• 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
  • Art of Quitting | Pawan Kumar | TEDxDSCE

    Pawan Kumar says "Quitting is OK". He explains this through the steps of his life as to how he quit, got on and moved at the right stage. Like quitting college to do theatre, then quitting his mentor which was a bold move in his life to do theatre on his own, then quitting his home space to learn more. He then moved to Mumbai and worked in various domains. He became a part of backstage crew, editing, corporate films, ad films and did everything for survival. He observed that many people who come to Mumbai, wait for some kind of magic to happen. He later figured out that this is not what he has to become! His next bold step was to quit Mumbai. He came back to Bangalore and did one show and decided to continue with it. He then started writing scripts and making films as an associate directo...

    published: 07 Jul 2017
  • 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
  • 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
  • R11. Principles of Algorithm Design

    MIT 6.006 Introduction to Algorithms, Fall 2011 View the complete course: http://ocw.mit.edu/6-006F11 Instructor: Victor Costan License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu

    published: 14 Jan 2013
  • 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
  • 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
  • YouTube Algorithm 2017 Explained - The A.I. Behind The Curtain

    Here is part of my CVXLive 2017 Presentation: YouTube Algorithm 2017 Explained - The A.I. Behind The Curtain. This is the 3rd part of the Decoding the YouTube Algorithm and learn how to grow fast on YouTube with Algorithm Driven views series. Learn about YouTube Machine Learning Technology and how it interacts with viewers, videos, and channels. Get Access to Free VidSummit Replays https://vidsummit.com/freereplays Tickets to VidSummit 2017 https://vidsummit.com Decoding the YouTube Algorithm and learn how to grow fast on YouTube with Algorithm Driven views series ➜ https://goo.gl/sa6aGp Get More Great Tips - Subscribe ➜ http://goo.gl/dWNo9H Share this Video: ➜ https://youtu.be/Ix1gnDmuMyI My Favorite YouTube Tool TubeBuddy Download TubeBuddy Free Today! ➜ http://goo.gl/PrGfLe Join ...

    published: 09 Aug 2017
  • How Random Forest algorithm works

    In this video I explain very briefly how the Random Forest algorithm works with a simple example composed by 4 decision trees.

    published: 04 Apr 2014
  • 12. Greedy Algorithms: Minimum Spanning Tree

    MIT 6.046J Design and Analysis of Algorithms, Spring 2015 View the complete course: http://ocw.mit.edu/6-046JS15 Instructor: Erik Demaine In this lecture, Professor Demaine introduces greedy algorithms, which make locally-best choices without regards to the future. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu

    published: 04 Mar 2016
  • Lecture 10 - Neural Networks

    Neural Networks - A biologically inspired model. The efficient backpropagation learning algorithm. Hidden layers. Lecture 10 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/course/machine-learning/id515364596 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 May 3, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA.

    published: 06 May 2012
  • 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
  • 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
  • Recurrent Neural Network Writes Music and Shakespeare Novels | Two Minute Papers #19

    Artificial neural networks are powerful machine learning techniques that can learn to recognize images or paint in the style of Van Gogh. Recurrent neural networks offer a more general model that can learn input sequences and create output sequences. The resulting technique (Long Short-Term Memory in these examples) can write novels in the style of Tolstoy, Shakespeare, or write their own music. ________________________ Andrej Karpathy's original article is available here: http://karpathy.github.io/2015/05/21/rnn-effectiveness/ Source code: https://github.com/karpathy/char-rnn The paper "Long Short-Term Memory" by Sepp Hochreiter and Jürgen Schmidhuber is available here: http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf Continuing "Let It Go" from Disney with a recurrent neur...

    published: 23 Oct 2015
  • 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
  • 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
  • ALGORITHMS - Official Trailer

    http://firstrunfeatures.com/algorithmshv.html In India, a group of boys dream of becoming Chess Masters, driven by a man with a vision. But this is no ordinary chess and these are no ordinary players. Algorithms is a documentary on the thriving but little known world of Blind Chess in India. Filmed over three years, Algorithms travels with three talented boys and a totally blind player turned pioneer to competitive national and world championships and visits them in their home milieu where they reveal their struggles, anxieties and hopes. Going beyond sight and story, this observational sport doc with a difference moves through the algorithms of the blind chess world challenging the sighted of what it means to see. It allows for the tactile and thoughtful journey that explores foresight,...

