• How Machines Learn

    How do all the algorithms around us learn to do their jobs? Bot Wallpapers on Patreon: https://www.patreon.com/posts/15959388 Discuss this video: https://www.reddit.com/r/CGPGrey/comments/7klmd3/how_do_machines_learn/ Footnote: https://www.youtube.com/watch?v=wvWpdrfoEv0 Podcasts: https://www.youtube.com/user/HelloInternetPodcast https://www.youtube.com/channel/UCqoy014xOu7ICwgLWHd9BzQ Thank you to my supporters on Patreon: James Bissonette, James Gill, Cas Eliëns, Jeremy Banks, Thomas J Miller Jr MD, Jaclyn Cauley, David F Watson, Jay Edwards, Tianyu Ge, Michael Cao, Caron Hideg, Andrea Di Biagio, Andrey Chursin, Christopher Anthony, Richard Comish, Stephen W. Carson, JoJo Chehebar, Mark Govea, John Buchan, Donal Botkin, Bob Kunz https://www.patreon.com/cgpgrey How neural networks ...

    published: 18 Dec 2017
  • How To Program For Beginners | Episode 1: Algorithms

    This is the start to a new series, and I hope to teach you guys all the tricks and tips you need to becoming a successful programmer! If you're interested in more videos, and you want to continue to get better at programming, please subscribe for all future episodes!

    published: 30 May 2016
  • Brian Christian & Tom Griffiths: "Algorithms to Live By" | Talks at Google

    Practical, everyday advice which will easily provoke an interest in computer science. In a dazzlingly interdisciplinary work, acclaimed author Brian Christian and cognitive scientist Tom Griffiths show how the algorithms used by computers can also untangle very human questions. They explain how to have better hunches and when to leave things to chance, how to deal with overwhelming choices and how best to connect with others. From finding a spouse to finding a parking spot, from organizing one's inbox to understanding the workings of memory, Algorithms to Live By transforms the wisdom of computer science into strategies for human living. Brian Christian is the author of The Most Human Human, a Wall Street Journal bestseller, New York Times editors’ choice, and a New Yorker favorite book ...

    published: 12 May 2016
  • Algorithms: Graph Search, DFS and BFS

    Learn the basics of graph search and common operations; Depth First Search (DFS) and Breadth First Search (BFS). This video is a part of HackerRank's Cracking The Coding Interview Tutorial with Gayle Laakmann McDowell. http://www.hackerrank.com/domains/tutorials/cracking-the-coding-interview?utm_source=video&utm_medium=youtube&utm_campaign=ctci

    published: 27 Sep 2016
  • Sorting in Python || Learn Python Programming (Computer Science)

    Sorting is a fundamental task in software engineering. In Python, there are a variety of ways to sort lists, tuples, and other objects. Today we talk about the sort() method which is an in-place algorithm for sorting lists. We also cover the sorted() function which can be used on more objects, and creates a sorted copy, leaving the original object unchanged. We were able to make this Python video with the help of our Patrons on Patreon! We would like to recognize the generosity of our VIP Patrons Matt Peters, Andrew Mengede, Martin Stephens, and Markie Ward. Thank you so much for helping us continue our work! ➢➢➢➢➢➢➢➢➢➢ To​ ​help​ us continue making videos,​ ​you​ ​can​ ​support​ Socratica at: ​Patreon​: https://www.patreon.com/socratica Socratica Paypal: https://www.paypal.m...

    published: 08 Oct 2017
  • Gradient descent, how neural networks learn | Chapter 2, deep learning

    Subscribe for more (part 3 will be on backpropagation): http://3b1b.co/subscribe Thanks to everybody supporting on Patreon. https://www.patreon.com/3blue1brown http://3b1b.co/nn2-thanks For any early stage ML startup founders, Amplify Partners would love to hear from you via 3blue1brown@amplifypartners.com To learn more, I highly recommend the book by Michael Nielsen http://neuralnetworksanddeeplearning.com/ The book walks through the code behind the example in these videos, which you can find here: https://github.com/mnielsen/neural-networks-and-deep-learning MNIST database: http://yann.lecun.com/exdb/mnist/ Also check out Chris Olah's blog: http://colah.github.io/ His post on Neural networks and topology is particular beautiful, but honestly all of the stuff there is great. And if...

    published: 16 Oct 2017
  • Algorithm using Flowchart and Pseudo code Level 1 Flowchart

    Algorithm using Flowchart and Pseudo code Level 1 Flowchart By: Yusuf Shakeel http://www.dyclassroom.com/flowchart/introduction 0:05 Things we will learn 0:21 Level 0:28 Level 1 Flowchart 0:33 Important terms 0:37 Procedure 0:45 Algorithm 0:54 Flowchart 1:00 Pseudo code 1:08 Answer this simple question 1:14 How will you log into your facebook account 1:30 Next question 1:32 Write an algorithm to log into your facebook account 1:44 Algorithm to log in to facebook account in simple English 2:06 Writing Algorithm 2:14 Flowchart 2:16 There are 6 basic symbols that are commonly used in Flowchart 2:20 Terminal 2:27 Input/Output 2:35 Process 2:42 Decision 2:52 Connector 3:00 Control Flow 3:06 All the 6 symbols 3:13 Flowchart rules 3:25 Flowchart exercise 3:28 Add 10 and 20 4:00 Another exerci...

    published: 27 Aug 2013
  • 'The Algorithm' - How YouTube Search & Discovery Works

    Welcome to this series of videos on how YouTube's search & discovery system works. In this first installment, we talk about how our 'algorithm' follows the audience. WATCH THE NEXT VIDEO: https://goo.gl/SJiwDS GO TO THE LESSON: https://goo.gl/qV5PgY SUBSCRIBE: https://goo.gl/So4XIG With over 400 hours of video uploaded every minute, that can be a challenge. YouTube’s recommendation systems provide a real-time feedback loop to cater to each viewer and their varying interests. It learns from over 80 billion bits of feedback from the audience, daily, to understand how to serve the right videos to the right viewers at the right time. Our goal is to get people to watch more videos that they enjoy, so that they come back to YouTube regularly. Creators often ask, “What kind of videos does the ...

