by Genevieve's Playhouse:
Ball Pounding Bench Preschool Toys for Toddlers: http://tinyurl.com/jv8eyyh
Toddler, Genevieve, teaches Kids Alphabet: http://tinyurl.com/hqvwcs9
Cool Nesting Toy Cars for Kids: http://tinyurl.com/z4cyh6y
Fun Marble Mazes for Kids: https://youtu.be/YUswQ_kfrdk
Car Carrying Truck for Kids: http://tinyurl.com/j7akkon
Friendly Honey Bees Preschool Toy for Toddlers: http://tinyurl.com/guw9kxg
Fun Peg Pounding Bench Toy for Kids: http://tinyurl.com/hn4huyv
GIANT Best Marble Maze for Kids: http://tinyurl.com/hn4huyv

** Machine Learning Engineer Masters Program: https://www.edureka.co/masters....-program/machine-lea **
This Edureka Machine Learning Full Course video will help you understand and learn Machine Learning Algorithms in detail. This Machine Learning Tutorial is ideal for both beginners as well as professionals who want to master Machine Learning Algorithms. Below are the topics covered in this Machine Learning Tutorial for Beginners video:
2:47 What is Machine Learning?
4:08 AI vs ML vs Deep Learning
5:43 How does Machine Learning works?
6:18 Types of Machine Learning
6:43 Supervised Learning
8:38 Supervised Learning Examples
11:49 Unsupervised Learning
13:54 Unsupervised Learning Examples
16:09 Reinforcement Learning
18:39 Reinforcement Learning Examples
19:34 AI vs Machine Learning vs Deep Learning
22:09 Examples of AI
23:39 Examples of Machine Learning
25:04 What is Deep Learning?
25:54 Example of Deep Learning
27:29 Machine Learning vs Deep Learning
33:49 Jupyter Notebook Tutorial
34:49 Installation
50:24 Machine Learning Tutorial
51:04 Classification Algorithm
51:39 Anomaly Detection Algorithm
52:14 Clustering Algorithm
53:34 Regression Algorithm
54:14 Demo: Iris Dataset
1:12:11 Stats & Probability for Machine Learning
1:16:16 Categories of Data
1:16:36 Qualitative Data
1:17:51 Quantitative Data
1:20:55 What is Statistics?
1:23:25 Statistics Terminologies
1:24:30 Sampling Techniques
1:27:15 Random Sampling
1:28:05 Systematic Sampling
1:28:35 Stratified Sampling
1:29:35 Types of Statistics
1:32:21 Descriptive Statistics
1:37:36 Measures of Spread
1:44:01 Information Gain & Entropy
1:56:08 Confusion Matrix
2:00:53 Probability
2:03:19 Probability Terminologies
2:04:55 Types of Events
2:05:35 Probability of Distribution
2:10:45 Types of Probability
2:11:10 Marginal Probability
2:11:40 Joint Probability
2:12:35 Conditional Probability
2:13:30 Use-Case
2:17:25 Bayes Theorem
2:23:40 Inferential Statistics
2:24:00 Point Estimation
2:26:50 Interval Estimate
2:30:10 Margin of Error
2:34:20 Hypothesis Testing
2:41:25 Supervised Learning Algorithms
2:42:40 Regression
2:44:05 Linear vs Logistic Regression
2:49:55 Understanding Linear Regression Algorithm
3:11:10 Logistic Regression Curve
3:18:34 Titanic Data Analysis
3:58:39 Decision Tree
3:58:59 what is Classification?
4:01:24 Types of Classification
4:08:35 Decision Tree
4:14:20 Decision Tree Terminologies
4:18:05 Entropy
4:44:05 Credit Risk Detection Use-case
4:51:45 Random Forest
5:00:40 Random Forest Use-Cases
5:04:29 Random Forest Algorithm
5:16:44 KNN Algorithm
5:20:09 KNN Algorithm Working
5:27:24 KNN Demo
5:35:05 Naive Bayes
5:40:55 Naive Bayes Working
5:44:25Industrial Use of Naive Bayes
5:50:25 Types of Naive Bayes
5:51:25 Steps involved in Naive Bayes
5:52:05 PIMA Diabetic Test Use Case
6:04:55 Support Vector Machine
6:10:20 Non-Linear SVM
6:12:05 SVM Use-case
6:13:30 k Means Clustering & Association Rule Mining
6:16:33 Types of Clustering
6:17:34 K-Means Clustering
6:17:59 K-Means Working
6:21:54 Pros & Cons of K-Means Clustering
6:23:44 K-Means Demo
6:28:44 Hirechial Clustering
6:31:14 Association Rule Mining
6:34:04 Apriori Algorithm
6:39:19 Apriori Algorithm Demo
6:43:29 Reinforcement Learning
6:46:39 Reinforcement Learning: Counter-Strike Example
6:53:59 Markov's Decision Process
6:58:04 Q-Learning
7:02:39 The Bellman Equation
7:12:14 Transitioning to Q-Learning
7:17:29 Implementing Q-Learning
7:23:33 Machine Learning Projects
7:38:53 Who is a ML Engineer?
7:39:28 ML Engineer Job Trends
7:40:43 ML Engineer Salary Trends
7:42:33 ML Engineer Skills
7:44:08 ML Engineer Job Description
7:45:53 ML Engineer Resume
7:54:48 Machine Learning Interview Questions