    published: 03 Dec 2013
  • 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
  • Practical Machine Learning Tutorial with Python Intro p.1

    The objective of this course is to give you a wholistic understanding of machine learning, covering theory, application, and inner workings of supervised, unsupervised, and deep learning algorithms. In this series, we'll be covering linear regression, K Nearest Neighbors, Support Vector Machines (SVM), flat clustering, hierarchical clustering, and neural networks. For each major algorithm that we cover, we will discuss the high level intuitions of the algorithms and how they are logically meant to work. Next, we'll apply the algorithms in code using real world data sets along with a module, such as with Scikit-Learn. Finally, we'll be diving into the inner workings of each of the algorithms by recreating them in code, from scratch, ourselves, including all of the math involved. This shou...

    published: 11 Apr 2016
  • Tensorflow and deep learning - without a PhD by Martin Görner

    Google has recently open-sourced its framework for machine learning and neural networks called Tensorflow. With this new tool, deep machine learning transitions from an area of research into mainstream software engineering. In this session, we will teach you how to choose the right neural network for your problem and how to make it behave. Familiarity with differential equations is no longer required. Instead, a couple of lines ofTensorflow Python, and a bag of "tricks of the trade" will do the job. No previous Python knowledge required. This university session will cover the basics of deep learning, without any assumptions about the level of the participants. Machine learning beginners are welcome. We will cover: - fully connected neural networks - convolutional neural networks - regular...

    published: 09 Nov 2016
  • Bias? In My Algorithms? A Facebook News Story

    Why Facebook News Can’t Escape Bias Tweet us! http://bit.ly/pbsideachanneltwitter Idea Channel Facebook! http://bit.ly/pbsideachannelfacebook Talk about this episode on reddit! http://bit.ly/pbsideachannelreddit Idea Channel IRC! http://bit.ly/pbsideachannelirc Email us! pbsideachannel [at] gmail [dot] com Support Idea Channel on Patreon! http://www.patreon.com/pbsideachannel In case you missed the news because it wasn’t trending on Facebook, Facebook’s Trending News Team has been… in the news. Not long ago the whole department got the axe after Gizmodo reported they’d been suppressing conservative news items and sources. This caused a stir. And perhaps rightfully so: facebook is used by all stripes of people with all manner of beliefs and politics and it is where those people go to ge...

    published: 14 Sep 2016
  • Dijkstra's Algorithm Single Source Shortest Path Graph Algorithm

    Find single source shortest path using Dijkstra algorithm https://www.facebook.com/tusharroy25 https://github.com/mission-peace/interview/blob/master/src/com/interview/graph/DijkstraShortestPath.java https://github.com/mission-peace/interview/wiki

    published: 28 Oct 2015
  • Amazing Technologies Inspired By Nature

    Share on Facebook: http://on.fb.me/1sJV9Po We have to thank the scientists and inventors who designed the high-tech gadgets we know and love. But who do THEY have to thank? Spiders, moths and geckos, oh my! From solar panels to adhesives, some of our most advanced technology is dedicated to mimicking what nature already perfected. In the future it will be important that we continue to fund biological sciences -- it might just spark our next big technological innovation! What technology based off of nature do you think is the coolest in the world? Let us know in the comments below and explain your answer! -------------------------------------------------------- Subscribe to Fw:Thinking: http://www.youtube.com/subscription_center?add_user=fwthinking For the audio podcast, blog and more...

    published: 04 Jun 2014
  • 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
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: 40440
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
Art of Quitting | Pawan Kumar | TEDxDSCE