    published: 28 Aug 2017
  • Programming For Beginners | Episode 1: Algorithms

    I can't wait to continue this series! I hope you guys found this first episode useful. Please leave comments to tell me how I can improve this series, or if you have any questions! Thanks for watching, see ya next time! If you enjoyed the video please hit the "Like" button, leave a comment on what you want to see next, and hit the link below to Subscribe! ➤ Twitter: goo.gl/aUPMZD ➤ Subscribe: goo.gl/lQl7mw

    published: 30 May 2016
  • Your First ML App - Machine Learning for Hackers #1

    This video will get you up and running with your first ML app in just 7 lines of Python. The app will be able to recognize Iris flowers. I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ Follow the install instructions for TensorFlow here: https://www.tensorflow.org/versions/r0.8/get_started/os_setup.html#pip-installation Follow the install instructions for SciKit Learn here: http://scikit-learn.org/stable/install.html And here is a link to the repo for Skflow (the scikit interface for TensorFlow): https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/learn/python/learn My code sample is in the README of that repo under "Linear Classifier". Map of Machine Learning Models: http://www.wangbo.info/img/mlmindmap.png Map to pick the right ...

    published: 02 May 2016
  • CS50 2017 - Lecture 3 - Algorithms

    00:00:00 - Memory Overview 00:02:37 - Null Terminator 00:04:06 - initials.c 00:18:50 - Finding 50 00:27:00 - Linear Search 00:27:53 - Binary Search 00:28:51 - Sorting Blue Books 00:31:19 - Sorting Humans 00:32:51 - Human Bubble Sort 00:34:55 - Human Selection Sort 00:38:13 - Bubble Sort Pseudocode 00:41:52 - Selection Sort Pseudocode 00:43:12 - Insertion Sort Pseudocode 00:45:16 - Algorithmic Running Time 00:47:23 - Analyzing Bubble Sort 00:52:47 - Big-O Notation 00:55:27 - Omega Notation 00:57:43 - Theta Notation 00:59:03 - Visualizing Algorithms 01:03:03 - sigma0.c 01:06:11 - sigma1.c 01:12:20 - Merge Sort Pseudocode 01:14:19 - Merge Sort Step-by-Step 01:25:20 - Formalizing Merge Sort 01:27:57 - Visualizing Merge Sort 01:30:03 - Pset3 Teaser 01:34:38 - The Sounds of Algorithms

    published: 13 Oct 2017
  • Let’s Write a Decision Tree Classifier from Scratch - Machine Learning Recipes #8

    Hey everyone! Glad to be back! Decision Tree classifiers are intuitive, interpretable, and one of my favorite supervised learning algorithms. In this episode, I’ll walk you through writing a Decision Tree classifier from scratch, in pure Python. I’ll introduce concepts including Decision Tree Learning, Gini Impurity, and Information Gain. Then, we’ll code it all up. Understanding how to accomplish this was helpful to me when I studied Machine Learning for the first time, and I hope it will prove useful to you as well. You can find the code from this video here: https://goo.gl/UdZoNr https://goo.gl/ZpWYzt Books! Hands-On Machine Learning with Scikit-Learn and TensorFlow https://goo.gl/kM0anQ Follow Josh on Twitter: https://twitter.com/random_forests Check out more Machine Learning Rec...

    published: 13 Sep 2017
  • Algorithms and Tips You Need to know to Master EPLL

    Hope this video helped! Thanks for watching! Video idea I may or may not continue: Basically, you guys can film yourselves on any event official or unofficial, and you can send in a good solve for YOUR standards. Note: Good reactions will be targeted Then, each month maybe, or 2 months, I'll upload a 'best solves of the month'. Featuring your videos. What defines best? Just an interesting solve of yours, maybe an event your good at, maybe an official solve, and good reactions will be fun to watch. How to send in videos: Nothing fancy, just upload to youtube public or unlisted and private message me a link to it. Or if you really want, you can just send the link here in the comments. Now remember, I'm not sure I will continue this series, I just want to see how successful and entertainin...

    published: 06 Apr 2017
  • Super-Learning Study Aid | 'Accelerated Learning MK2' | Binaural Beats Focus & Concentration

    MP3 DOWNLOAD: http://www.binauralbeatshub.com/?product=accelerated-learning-mk2 ►High Quality MP3s: http://www.binauralbeatshub.com ►Subscribe to our channel and stay up to date on high quality brainwave entrainment audios! Become a part of our growing community on these social platforms: ►Facebook: https://www.facebook.com/binauralbrainwave ►Google +: https://plus.google.com/u/0/b/103041527589930535493/+BinauralBrainwave/posts?pageId=103041527589930535493 ►Twitter: https://twitter.com/Binaural_Beats1 Want to dramatically boost your academic performance? These frequencies have been shown to help optimise your study performance and actually REMEMBER what you've read! For OPTIMAL RESULTS, repeat your study 3 times within the 2 hour duration. Repetition has been shown to dramatically imp...

    published: 10 Jan 2014
  • Artificial Intelligence in Google's Dinosaur (English Sub)

    Link for the code: https://github.com/ivanseidel/IAMDinosaur This is a project made for my university, using a Neural Network and Genetic Algorithm to teach Google's dinosaur from Chrome to jump cactus without dying so easily. All the implementation was using Node.js, and the game was not modified to allow interaction with the game, instead, I used pixel readings and virtual key presses from Node.js. Presentation: I normally use Apple's Keynote to make presentations and record it livelly with my screen. Music: It's my own composition and improvisation. Link: https://soundcloud.com/ivan-seidel/at-night-with-headphones

    published: 27 Dec 2015
  • Predicting Stock Price Mathematically

    There are two prices that are critical for any investor to know: the current price of the investment he or she owns, or plans to own, and its future selling price. Despite this, investors are constantly reviewing past pricing history and using it to influence their future investment decisions. Some investors won't buy a stock or index that has risen too sharply, because they assume that it's due for a correction, while other investors avoid a falling stock, because they fear that it will continue to deteriorate. http://www.garguniversity.com Check out Ebook "Mind Math" from Dr. Garg https://www.amazon.com/MIND-MATH-Learn-Math-Fun-ebook/dp/B017QEIF18

    published: 07 Nov 2015
  • Java Programming

    Cheat Sheet is Here : http://goo.gl/OPMjte Slower Java Tutorial : http://goo.gl/UHdlyP How to Install Java & Eclipse : http://goo.gl/vEEEJE Best Java Book : http://amzn.to/2l27h2h Support Me on Patreon : https://www.patreon.com/derekbanas In this Java programming Tutorial I'll teach you all of the core knowledge needed to write Java code in 30 minutes. This is the most popular request from everyone. I specifically cover the following topics: primitive data types, comments, class, import, Scanner, final, Strings, static, private, protected, public, constructors, math, hasNextLine, nextLine, getters, setters, method overloading, Random, casting, toString, conversion from Strings to primitives, converting from primitives to Strings, if, else, else if, print, println, printf, logical ope...