--------------------------------------------------------------------------------------------------------

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Got a question on the topic? Please share it in the comment section below and our experts will answer it for you.

For more information, please write back to us at sales@edureka.in or call us at IND: 9606058406 / US: 18338555775 (toll-free).

Google DeepMind created an artificial intelligence program using deep reinforcement learning that plays Atari games and improves itself to a superhuman level. It is capable of playing many Atari games and uses a combination of deep artificial neural networks and reinforcement learning. After presenting their initial results with the algorithm, Google almost immediately acquired the company for several hundred million dollars, hence the name Google DeepMind. Please enjoy the footage and let me know if you have any questions regarding deep learning!

______________________

Recommended for you:
1. How DeepMind's AlphaGo Defeated Lee Sedol - https://www.youtube.com/watch?v=a-ovvd_ZrmA&index=58&list=PLujxSBD-JXgnqDD1n-V30pKtp6Q886x7e
2. How DeepMind Conquered Go With Deep Learning (AlphaGo) - https://www.youtube.com/watch?v=IFmj5M5Q5jg&index=42&list=PLujxSBD-JXgnqDD1n-V30pKtp6Q886x7e
3. Google DeepMind's Deep Q-Learning & Superhuman Atari Gameplays -
https://www.youtube.com/watch?v=Ih8EfvOzBOY&index=14&list=PLujxSBD-JXgnqDD1n-V30pKtp6Q886x7e

Subscribe if you would like to see more content like this: http://www.youtube.com/subscri....ption_center?add_use

- Original DeepMind code: https://sites.google.com/a/deepmind.com/dqn/

- Ilya Kuzovkin's fork with visualization:
https://github.com/kuz/DeepMin....d-Atari-Deep-Q-Learn

- This patch fixes the visualization when reloading a pre-trained network. The window will appear after the first evaluation batch is done (typically a few minutes):
http://cg.tuwien.ac.at/~zsolna....i/wp/wp-content/uplo

- This configuration file will run Ilya Kuzovkin's version with less than 1GB of VRAM:
http://cg.tuwien.ac.at/~zsolna....i/wp/wp-content/uplo

- The original Nature paper on this deep learning technique is available here:
http://www.nature.com/nature/j....ournal/v518/n7540/fu

- And some mirrors that are not behind a paywall:
http://www.cs.swarthmore.edu/~....meeden/cs63/s15/natu
http://diyhpl.us/~nmz787/pdf/H....uman-level_control_t

Web → https://cg.tuwien.ac.at/~zsolnai/
Twitter → https://twitter.com/karoly_zsolnai

Accelerated Learning - Focus Music - Gamma Waves for Focus, Concentration, Memory - Monaural Beats

Purchase this MP3: https://goo.gl/Jpk2zw

Magnetic Minds:

This video contains 40 Hz Gamma Monaural Beats.

Gamma Waves are associated with Higher Awareness states, such as those seen during Intensive Task Processing, or Tibetan monks meditating on the intent of Compassion.

The following frequencies are contained in this video:

40 Hz
Gamma Monaural Beats
Intellectual Acuity

6 Hz
Theta Binaural Beats
Memory Stimulation
Carrier Frequency: 126.1 Hz (Hyper-Gamma)

If you enjoy this video, please Like and Subscribe for weekly updates.