Art of Quitting | Pawan Kumar | TEDxDSCE

  • Order:
  • Duration: 17:27
  • Updated: 07 Jul 2017
  • views: 59006
videos
Pawan Kumar says "Quitting is OK". He explains this through the steps of his life as to how he quit, got on and moved at the right stage. Like quitting college to do theatre, then quitting his mentor which was a bold move in his life to do theatre on his own, then quitting his home space to learn more. He then moved to Mumbai and worked in various domains. He became a part of backstage crew, editing, corporate films, ad films and did everything for survival. He observed that many people who come to Mumbai, wait for some kind of magic to happen. He later figured out that this is not what he has to become! His next bold step was to quit Mumbai. He came back to Bangalore and did one show and decided to continue with it. He then started writing scripts and making films as an associate director. He released various movies in 2010 like Pancharangi and Manasare in kannada (local language). He then quit working in associations and then started directing movies on his own! He didn't gave up on his passion and did what he wanted to do always by quitting things that didn't make sense at that point of time. So to all those people out there, "It's okay to quit!" Pawan Kumar, a film director​, actor and screenwriter in the Kannada film industry. His directional debut "Lifeu Ishtene", "Lucia" - a psychological thriller, "U-turn" and "Ondu Motteya Kathe" (Yet to be released in India) in recent times has made a tremendous impact on the Kannada film industry. The man behind the lens is all set to throw his web of thought and entangle your mind. 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
https://wn.com/Art_Of_Quitting_|_Pawan_Kumar_|_Tedxdsce
Algorithms: Graph Search, DFS and BFS

Algorithms: Graph Search, DFS and BFS

  • Order:
  • Duration: 11:49
  • Updated: 27 Sep 2016
  • views: 83602
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
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: 467
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
R11. Principles of Algorithm Design

R11. Principles of Algorithm Design

  • Order:
  • Duration: 58:26
  • Updated: 14 Jan 2013
  • views: 24854
videos
MIT 6.006 Introduction to Algorithms, Fall 2011 View the complete course: http://ocw.mit.edu/6-006F11 Instructor: Victor Costan License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
https://wn.com/R11._Principles_Of_Algorithm_Design
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: 304472
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
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: 1204197
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
YouTube Algorithm 2017 Explained - The A.I. Behind The Curtain

YouTube Algorithm 2017 Explained - The A.I. Behind The Curtain

  • Order:
  • Duration: 33:07
  • Updated: 09 Aug 2017
  • views: 3625
videos
Here is part of my CVXLive 2017 Presentation: YouTube Algorithm 2017 Explained - The A.I. Behind The Curtain. This is the 3rd part of the Decoding the YouTube Algorithm and learn how to grow fast on YouTube with Algorithm Driven views series. Learn about YouTube Machine Learning Technology and how it interacts with viewers, videos, and channels. Get Access to Free VidSummit Replays https://vidsummit.com/freereplays Tickets to VidSummit 2017 https://vidsummit.com Decoding the YouTube Algorithm and learn how to grow fast on YouTube with Algorithm Driven views series ➜ https://goo.gl/sa6aGp Get More Great Tips - Subscribe ➜ http://goo.gl/dWNo9H Share this Video: ➜ https://youtu.be/Ix1gnDmuMyI My Favorite YouTube Tool TubeBuddy Download TubeBuddy Free Today! ➜ http://goo.gl/PrGfLe Join My Patreon Community for More Advanced Training Check Out Our Community! ➜ https://www.patreon.com/derraleves ★ ★ Be the Next Lucky Subscriber to get an In-depth Channel Evaluation: 1. Must be subscribed to My YouTube Channel Subscribe ➜ http://goo.gl/dWNo9H 2. Must be one of my Patrons on Patreon. Check it out! ➜ https://www.patreon.com/derraleves 3. Must be uploading good quality content frequently to your YouTube Channel and really trying hard to make it 4. Must be engaged in my channel by liking, commenting, posting, sharing and encouraging others to subscribe to my channel. Ask me A Question by using hashtag on YouTube or Twitter #AskDerral @derraleves
https://wn.com/Youtube_Algorithm_2017_Explained_The_A.I._Behind_The_Curtain
How Random Forest algorithm works

How Random Forest algorithm works

  • Order:
  • Duration: 5:47
  • Updated: 04 Apr 2014
  • views: 178714
videos
In this video I explain very briefly how the Random Forest algorithm works with a simple example composed by 4 decision trees.
https://wn.com/How_Random_Forest_Algorithm_Works
12. Greedy Algorithms: Minimum Spanning Tree