    published: 03 Jun 2014
  • How to Figure Out the Day of the Week For Any Date Ever

    To learn more about Brilliant, go to https://brilliant.org/BeSmart/ and sign up for free. First 200 people will get 20% off the annual Premium subscription. ↓↓↓ More info and sources below ↓↓↓ You can be a human computer too. Our cheat sheet: http://bit.ly/2rftqkv Want to go NEXT LEVEL? Learn how to adjust for Julian calendar and BC dates You might think that computers are the only things that run algorithms, but you’re wrong. Here’s a neat mental trick for calculating the day of the week for any day ever, developed by famous mathematician John H. Conway Don’t miss our next video! SUBSCRIBE! ►► http://bit.ly/iotbs_sub READ MORE: https://en.wikipedia.org/wiki/Doomsday_rule Martin Gardner, "The Universe in a Handkerchief: Lewis Carroll's Mathematical Recreations, Games, Puzzles, ...

    published: 16 Jan 2018
  • But what *is* a Neural Network? | Chapter 1, deep learning

    Subscribe to stay notified about new videos: http://3b1b.co/subscribe Support more videos like this on Patreon: https://www.patreon.com/3blue1brown Special thanks to these supporters: http://3b1b.co/nn1-thanks For any early-stage ML entrepreneurs, Amplify Partners would love to hear from you: 3blue1brown@amplifypartners.com Full playlist: http://3b1b.co/neural-networks Typo correction: At 14:45, the last index on the bias vector is n, when it's supposed to in fact be a k. Thanks for the sharp eyes that caught that! For those who want to learn more, I highly recommend the book by Michael Nielsen introducing neural networks and deep learning: https://goo.gl/Zmczdy There are two neat things about this book. First, it's available for free, so consider joining me in making a donation Nie...

    published: 05 Oct 2017
  • What Makes a Good Feature? - Machine Learning Recipes #3

    Good features are informative, independent, and simple. In this episode, we'll introduce these concepts by using a histogram to visualize a feature from a toy dataset. Updates: many thanks for the supportive feedback! I’d love to release these episodes faster, but I’m writing them as we go. That way, I can see what works and (more importantly) where I can improve. We've covered a lot of ground already, so next episode I'll review and reinforce concepts, introduce clearer syntax, spend more time on testing, and continue building intuition for supervised learning. I also realize some folks had dependency bugs with Graphviz (my fault!). Moving forward, I won't use any libraries not already installed by Anaconda or Tensorflow. Last: my code in this cast is similar to these great examples...

    published: 27 Apr 2016
  • Quantum Computing - The Math of Intelligence #10

    Quantum Computing offers hope for computing progress as we approach the limits of transistor density on silicon hardware. We're going to talk about the theory behind them then build our own quantum algorithm using IBM's Quantum API! This is the last episode of this series. Code for this video: https://github.com/llSourcell/quantum_computing Noah's Winning code: https://github.com/NoahLidell/math-of-intelligence/tree/master/q_learning jhGitHub009's Runner Up code: https://github.com/jhGitHub009/Game_bot_DQN Please Subscribe! And like. And comment. That's what keeps me going. More learning resources: https://people.cs.umass.edu/~strubell/doc/quantum_tutorial.pdf https://physics.stackexchange.com/questions/3390/can-anybody-provide-a-simple-example-of-a-quantum-computer-algorithm http:/...

    published: 18 Aug 2017
  • Randomized algorithms (intro) | Journey into cryptography | Computer Science | Khan Academy

    How could random numbers speed up a decision algorithm? Watch the next lesson: https://www.khanacademy.org/computing/computer-science/cryptography/random-algorithms-probability/v/bayes-theorem-visualized?utm_source=YT&utm_medium=Desc&utm_campaign=computerscience Missed the previous lesson? https://www.khanacademy.org/computing/computer-science/cryptography/comp-number-theory/v/rsa-encryption-checkpoint?utm_source=YT&utm_medium=Desc&utm_campaign=computerscience Computer Science on Khan Academy: Learn select topics from computer science - algorithms (how we solve common problems in computer science and measure the efficiency of our solutions), cryptography (how we protect secret information), and information theory (how we encode and compress information). About Khan Academy: Khan Academ...

    published: 30 Apr 2014
  • Cognition: How Your Mind Can Amaze and Betray You - Crash Course Psychology #15

    You can directly support Crash Course at http://www.subbable.com/crashcourse Subscribe for as little as $0 to keep up with everything we're doing. Also, if you can afford to pay a little every month, it really helps us to continue producing great content. We used to think that the human brain was a lot like a computer; using logic to figure out complicated problems. It turns out, it's a lot more complex and, well, weird than that. In this episode of Crash Course Psychology, Hank discusses thinking & communication, solving problems, creating problems, and a few ideas about what our brains are doing up there. -- Table of Contents Thinking & Communicating 01:39:16 Solving Problems 03:21:03 Creating Problems 05:46:06 -- Want to find Crash Course elsewhere on the internet? Facebook - http...

    published: 19 May 2014
  • 12. Clustering

    MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: John Guttag Prof. Guttag discusses clustering. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu

    published: 19 May 2017
developed with YouTube
How Machines Learn

How Machines Learn

  • Order:
  • Duration: 8:55
  • Updated: 18 Dec 2017
  • views: 2153042
videos
How do all the algorithms around us learn to do their jobs? Bot Wallpapers on Patreon: https://www.patreon.com/posts/15959388 Discuss this video: https://www.reddit.com/r/CGPGrey/comments/7klmd3/how_do_machines_learn/ Footnote: https://www.youtube.com/watch?v=wvWpdrfoEv0 Podcasts: https://www.youtube.com/user/HelloInternetPodcast https://www.youtube.com/channel/UCqoy014xOu7ICwgLWHd9BzQ Thank you to my supporters on Patreon: James Bissonette, James Gill, Cas Eliëns, Jeremy Banks, Thomas J Miller Jr MD, Jaclyn Cauley, David F Watson, Jay Edwards, Tianyu Ge, Michael Cao, Caron Hideg, Andrea Di Biagio, Andrey Chursin, Christopher Anthony, Richard Comish, Stephen W. Carson, JoJo Chehebar, Mark Govea, John Buchan, Donal Botkin, Bob Kunz https://www.patreon.com/cgpgrey How neural networks really work with the real linear algebra: https://www.youtube.com/watch?v=aircAruvnKk Music by: http://www.davidreesmusic.com
https://wn.com/How_Machines_Learn
How To Program For Beginners | Episode 1: Algorithms