===== General Questions =====

Q. What are Binaural Beats?

"Binaural Beats" is a term given to playing one sound frequency in one ear, and another sound frequency in the opposite ear, creating a two-tone effect in the mid-brain that is actually perceived to be one tone. This causes an "Entrainment" effect in the brain that has a variety of results depending on the frequency.

Q. What are Binaural Beats good for?

Lots of things. Meditation, Relaxation, Stress Relief, Deeper Sleep, Pain Relief, Mind Expansion, Brain Hemisphere Synchronization, and the list goes on and on.

Pretty much any element of the Mind / Body complex can be improved using Binaural Beats, which again is just Brainwave Entrainment.

Q. Do Binaural Beats Actually Work?

Indeed. Many scientific studies (especially as of late) have conclusive research on Brainwave Entrainment and it's effects.

Q. Must I wear headphones for these videos?

You don't have to use headphones, but the Binaural effect is increased if you do.

Q. Do I need to close my eyes while listening to this?

No, although you'll find closing your eyes will generally lead to a deeper, more profound state while listening.

If you enjoy this video, please Like and Subscribe for weekly updates.

Can we predict the outcome of a football game given a dataset of past games? That's the question that we'll answer in this episode by using the scikit-learn machine learning library as our predictive tool.

Code for this video:
https://github.com/llSourcell/....Predicting_Winning_T

Please Subscribe! And like. And comment.

More learning resources:
https://arxiv.org/pdf/1511.05837.pdf
https://doctorspin.me/digital-....strategy/machine-lea
https://dashee87.github.io/foo....tball/python/predict
http://data-informed.com/predi....ct-winners-big-games
https://github.com/ihaque/fantasy
https://www.credera.com/blog/b....usiness-intelligence

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

And please support me on Patreon:
https://www.patreon.com/user?u=3191693
Follow me:
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Signup for my newsletter for exciting updates in the field of AI:
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Hit the Join button above to sign up to become a member of my channel for access to exclusive content!

This talk was given at a local TEDx event, produced independently of the TED Conferences. Blended Learning has become a catch phrase-- a buzzword for using technology in the classroom. The idea is used to describe the education system's response to the need for 21st century skills, a solution to the budget crisis in schools, and a beacon of hope for the transformation of education through individual interventions. In my talk I sharpen the idea of blended learning and offer some insight on potential successes and pitfalls of implementing a true blended model. Blended learning has enormous potential but it is not a panacea for the woes of education. Drawing on my experience in a blended classroom and as an administrator my goal is to contribute to the debate that is currently happening in our school districts, in our schools, and in our media over the incorporation of technology in education.

Monique is currently teaching in the Education Department at Ithaca College, training the newest generation of future teachers as they prepare to inspire their students. Education is her career and passion. Last year she was part of the founding team at Alpha: Blanca Alvarado Middle School, a blended learning charter school in San Jose which is integrating technology to innovate on the middle school curriculum. Prior to that she was an assistant principal, taught math and science, coached volleyball, and created an Outdoor Education Program for middle school students. Monique received her MEd in School Leadership from Harvard University and her BS in Human Development from Cornell University.

About TEDx, x = independently organized event 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)

In Lecture 14 we move from supervised learning to reinforcement learning (RL), in which an agent must learn to interact with an environment in order to maximize its reward. We formalize reinforcement learning using the language of Markov Decision Processes (MDPs), policies, value functions, and Q-Value functions. We discuss different algorithms for reinforcement learning including Q-Learning, policy gradients, and Actor-Critic. We show how deep reinforcement learning has been used to play Atari games and to achieve super-human Go performance in AlphaGo.

Keywords: Reinforcement learning, RL, Markov decision process, MDP, Q-Learning, policy gradients, REINFORCE, actor-critic, Atari games, AlphaGo

Slides: http://cs231n.stanford.edu/sli....des/2017/cs231n_2017

--------------------------------------------------------------------------------------

Convolutional Neural Networks for Visual Recognition

Instructors:
Fei-Fei Li: http://vision.stanford.edu/feifeili/
Justin Johnson: http://cs.stanford.edu/people/jcjohns/
Serena Yeung: http://ai.stanford.edu/~syyeung/

Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.

Website:
http://cs231n.stanford.edu/

For additional learning opportunities please visit:
http://online.stanford.edu/

Forecasts are critical in many fields, including finance, manufacturing, and meteorology. At Uber, probabilistic time series forecasting is essential for marketplace optimization, accurate hardware capacity predictions, marketing spend allocations, and real-time system outage detection across millions of metrics.