12. Greedy Algorithms: Minimum Spanning Tree

  • Order:
  • Duration: 1:22:10
  • Updated: 04 Mar 2016
  • views: 33744
videos
MIT 6.046J Design and Analysis of Algorithms, Spring 2015 View the complete course: http://ocw.mit.edu/6-046JS15 Instructor: Erik Demaine In this lecture, Professor Demaine introduces greedy algorithms, which make locally-best choices without regards to the future. 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._Greedy_Algorithms_Minimum_Spanning_Tree
Lecture 10 - Neural Networks

Lecture 10 - Neural Networks

  • Order:
  • Duration: 1:25:16
  • Updated: 06 May 2012
  • views: 286621
videos
Neural Networks - A biologically inspired model. The efficient backpropagation learning algorithm. Hidden layers. Lecture 10 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/course/machine-learning/id515364596 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 May 3, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA.
https://wn.com/Lecture_10_Neural_Networks
Programming For Beginners | Episode 1: Algorithms

Programming For Beginners | Episode 1: Algorithms

  • Order:
  • Duration: 24:10
  • Updated: 30 May 2016
  • views: 17
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
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: 25314
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
Recurrent Neural Network Writes Music and Shakespeare Novels | Two Minute Papers #19

Recurrent Neural Network Writes Music and Shakespeare Novels | Two Minute Papers #19

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  • Duration: 3:54
  • Updated: 23 Oct 2015
  • views: 21867
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Artificial neural networks are powerful machine learning techniques that can learn to recognize images or paint in the style of Van Gogh. Recurrent neural networks offer a more general model that can learn input sequences and create output sequences. The resulting technique (Long Short-Term Memory in these examples) can write novels in the style of Tolstoy, Shakespeare, or write their own music. ________________________ Andrej Karpathy's original article is available here: http://karpathy.github.io/2015/05/21/rnn-effectiveness/ Source code: https://github.com/karpathy/char-rnn The paper "Long Short-Term Memory" by Sepp Hochreiter and Jürgen Schmidhuber is available here: http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf Continuing "Let It Go" from Disney with a recurrent neural network: https://ericye16.com/music-rnn/ Recommended for you: Artificial Neural Networks and Deep Learning - https://www.youtube.com/watch?v=rCWTOOgVXyE Deep Neural Network Learns Van Gogh's Art - https://www.youtube.com/watch?v=-R9bJGNHltQ Creating Photographs Using Deep Learning - https://www.youtube.com/watch?v=HOLoPgTzV6g A great write-up on how LSTMs work: http://colah.github.io/posts/2015-08-Understanding-LSTMs/ More applications of Long Short-Term Memory: http://googleresearch.blogspot.co.uk/2015/09/google-voice-search-faster-and-more.html http://googleresearch.blogspot.co.at/2015/08/the-neural-networks-behind-google-voice.html Subscribe if you would like to see more of these! - http://www.youtube.com/subscription_center?add_user=keeroyz The thumbnail image background was created by Brandon Giesbrecht (license: CC BY 2.0). Slight changes were made for better blending. - https://www.flickr.com/photos/naturegeak/5819184201/ Splash screen/thumbnail design: Felícia Fehér - http://felicia.hu Music: "Gymnopedie no1" by Satie. 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/
https://wn.com/Recurrent_Neural_Network_Writes_Music_And_Shakespeare_Novels_|_Two_Minute_Papers_19
What Makes a Good Feature? - Machine Learning Recipes #3

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

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  • Duration: 5:41
  • Updated: 27 Apr 2016
  • views: 174758
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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
Algorithms and Tips You Need to know to Master EPLL

Algorithms and Tips You Need to know to Master EPLL

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  • Duration: 4:12
  • Updated: 06 Apr 2017
  • views: 2389
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
ALGORITHMS - Official Trailer

ALGORITHMS - Official Trailer

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  • Duration: 2:09
  • Updated: 03 Dec 2013
  • views: 928
videos
http://firstrunfeatures.com/algorithmshv.html In India, a group of boys dream of becoming Chess Masters, driven by a man with a vision. But this is no ordinary chess and these are no ordinary players. Algorithms is a documentary on the thriving but little known world of Blind Chess in India. Filmed over three years, Algorithms travels with three talented boys and a totally blind player turned pioneer to competitive national and world championships and visits them in their home milieu where they reveal their struggles, anxieties and hopes. Going beyond sight and story, this observational sport doc with a difference moves through the algorithms of the blind chess world challenging the sighted of what it means to see. It allows for the tactile and thoughtful journey that explores foresight, sight and vision to continue long after the moving image ends.
https://wn.com/Algorithms_Official_Trailer
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