How To Program For Beginners | Episode 1: Algorithms

  • Order:
  • Duration: 24:10
  • Updated: 30 May 2016
  • views: 1553
videos
This is the start to a new series, and I hope to teach you guys all the tricks and tips you need to becoming a successful programmer! If you're interested in more videos, and you want to continue to get better at programming, please subscribe for all future episodes!
https://wn.com/How_To_Program_For_Beginners_|_Episode_1_Algorithms
Brian Christian & Tom Griffiths: "Algorithms to Live By" | Talks at Google

Brian Christian & Tom Griffiths: "Algorithms to Live By" | Talks at Google

  • Order:
  • Duration: 1:07:28
  • Updated: 12 May 2016
  • views: 59573
videos
Practical, everyday advice which will easily provoke an interest in computer science. In a dazzlingly interdisciplinary work, acclaimed author Brian Christian and cognitive scientist Tom Griffiths show how the algorithms used by computers can also untangle very human questions. They explain how to have better hunches and when to leave things to chance, how to deal with overwhelming choices and how best to connect with others. From finding a spouse to finding a parking spot, from organizing one's inbox to understanding the workings of memory, Algorithms to Live By transforms the wisdom of computer science into strategies for human living. Brian Christian is the author of The Most Human Human, a Wall Street Journal bestseller, New York Times editors’ choice, and a New Yorker favorite book of the year. His writing has appeared in The New Yorker, The Atlantic, Wired, The Wall Street Journal, The Guardian, and The Paris Review, as well as in scientific journals such as Cognitive Science, and has been translated into eleven languages. He lives in San Francisco. Tom Griffiths is a professor of psychology and cognitive science at UC Berkeley, where he directs the Computational Cognitive Science Lab. He has published more than 150 scientific papers on topics ranging from cognitive psychology to cultural evolution, and has received awards from the National Science Foundation, the Sloan Foundation, the American Psychological Association, and the Psychonomic Society, among others. He lives in Berkeley. On behalf of Talks at Google this talk was hosted by Boris Debic. eBook https://play.google.com/store/books/details/Brian_Christian_Algorithms_to_Live_By?id=yvaLCgAAQBAJ
https://wn.com/Brian_Christian_Tom_Griffiths_Algorithms_To_Live_By_|_Talks_At_Google
Algorithms: Graph Search, DFS and BFS

Algorithms: Graph Search, DFS and BFS

  • Order:
  • Duration: 11:49
  • Updated: 27 Sep 2016
  • views: 173490
videos
Learn the basics of graph search and common operations; Depth First Search (DFS) and Breadth First Search (BFS). This video is a part of HackerRank's Cracking The Coding Interview Tutorial with Gayle Laakmann McDowell. http://www.hackerrank.com/domains/tutorials/cracking-the-coding-interview?utm_source=video&utm_medium=youtube&utm_campaign=ctci
https://wn.com/Algorithms_Graph_Search,_Dfs_And_Bfs
Sorting in Python  ||  Learn Python Programming  (Computer Science)

Sorting in Python || Learn Python Programming (Computer Science)

  • Order:
  • Duration: 6:24
  • Updated: 08 Oct 2017
  • views: 18753
videos
Sorting is a fundamental task in software engineering. In Python, there are a variety of ways to sort lists, tuples, and other objects. Today we talk about the sort() method which is an in-place algorithm for sorting lists. We also cover the sorted() function which can be used on more objects, and creates a sorted copy, leaving the original object unchanged. We were able to make this Python video with the help of our Patrons on Patreon! We would like to recognize the generosity of our VIP Patrons Matt Peters, Andrew Mengede, Martin Stephens, and Markie Ward. Thank you so much for helping us continue our work! ➢➢➢➢➢➢➢➢➢➢ To​ ​help​ us continue making videos,​ ​you​ ​can​ ​support​ Socratica at: ​Patreon​: https://www.patreon.com/socratica Socratica Paypal: https://www.paypal.me/socratica We also accept Bitcoin! :) Our​ ​address​ ​is: 1EttYyGwJmpy9bLY2UcmEqMJuBfaZ1HdG9 Thank​ ​you!! ➢➢➢➢➢➢➢➢➢➢ If you’d like a reference book, we recommend “Python Cookbook, 3rd Edition” from O’Reilly: http://amzn.to/2sCNYlZ The Mythical Man Month - Essays on Software Engineering & Project Management http://amzn.to/2tYdNeP ➢➢➢➢➢➢➢➢➢➢ You​ ​can​ ​also​ ​follow​ ​Socratica​ ​on: -​ ​Twitter:​ ​@socratica -​ ​Instagram:​ ​@SocraticaStudios -​ ​Facebook:​ ​@SocraticaStudios ➢➢➢➢➢➢➢➢➢➢ Python instructor: Ulka Simone Mohanty (@ulkam on Twitter) Written & Produced by Michael Harrison (@mlh496 on Twitter)
https://wn.com/Sorting_In_Python_||_Learn_Python_Programming_(Computer_Science)
Gradient descent, how neural networks learn | Chapter 2, deep learning

Gradient descent, how neural networks learn | Chapter 2, deep learning

  • Order:
  • Duration: 21:01
  • Updated: 16 Oct 2017
  • views: 492856
videos
Subscribe for more (part 3 will be on backpropagation): http://3b1b.co/subscribe Thanks to everybody supporting on Patreon. https://www.patreon.com/3blue1brown http://3b1b.co/nn2-thanks For any early stage ML startup founders, Amplify Partners would love to hear from you via 3blue1brown@amplifypartners.com To learn more, I highly recommend the book by Michael Nielsen http://neuralnetworksanddeeplearning.com/ The book walks through the code behind the example in these videos, which you can find here: https://github.com/mnielsen/neural-networks-and-deep-learning MNIST database: http://yann.lecun.com/exdb/mnist/ Also check out Chris Olah's blog: http://colah.github.io/ His post on Neural networks and topology is particular beautiful, but honestly all of the stuff there is great. And if you like that, you'll *love* the publications at distill: https://distill.pub/ For more videos, Welch Labs also has some great series on machine learning: https://youtu.be/i8D90DkCLhI https://youtu.be/bxe2T-V8XRs "But I've already voraciously consumed Nielsen's, Olah's and Welch's works", I hear you say. Well well, look at you then. That being the case, I might recommend that you continue on with the book "Deep Learning" by Goodfellow, Bengio, and Courville. Thanks to Lisha Li (@lishali88) for her contributions at the end, and for letting me pick her brain so much about the material. Here are the articles she referenced at the end: https://arxiv.org/abs/1611.03530 https://arxiv.org/abs/1706.05394 https://arxiv.org/abs/1412.0233 Music by Vincent Rubinetti: https://soundcloud.com/vincerubinetti/ ------------------ 3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted on new videos, subscribe, and click the bell to receive notifications (if you're into that). If you are new to this channel and want to see more, a good place to start is this playlist: http://3b1b.co/recommended Various social media stuffs: Website: https://www.3blue1brown.com Twitter: https://twitter.com/3Blue1Brown Patreon: https://patreon.com/3blue1brown Facebook: https://www.facebook.com/3blue1brown Reddit: https://www.reddit.com/r/3Blue1Brown
https://wn.com/Gradient_Descent,_How_Neural_Networks_Learn_|_Chapter_2,_Deep_Learning
Algorithm using Flowchart and Pseudo code Level 1 Flowchart