In this talk, Franziska Bell provides an overview of classical, machine learning and deep learning forecasting approaches. In addition fundamental forecasting best practices will be covered.

This video was recorded at QCon.ai 2018: https://bit.ly/2piRtLl

If you are a software engineer that wants to learn more about machine learning check our dedicated introductory guide https://bit.ly/2HPyuzY .

For more awesome presentations on innovator and early adopter, topics check InfoQ’s selection of talks from conferences worldwide http://bit.ly/2tm9loz

In this Disney Car Learning video for kids, let's learn colors and counting with color changing Disney Cars toys. It's the big race and Lightning McQueen, Mater, and their Disney Cars friends are ready to roll. In this educational preschool learning video we'll play with our color changing Disney cars, and learn counting as well. Which Disney Car do you think will win the big race? Watch and find out!



=======================================================
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https://amzn.to/2BvGRyS
https://amzn.to/2R5HaKz
https://amzn.to/2BBrWmS
=======================================================




=======================================================
Get Awesome Genevieve's Playhouse Shirts, Backpacks, and more:
https://www.genevievesplayhouse.com
And shop for some of our Favorite Toys:
https://amzn.to/2EHqsvk
https://amzn.to/2BvGRyS
https://amzn.to/2R5HaKz
https://amzn.to/2BBrWmS
=======================================================

Subscribe to Genevieve's Playhouse Here:
https://www.youtube.com/Genevi....evesPlayhouse?sub_co


Here are some of our other fun kid & toddler learning videos by Genevieve's Playhouse:
Ball Pounding Bench Preschool Toys for Toddlers: http://tinyurl.com/jv8eyyh
Toddler, Genevieve, teaches Kids Alphabet: http://tinyurl.com/hqvwcs9
Cool Nesting Toy Cars for Kids: http://tinyurl.com/z4cyh6y
Fun Marble Mazes for Kids: https://youtu.be/YUswQ_kfrdk
Car Carrying Truck for Kids: http://tinyurl.com/j7akkon
Friendly Honey Bees Preschool Toy for Toddlers: http://tinyurl.com/guw9kxg
Fun Peg Pounding Bench Toy for Kids: http://tinyurl.com/hn4huyv
GIANT Best Marble Maze for Kids: http://tinyurl.com/hn4huyv

Künstliche Intelligenz verändert unser Leben. Alles, fast was wir online tun wird heute schon von "Machine Learning" beeinflusst. Und dennoch wissen viele von uns gar nicht, wie das eigentlich genau funktioniert. Wie lernt eine künstliche Intelligenz? Um diese Frage zu beantworten müssen wir zuerst klären, wie wir Menschen lernen.

Vielen Dank an ZEISS und im besonderen Dr. Jascha Ulrich für die Unterstützung bei diesem Video.

Für das ZDF durfte ich eine kleine Doku auf dem Kanal von Terra X produzieren: https://youtu.be/qzCD0ICPWEQ

Auf dem Kanal der Elektroindustrie mache ich regelmäßig Videos und erkläre zum Beispiel, wie Bitcoin funktioniert: https://youtu.be/9HO6Mz3jDmw

Natürlich bin ich weiter Sprecher auf dem Kanal Schlaumal: https://youtu.be/uvcleXH_GF8

Und ich war mal wieder zu Besuch bei Phil's Physics: https://youtu.be/47Mo1puuzsg

Patreon: https://www.patreon.com/DoktorWhatson
Twitter: http://www.twitter.com/DoktorWhatson
Instagram: http://instagram.com/DoktorWhatson
Facebook: http://www.facebook.com/DoktorWhatson

Musik:
Novah – Sea of Clouds
https://www.youtube.com/user/NovahMedia
https://soundcloud.com/novahofficial

Alphabet Animals and more preschool learning songs collection. Learn phonics and the alphabet, colors, counting, animals and more! Lots of fun learning songs for young children. Sing along and learn with Bounce Patrol!