Algorithm using Flowchart and Pseudo code Level 1 Flowchart

  • Order:
  • Duration: 5:41
  • Updated: 27 Aug 2013
  • views: 409332
videos
Algorithm using Flowchart and Pseudo code Level 1 Flowchart By: Yusuf Shakeel http://www.dyclassroom.com/flowchart/introduction 0:05 Things we will learn 0:21 Level 0:28 Level 1 Flowchart 0:33 Important terms 0:37 Procedure 0:45 Algorithm 0:54 Flowchart 1:00 Pseudo code 1:08 Answer this simple question 1:14 How will you log into your facebook account 1:30 Next question 1:32 Write an algorithm to log into your facebook account 1:44 Algorithm to log in to facebook account in simple English 2:06 Writing Algorithm 2:14 Flowchart 2:16 There are 6 basic symbols that are commonly used in Flowchart 2:20 Terminal 2:27 Input/Output 2:35 Process 2:42 Decision 2:52 Connector 3:00 Control Flow 3:06 All the 6 symbols 3:13 Flowchart rules 3:25 Flowchart exercise 3:28 Add 10 and 20 4:00 Another exercise 4:03 Find the sum of 5 numbers 4:34 Another exercise 4:35 Print Hello World 10 times 5:06 Another exercise 5:07 Draw a flowchart to log in to facebook account 5:26 Note! End of Level 1 Related Videos Algorithm Flowchart and Pseudo code Level 1 Flowchart http://youtu.be/vOEN65nm4YU Level 2 Important Programming Concepts http://youtu.be/kwA3M8YxNk4 Level 3 Pseudo code http://youtu.be/r1BpraNa2Zc
https://wn.com/Algorithm_Using_Flowchart_And_Pseudo_Code_Level_1_Flowchart
'The Algorithm' - How YouTube Search & Discovery Works

'The Algorithm' - How YouTube Search & Discovery Works

  • Order:
  • Duration: 2:02
  • Updated: 28 Aug 2017
  • views: 188912
videos
Welcome to this series of videos on how YouTube's search & discovery system works. In this first installment, we talk about how our 'algorithm' follows the audience. WATCH THE NEXT VIDEO: https://goo.gl/SJiwDS GO TO THE LESSON: https://goo.gl/qV5PgY SUBSCRIBE: https://goo.gl/So4XIG With over 400 hours of video uploaded every minute, that can be a challenge. YouTube’s recommendation systems provide a real-time feedback loop to cater to each viewer and their varying interests. It learns from over 80 billion bits of feedback from the audience, daily, to understand how to serve the right videos to the right viewers at the right time. Our goal is to get people to watch more videos that they enjoy, so that they come back to YouTube regularly. Creators often ask, “What kind of videos does the algorithm like most?” Our systems have no opinion about what type of video you make, and doesn’t favor any particular format. Rather, it tries its best to follow the audience by paying attention to things like: • what they watch • what they don’t watch • how much time they spend watching • likes and dislikes • ‘not interested’ feedback Instead of worrying about what the algorithm likes, it’s better to focus on what your audience likes instead. If you do that and people watch, the algorithm will follow. So, which videos do they enjoy most? How often do they like to watch your channel? Check your YouTube Analytics to answer these questions. Whether you’re pursuing a passion or a business, we strive to give every video a chance to reach its potential audience. We realize however that YouTube has a lot of features, and it can be easy to get confused. Keep watching to learn about six key places where your videos appear, and what you can do to improve your chances for success: Search, Suggested Videos, Home, Trending, Subscriptions, and Notifications, in no particular order. - Level up your YouTube skills with Creator Academy lessons: http://goo.gl/E9umlU - See index of all lessons: http://goo.gl/x2h1NG - Get how-to step-by-step help: http://goo.gl/fBzr7
https://wn.com/'The_Algorithm'_How_Youtube_Search_Discovery_Works
Programming For Beginners | Episode 1: Algorithms

Programming For Beginners | Episode 1: Algorithms

  • Order:
  • Duration: 24:10
  • Updated: 30 May 2016
  • views: 18
videos
I can't wait to continue this series! I hope you guys found this first episode useful. Please leave comments to tell me how I can improve this series, or if you have any questions! Thanks for watching, see ya next time! If you enjoyed the video please hit the "Like" button, leave a comment on what you want to see next, and hit the link below to Subscribe! ➤ Twitter: goo.gl/aUPMZD ➤ Subscribe: goo.gl/lQl7mw
https://wn.com/Programming_For_Beginners_|_Episode_1_Algorithms
Your First ML App - Machine Learning for Hackers #1

Your First ML App - Machine Learning for Hackers #1

  • Order:
  • Duration: 4:30
  • Updated: 02 May 2016
  • views: 111919
videos
This video will get you up and running with your first ML app in just 7 lines of Python. The app will be able to recognize Iris flowers. I created a Slack channel for us, sign up here: https://wizards.herokuapp.com/ Follow the install instructions for TensorFlow here: https://www.tensorflow.org/versions/r0.8/get_started/os_setup.html#pip-installation Follow the install instructions for SciKit Learn here: http://scikit-learn.org/stable/install.html And here is a link to the repo for Skflow (the scikit interface for TensorFlow): https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/learn/python/learn My code sample is in the README of that repo under "Linear Classifier". Map of Machine Learning Models: http://www.wangbo.info/img/mlmindmap.png Map to pick the right model from SciKit Learn (although this doesn't take into account deep neural nets [just think -- lots of data? Just go with the DNN]): http://1.bp.blogspot.com/-ME24ePzpzIM/UQLWTwurfXI/AAAAAAAAANw/W3EETIroA80/s1600/drop_shadows_background.png This is the first in my new application-focused machine learning series. The goal is to avoid anything math-heavy and focus on building things with machine learning libraries. I recently created a Patreon page. If you like my videos, feel free to help support my effort here!: https://www.patreon.com/user?ty=h&u=3191693 Much more to come so please subscribe, like, and comment. That stuff is what encourages me to continue! Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/
https://wn.com/Your_First_Ml_App_Machine_Learning_For_Hackers_1
CS50 2017 - Lecture  3 - Algorithms

CS50 2017 - Lecture 3 - Algorithms

  • Order:
  • Duration: 1:36:56
  • Updated: 13 Oct 2017
  • views: 23953
videos
00:00:00 - Memory Overview 00:02:37 - Null Terminator 00:04:06 - initials.c 00:18:50 - Finding 50 00:27:00 - Linear Search 00:27:53 - Binary Search 00:28:51 - Sorting Blue Books 00:31:19 - Sorting Humans 00:32:51 - Human Bubble Sort 00:34:55 - Human Selection Sort 00:38:13 - Bubble Sort Pseudocode 00:41:52 - Selection Sort Pseudocode 00:43:12 - Insertion Sort Pseudocode 00:45:16 - Algorithmic Running Time 00:47:23 - Analyzing Bubble Sort 00:52:47 - Big-O Notation 00:55:27 - Omega Notation 00:57:43 - Theta Notation 00:59:03 - Visualizing Algorithms 01:03:03 - sigma0.c 01:06:11 - sigma1.c 01:12:20 - Merge Sort Pseudocode 01:14:19 - Merge Sort Step-by-Step 01:25:20 - Formalizing Merge Sort 01:27:57 - Visualizing Merge Sort 01:30:03 - Pset3 Teaser 01:34:38 - The Sounds of Algorithms
https://wn.com/Cs50_2017_Lecture_3_Algorithms
Let’s Write a Decision Tree Classifier from Scratch - Machine Learning Recipes #8

Let’s Write a Decision Tree Classifier from Scratch - Machine Learning Recipes #8

  • Order:
  • Duration: 9:53
  • Updated: 13 Sep 2017
  • views: 60934
videos
Hey everyone! Glad to be back! Decision Tree classifiers are intuitive, interpretable, and one of my favorite supervised learning algorithms. In this episode, I’ll walk you through writing a Decision Tree classifier from scratch, in pure Python. I’ll introduce concepts including Decision Tree Learning, Gini Impurity, and Information Gain. Then, we’ll code it all up. Understanding how to accomplish this was helpful to me when I studied Machine Learning for the first time, and I hope it will prove useful to you as well. You can find the code from this video here: https://goo.gl/UdZoNr https://goo.gl/ZpWYzt Books! Hands-On Machine Learning with Scikit-Learn and TensorFlow https://goo.gl/kM0anQ Follow Josh on Twitter: https://twitter.com/random_forests Check out more Machine Learning Recipes here: https://goo.gl/KewA03 Subscribe to the Google Developers channel: http://goo.gl/mQyv5L
https://wn.com/Let’S_Write_A_Decision_Tree_Classifier_From_Scratch_Machine_Learning_Recipes_8
Algorithms and Tips You Need to know to Master EPLL

Algorithms and Tips You Need to know to Master EPLL

  • Order:
  • Duration: 4:12
  • Updated: 06 Apr 2017
  • views: 3055
videos
Hope this video helped! Thanks for watching! Video idea I may or may not continue: Basically, you guys can film yourselves on any event official or unofficial, and you can send in a good solve for YOUR standards. Note: Good reactions will be targeted Then, each month maybe, or 2 months, I'll upload a 'best solves of the month'. Featuring your videos. What defines best? Just an interesting solve of yours, maybe an event your good at, maybe an official solve, and good reactions will be fun to watch. How to send in videos: Nothing fancy, just upload to youtube public or unlisted and private message me a link to it. Or if you really want, you can just send the link here in the comments. Now remember, I'm not sure I will continue this series, I just want to see how successful and entertaining it is. Also, my PBs! (Let me know if you can't access them) https://docs.google.com/spreadsheets/d/1-_G72PqdH3o4V3UpeWQLaoPLd6M5G_BxlY7iB03-rg8/edit#gid=0
https://wn.com/Algorithms_And_Tips_You_Need_To_Know_To_Master_Epll
Super-Learning Study Aid | 'Accelerated Learning MK2' | Binaural Beats Focus & Concentration

Super-Learning Study Aid | 'Accelerated Learning MK2' | Binaural Beats Focus & Concentration

  • Order:
  • Duration: 1:59:39
  • Updated: 10 Jan 2014
  • views: 405826
videos
MP3 DOWNLOAD: http://www.binauralbeatshub.com/?product=accelerated-learning-mk2 ►High Quality MP3s: http://www.binauralbeatshub.com ►Subscribe to our channel and stay up to date on high quality brainwave entrainment audios! Become a part of our growing community on these social platforms: ►Facebook: https://www.facebook.com/binauralbrainwave ►Google +: https://plus.google.com/u/0/b/103041527589930535493/+BinauralBrainwave/posts?pageId=103041527589930535493 ►Twitter: https://twitter.com/Binaural_Beats1 Want to dramatically boost your academic performance? These frequencies have been shown to help optimise your study performance and actually REMEMBER what you've read! For OPTIMAL RESULTS, repeat your study 3 times within the 2 hour duration. Repetition has been shown to dramatically improve retention of large amounts of information! My Facebook page: https://www.facebook.com/binauralbrainwave Use headphones/earbuds and begin at '0' volume. Slowly raise the meter until you reach a comfortable level. ****Loud levels are not necessary to experience the benefits of entrainment.**** If listening for extended periods, take frequent breaks The first hour is closely based on the original version. The frequencies range from theta (4hz) to very low beta (14hz); these are conductive to a mindset which absorbs and retains information during the study period. The second half of this study aid contains a similar step sequence which also ends at low beta, but this time begins at 9hz instead of 4, rising slowly by 1hz every 10 mins or so. There is a short 5 minute segment of pink noise half way through the audio. You can either continue studying through this, or take a short break. Try alternating with this study aid: https://www.youtube.com/watch?v=w4w-Nac5_Xk (Spaced Learning) or this one: https://www.youtube.com/watch?v=jexSg29hpo8 (Gamma Study Aid) Speed Reading: https://www.youtube.com/watch?v=KHbJi8qai50 MK3: https://www.youtube.com/watch?v=Ap4T1Mxj2Nw Deep Sleep Programming For Memory: https://www.youtube.com/watch?v=Bj8Whtjlhhs information retention, improve memory, retain information, study music, focus, concentration
https://wn.com/Super_Learning_Study_Aid_|_'Accelerated_Learning_Mk2'_|_Binaural_Beats_Focus_Concentration
Artificial Intelligence in Google's Dinosaur (English Sub)