Download videos to watch offline with no ads:
http://bit.ly/Download-BP

Get the music:
iTunes: http://apple.co/2h6HZTt
Google Play: http://bit.ly/2hvz6iX
Spotify: http://spoti.fi/2z7m5Ux
Amazon Music: https://amzn.to/2LIemBK

For behind the scenes extras, parents can find us in these places:
Facebook: http://www.facebook.com/BouncePatrol
Twitter: http://www.twitter.com/BouncePatrol

Subscribe so you don't miss our new videos:
http://www.youtube.com/user/BouncePatrolKids

Bounce Patrol make original songs and nursery rhymes for kids - from toddlers and preschool, through to kindergarten and elementary school age.

Thanks for watching!

When 50,000 of Mark Rober's 3 million YouTube subscribers participated in a basic coding challenge, the data all pointed to what Rober has dubbed the Super Mario Effect. The YouTube star and former NASA engineer describes how this data-backed mindset for life gamification has stuck with him along his journey, and how it impacts the ways he helps (or tricks) his viewers into learning science, engineering, and design. Mark Rober has made a career out of engineering, entertainment, and education. After completing degrees in mechanical engineering from Brigham Young University and the University of Southern California, Rober joined NASA’s Jet Propulsion Laboratory in 2004. In his nine years as a NASA engineer, seven of which were on the Mars rover Curiosity team, Rober worked on both the Descent Stage (the jet pack that lowered the Rover to the surface) and some hardware on the Rover top deck for collecting samples. In 2011, Rober’s iPad-based Halloween costume helped launch both his creative costume company, Digital Dudz, and his YouTube channel, which now boasts 3 million subscribers and 400 million views. His videos focus on creative ideas and science- and engineering-based pranks and activities. Rober is a regular guest on "Jimmy Kimmel Live!". Today, he does research and development work for a large technology company in Northern California, where he lives with his wife and son. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx

Machine learning is all around us; on our phones, powering social networks, helping the police and doctors, scientists and mayors. But how does it work? In this animation we take a look at how statistics and computer science can be used to make machines that learn.

Visit www.oxfordsparks.ox.ac.uk to find out more.
Don’t forget to connect with us on Facebook @OxSparks and on Twitter @OxfordSparks Instagram: @OxfordSparks

Le deep learning, une technique qui révolutionne l'intelligence artificielle...et bientôt notre quotidien !

Le billet qui accompagne la vidéo : http://wp.me/p11Vwl-23E

Mon livre : http://science-etonnante.com/livre.html

Facebook : http://www.facebook.com/sciencetonnante
Twitter : http://www.twitter.com/dlouapre
Tipeee : http://www.tipeee.com/science-etonnante
Abonnez-vous : https://www.youtube.com/user/ScienceE...

La vidéo de Fei Fei Li à TED : https://www.ted.com/talks/fei_....fei_li_how_we_re_tea

La leçon inaugurale de Yann Le Cun au Collège de France : http://www.college-de-france.f....r/site/yann-lecun/in

Références :
==========
Russakovsky, Olga, et al. « Imagenet large scale visual recognition challenge. » International Journal of Computer Vision 115.3 (2015): 211-252. http://arxiv.org/pdf/1409.0575

Radford, Alec, Luke Metz, and Soumith Chintala. « Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. » arXiv preprint arXiv:1511.06434 (2015). http://arxiv.org/pdf/1511.06434

Zeiler, Matthew D., and Rob Fergus. « Visualizing and understanding convolutional networks. » Computer vision–ECCV 2014. Springer International Publishing, 2014. 818-833. http://arxiv.org/pdf/1311.2901

Vinyals, Oriol, et al. "Show and tell: A neural image caption generator." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015. http://arxiv.org/pdf/1411.4555.pdf

Artificial neural networks provide us incredibly powerful tools in machine learning that are useful for a variety of tasks ranging from image classification to voice translation. So what is all the deep learning rage about? The media seems to be all over the newest neural network research of the DeepMind company that was recently acquired by Google. They used neural networks to create algorithms that are able to play Atari games, learn them like a human would, eventually achieving superhuman performance.

Deep learning means that we use artificial neural network with multiple layers, making it even more powerful for more difficult tasks. These machine learning techniques proved to be useful for many tasks beyond image recognition: they also excel at weather predictions, breast cancer cell mitosis detection, brain image segmentation and toxicity prediction among many others.

In this episode, an intuitive explanation is given to show the inner workings of deep learning algorithms.