Artificial Intelligence in Google's Dinosaur (English Sub)

  • Order:
  • Duration: 31:23
  • Updated: 27 Dec 2015
  • views: 1041379
videos
Link for the code: https://github.com/ivanseidel/IAMDinosaur This is a project made for my university, using a Neural Network and Genetic Algorithm to teach Google's dinosaur from Chrome to jump cactus without dying so easily. All the implementation was using Node.js, and the game was not modified to allow interaction with the game, instead, I used pixel readings and virtual key presses from Node.js. Presentation: I normally use Apple's Keynote to make presentations and record it livelly with my screen. Music: It's my own composition and improvisation. Link: https://soundcloud.com/ivan-seidel/at-night-with-headphones
https://wn.com/Artificial_Intelligence_In_Google's_Dinosaur_(English_Sub)
Predicting Stock Price Mathematically

Predicting Stock Price Mathematically

  • Order:
  • Duration: 11:33
  • Updated: 07 Nov 2015
  • views: 93180
videos
There are two prices that are critical for any investor to know: the current price of the investment he or she owns, or plans to own, and its future selling price. Despite this, investors are constantly reviewing past pricing history and using it to influence their future investment decisions. Some investors won't buy a stock or index that has risen too sharply, because they assume that it's due for a correction, while other investors avoid a falling stock, because they fear that it will continue to deteriorate. http://www.garguniversity.com Check out Ebook "Mind Math" from Dr. Garg https://www.amazon.com/MIND-MATH-Learn-Math-Fun-ebook/dp/B017QEIF18
https://wn.com/Predicting_Stock_Price_Mathematically
Java Programming

Java Programming

  • Order:
  • Duration: 34:30
  • Updated: 03 Jun 2014
  • views: 3246454
videos
Cheat Sheet is Here : http://goo.gl/OPMjte Slower Java Tutorial : http://goo.gl/UHdlyP How to Install Java & Eclipse : http://goo.gl/vEEEJE Best Java Book : http://amzn.to/2l27h2h Support Me on Patreon : https://www.patreon.com/derekbanas In this Java programming Tutorial I'll teach you all of the core knowledge needed to write Java code in 30 minutes. This is the most popular request from everyone. I specifically cover the following topics: primitive data types, comments, class, import, Scanner, final, Strings, static, private, protected, public, constructors, math, hasNextLine, nextLine, getters, setters, method overloading, Random, casting, toString, conversion from Strings to primitives, converting from primitives to Strings, if, else, else if, print, println, printf, logical operators, comparison operators, ternary operator, switch, for, while, break, continue, do while, polymorphism, arrays, for each, multidimensional arrays and more.
https://wn.com/Java_Programming
How to Figure Out the Day of the Week For Any Date Ever

How to Figure Out the Day of the Week For Any Date Ever

  • Order:
  • Duration: 7:53
  • Updated: 16 Jan 2018
  • views: 328909
videos
To learn more about Brilliant, go to https://brilliant.org/BeSmart/ and sign up for free. First 200 people will get 20% off the annual Premium subscription. ↓↓↓ More info and sources below ↓↓↓ You can be a human computer too. Our cheat sheet: http://bit.ly/2rftqkv Want to go NEXT LEVEL? Learn how to adjust for Julian calendar and BC dates You might think that computers are the only things that run algorithms, but you’re wrong. Here’s a neat mental trick for calculating the day of the week for any day ever, developed by famous mathematician John H. Conway Don’t miss our next video! SUBSCRIBE! ►► http://bit.ly/iotbs_sub READ MORE: https://en.wikipedia.org/wiki/Doomsday_rule Martin Gardner, "The Universe in a Handkerchief: Lewis Carroll's Mathematical Recreations, Games, Puzzles, and Word Plays" ----------- FOLLOW US: Merch: https://store.dftba.com/collections/its-okay-to-be-smart Facebook: http://www.facebook.com/itsokaytobesmart Twitter:@DrJoeHanson @okaytobesmart Tumblr: http://www.itsokaytobesmart.com Instagram: @DrJoeHanson ----------- It’s Okay To Be Smart is hosted by Joe Hanson, Ph.D. Director: Joe Nicolosi Writer: Joe Hanson, Ph.D. Producer/editor/animator: Jordan Husmann Producer: Stephanie Noone and Amanda Fox Produced by PBS Digital Studios Music via APM Stock images from Shutterstock http://www.shutterstock.com ------
https://wn.com/How_To_Figure_Out_The_Day_Of_The_Week_For_Any_Date_Ever
But what *is* a Neural Network? | Chapter 1, deep learning

But what *is* a Neural Network? | Chapter 1, deep learning

  • Order:
  • Duration: 19:13
  • Updated: 05 Oct 2017
  • views: 1019259
videos
Subscribe to stay notified about new videos: http://3b1b.co/subscribe Support more videos like this on Patreon: https://www.patreon.com/3blue1brown Special thanks to these supporters: http://3b1b.co/nn1-thanks For any early-stage ML entrepreneurs, Amplify Partners would love to hear from you: 3blue1brown@amplifypartners.com Full playlist: http://3b1b.co/neural-networks Typo correction: At 14:45, the last index on the bias vector is n, when it's supposed to in fact be a k. Thanks for the sharp eyes that caught that! For those who want to learn more, I highly recommend the book by Michael Nielsen introducing neural networks and deep learning: https://goo.gl/Zmczdy There are two neat things about this book. First, it's available for free, so consider joining me in making a donation Nielsen's way if you get something out of it. And second, it's centered around walking through some code and data which you can download yourself, and which covers the same example that I introduce in this video. Yay for active learning! https://github.com/mnielsen/neural-networks-and-deep-learning I also highly recommend Chris Olah's blog: http://colah.github.io/ For more videos, Welch Labs also has some great series on machine learning: https://youtu.be/i8D90DkCLhI https://youtu.be/bxe2T-V8XRs For those of you looking to go *even* deeper, check out the text "Deep Learning" by Goodfellow, Bengio, and Courville. Also, the publication Distill is just utterly beautiful: https://distill.pub/ Lion photo by Kevin Pluck Music by Vincent Rubinetti: https://soundcloud.com/vincerubinetti/ ------------------ 3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted on new videos, subscribe, and click the bell to receive notifications (if you're into that). If you are new to this channel and want to see more, a good place to start is this playlist: http://3b1b.co/recommended Various social media stuffs: Website: https://www.3blue1brown.com Twitter: https://twitter.com/3Blue1Brown Patreon: https://patreon.com/3blue1brown Facebook: https://www.facebook.com/3blue1brown Reddit: https://www.reddit.com/r/3Blue1Brown
https://wn.com/But_What_Is_A_Neural_Network_|_Chapter_1,_Deep_Learning
What Makes a Good Feature? - Machine Learning Recipes #3