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Original blog post by Christopher Olah (source of many images):
http://colah.github.io/posts/2....014-03-NN-Manifolds-

You can train your own deep neural networks on Andrej Karpathy's website:
http://cs.stanford.edu/people/....karpathy/convnetjs/d

Images used in this video:
Bunny by Tomi Tapio K (CC BY 2.0) - https://flic.kr/p/8EbcEk
Train by B4bees (CC BY 2.0) - https://flic.kr/p/6RzHe4
Train with bunny by Alyssa L. Miller (CC BY 2.0) - https://flic.kr/p/5WPeRN
The knot theory blackboard image was created by Clayton Shonkwiler (CC BY 2.0) https://flic.kr/p/64FYv
The tangled knot image was created by Mikael Hvidtfeldt Christensen (CC BY 2.0) https://flic.kr/p/beYG9D

The thumbnail image is a work of Duncan Hull (CC BY 2.0) - https://flic.kr/p/98qtJB

Subscribe if you would like to see more of these! - http://www.youtube.com/subscri....ption_center?add_use

Splash screen/thumbnail design: Felícia Fehér - http://felicia.hu

Károly Zsolnai-Fehér's links:
Patreon → https://www.patreon.com/TwoMinutePapers
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Twitter → https://twitter.com/karoly_zsolnai
Web → https://cg.tuwien.ac.at/~zsolnai/

Machine learning provides us an incredible set of tools. If you have a difficult problem at hand, you don't need to hand craft an algorithm for it. It finds out by itself what is important about the problem and tries to solve it on its own. In this video, you'll see a number of incredible applications of different machine learning techniques (neural networks, deep learning, convolutional neural networks and more).

Note: the fluid simulation paper is using regression forests, which is a machine learning technique, but not strictly deep learning. There are variants of it that are though (e.g., Deep Neural Decision Forests).
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The paper "Toxicity Prediction using Deep Learning" and "Prediction of human population responses to toxic compounds by a collaborative competition" are available here:
http://arxiv.org/pdf/1503.01445.pdf
http://www.nature.com/nbt/jour....nal/v33/n9/full/nbt.

The paper "A Comparison of Algorithms and Humans For Mitosis Detection" is available here:
http://people.idsia.ch/~juerge....n/deeplearningwinsMI
http://people.idsia.ch/~ciresan/data/isbi2014.pdf

Kaggle-related things:
http://kaggle.com
https://www.kaggle.com/c/dato-native
http://blog.kaggle.com/2015/12..../03/dato-winners-int

The paper "Deep AutoRegressive Networks" is available here:
http://arxiv.org/pdf/1310.8499v2.pdf
https://www.youtube.com/watch?v=-yX1SYeDHbg&feature=youtu.be&t=2976

The furniture completion paper, "Data-driven Structural Priors for Shape Completion" is available here:
http://cs.stanford.edu/~mhsung..../projects/structure-

Data-driven fluid simulations using regression forests:
https://graphics.ethz.ch/~soba....rbar/papers/Lad15/Da
https://www.inf.ethz.ch/person....al/ladickyl/fluid_si

Selfies and convolutional neural networks:
http://karpathy.github.io/2015/10/25/selfie/

Multiagent Cooperation and Competition with Deep Reinforcement Learning:
http://arxiv.org/abs/1511.08779
https://www.youtube.com/watch?v=Gb9DprIgdGw&index=2&list=PLfLv_F3r0TwyaZPe50OOUx8tRf0HwdR_u
https://github.com/NeuroCSUT/D....eepMind-Atari-Deep-Q

Kaggle automatic essay scoring contest:
https://www.kaggle.com/c/asap-aes
http://www.vikparuchuri.com/bl....og/on-the-automated-

Great talks on Kaggle:
https://www.youtube.com/watch?v=9Zag7uhjdYo
https://www.youtube.com/watch?v=OKOlO9nIHUE
https://www.youtube.com/watch?v=R9QxucPzicQ

The thumbnail image was created by Barn Images - https://flic.kr/p/xxBc94

Subscribe if you would like to see more of these! - http://www.youtube.com/subscri....ption_center?add_use

Splash screen/thumbnail design: Felícia Fehér - http://felicia.hu

Károly Zsolnai-Fehér's links:
Patreon → https://www.patreon.com/TwoMinutePapers
Facebook → https://www.facebook.com/TwoMinutePapers/
Twitter → https://twitter.com/karoly_zsolnai
Web → https://cg.tuwien.ac.at/~zsolnai/

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