What Makes a Good Feature? - Machine Learning Recipes #3

  • Order:
  • Duration: 5:41
  • Updated: 27 Apr 2016
  • views: 252915
videos
Good features are informative, independent, and simple. In this episode, we'll introduce these concepts by using a histogram to visualize a feature from a toy dataset. Updates: many thanks for the supportive feedback! I’d love to release these episodes faster, but I’m writing them as we go. That way, I can see what works and (more importantly) where I can improve. We've covered a lot of ground already, so next episode I'll review and reinforce concepts, introduce clearer syntax, spend more time on testing, and continue building intuition for supervised learning. I also realize some folks had dependency bugs with Graphviz (my fault!). Moving forward, I won't use any libraries not already installed by Anaconda or Tensorflow. Last: my code in this cast is similar to these great examples. You can use them to produce a more polished chart, if you like: http://matplotlib.org/examples/statistics/histogram_demo_multihist.html Follow https://twitter.com/random_forests for updates on new episodes! Subscribe to the Google Developers: http://goo.gl/mQyv5L - Subscribe to the brand new Firebase Channel: https://goo.gl/9giPHG And here's our playlist: https://goo.gl/KewA03
https://wn.com/What_Makes_A_Good_Feature_Machine_Learning_Recipes_3
Quantum Computing - The Math of Intelligence #10

Quantum Computing - The Math of Intelligence #10

  • Order:
  • Duration: 9:58
  • Updated: 18 Aug 2017
  • views: 38270
videos
Quantum Computing offers hope for computing progress as we approach the limits of transistor density on silicon hardware. We're going to talk about the theory behind them then build our own quantum algorithm using IBM's Quantum API! This is the last episode of this series. Code for this video: https://github.com/llSourcell/quantum_computing Noah's Winning code: https://github.com/NoahLidell/math-of-intelligence/tree/master/q_learning jhGitHub009's Runner Up code: https://github.com/jhGitHub009/Game_bot_DQN Please Subscribe! And like. And comment. That's what keeps me going. More learning resources: https://people.cs.umass.edu/~strubell/doc/quantum_tutorial.pdf https://physics.stackexchange.com/questions/3390/can-anybody-provide-a-simple-example-of-a-quantum-computer-algorithm http://michaelnielsen.org/blog/quantum-computing-for-everyone/ https://www.dwavesys.com/tutorials/background-reading-series/quantum-computing-primer http://www.quantumplayground.net/#/home https://www.research.ibm.com/ibm-q/ Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Special thanks to TED & Kurzgesagt for the animation clips Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/
https://wn.com/Quantum_Computing_The_Math_Of_Intelligence_10
Randomized algorithms (intro) | Journey into cryptography | Computer Science | Khan Academy

Randomized algorithms (intro) | Journey into cryptography | Computer Science | Khan Academy

  • Order:
  • Duration: 9:23
  • Updated: 30 Apr 2014
  • views: 30166
videos
How could random numbers speed up a decision algorithm? Watch the next lesson: https://www.khanacademy.org/computing/computer-science/cryptography/random-algorithms-probability/v/bayes-theorem-visualized?utm_source=YT&utm_medium=Desc&utm_campaign=computerscience Missed the previous lesson? https://www.khanacademy.org/computing/computer-science/cryptography/comp-number-theory/v/rsa-encryption-checkpoint?utm_source=YT&utm_medium=Desc&utm_campaign=computerscience Computer Science on Khan Academy: Learn select topics from computer science - algorithms (how we solve common problems in computer science and measure the efficiency of our solutions), cryptography (how we protect secret information), and information theory (how we encode and compress information). About Khan Academy: Khan Academy is a nonprofit with a mission to provide a free, world-class education for anyone, anywhere. We believe learners of all ages should have unlimited access to free educational content they can master at their own pace. We use intelligent software, deep data analytics and intuitive user interfaces to help students and teachers around the world. Our resources cover preschool through early college education, including math, biology, chemistry, physics, economics, finance, history, grammar and more. We offer free personalized SAT test prep in partnership with the test developer, the College Board. Khan Academy has been translated into dozens of languages, and 100 million people use our platform worldwide every year. For more information, visit www.khanacademy.org, join us on Facebook or follow us on Twitter at @khanacademy. And remember, you can learn anything. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to Khan Academy’s Computer Science channel: https://www.youtube.com/channel/UC8uHgAVBOy5h1fDsjQghWCw?sub_confirmation=1 Subscribe to Khan Academy: https://www.youtube.com/subscription_center?add_user=khanacademy
https://wn.com/Randomized_Algorithms_(Intro)_|_Journey_Into_Cryptography_|_Computer_Science_|_Khan_Academy
Cognition: How Your Mind Can Amaze and Betray You - Crash Course Psychology #15

Cognition: How Your Mind Can Amaze and Betray You - Crash Course Psychology #15

  • Order:
  • Duration: 10:42
  • Updated: 19 May 2014
  • views: 1363306
videos
You can directly support Crash Course at http://www.subbable.com/crashcourse Subscribe for as little as $0 to keep up with everything we're doing. Also, if you can afford to pay a little every month, it really helps us to continue producing great content. We used to think that the human brain was a lot like a computer; using logic to figure out complicated problems. It turns out, it's a lot more complex and, well, weird than that. In this episode of Crash Course Psychology, Hank discusses thinking & communication, solving problems, creating problems, and a few ideas about what our brains are doing up there. -- Table of Contents Thinking & Communicating 01:39:16 Solving Problems 03:21:03 Creating Problems 05:46:06 -- Want to find Crash Course elsewhere on the internet? Facebook - http://www.facebook.com/YouTubeCrashCourse Twitter - http://www.twitter.com/TheCrashCourse Tumblr - http://thecrashcourse.tumblr.com Support CrashCourse on Subbable: http://subbable.com/crashcourse
https://wn.com/Cognition_How_Your_Mind_Can_Amaze_And_Betray_You_Crash_Course_Psychology_15
12. Clustering

12. Clustering

  • Order:
  • Duration: 50:40
  • Updated: 19 May 2017
  • views: 24939
videos
MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: John Guttag Prof. Guttag discusses clustering. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
https://wn.com/12._Clustering