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  • Duration: 9:53
  • Updated: 13 Sep 2017
  • views: 2307
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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: http://goo.gl/mQyv5L
https://wn.com/Let’S_Write_A_Decision_Tree_Classifier_From_Scratch_Machine_Learning_Recipes_8
Practical Machine Learning Tutorial with Python Intro p.1

Practical Machine Learning Tutorial with Python Intro p.1

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  • Duration: 5:55
  • Updated: 11 Apr 2016
  • views: 382073
videos
The objective of this course is to give you a wholistic understanding of machine learning, covering theory, application, and inner workings of supervised, unsupervised, and deep learning algorithms. In this series, we'll be covering linear regression, K Nearest Neighbors, Support Vector Machines (SVM), flat clustering, hierarchical clustering, and neural networks. For each major algorithm that we cover, we will discuss the high level intuitions of the algorithms and how they are logically meant to work. Next, we'll apply the algorithms in code using real world data sets along with a module, such as with Scikit-Learn. Finally, we'll be diving into the inner workings of each of the algorithms by recreating them in code, from scratch, ourselves, including all of the math involved. This should give you a complete understanding of exactly how the algorithms work, how they can be tweaked, what advantages are, and what their disadvantages are. In order to follow along with the series, I suggest you have at the very least a basic understanding of Python. If you do not, I suggest you at least follow the Python 3 Basics tutorial until the module installation with pip tutorial. If you have a basic understanding of Python, and the willingness to learn/ask questions, you will be able to follow along here with no issues. Most of the machine learning algorithms are actually quite simple, since they need to be in order to scale to large datasets. Math involved is typically linear algebra, but I will do my best to still explain all of the math. If you are confused/lost/curious about anything, ask in the comments section on YouTube, the community here, or by emailing me. You will also need Scikit-Learn and Pandas installed, along with others that we'll grab along the way. Machine learning was defined in 1959 by Arthur Samuel as the "field of study that gives computers the ability to learn without being explicitly programmed." This means imbuing knowledge to machines without hard-coding it. https://pythonprogramming.net/machine-learning-tutorial-python-introduction/ https://twitter.com/sentdex https://www.facebook.com/pythonprogra... https://plus.google.com/+sentdex
https://wn.com/Practical_Machine_Learning_Tutorial_With_Python_Intro_P.1
Tensorflow and deep learning - without a PhD by Martin Görner

Tensorflow and deep learning - without a PhD by Martin Görner

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  • Duration: 2:35:53
  • Updated: 09 Nov 2016
  • views: 243382
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Google has recently open-sourced its framework for machine learning and neural networks called Tensorflow. With this new tool, deep machine learning transitions from an area of research into mainstream software engineering. In this session, we will teach you how to choose the right neural network for your problem and how to make it behave. Familiarity with differential equations is no longer required. Instead, a couple of lines ofTensorflow Python, and a bag of "tricks of the trade" will do the job. No previous Python knowledge required. This university session will cover the basics of deep learning, without any assumptions about the level of the participants. Machine learning beginners are welcome. We will cover: - fully connected neural networks - convolutional neural networks - regularisation techniques: dropout, learning rate decay, batch normalisation - recurrent neural networks - natural language analysis, word embeddings - transfer learning - image analysis - image generation - and many examples. Martin Görner is passionate about science, technology, coding, algorithms and everything in between. He graduated from Mines Paris Tech, enjoyed his first engineering years in the computer architecture group of ST Microlectronics and then spent the next 11 years shaping the nascent eBook market, starting with the Mobipocket startup, which later became the software part of the Amazon Kindle and its mobile variants. He joined Google Developer Relations in 2011 and now focuses on parallel processing and machine learning. [ULT-2698]
https://wn.com/Tensorflow_And_Deep_Learning_Without_A_Phd_By_Martin_Görner
Bias? In My Algorithms? A Facebook News Story

Bias? In My Algorithms? A Facebook News Story

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  • Duration: 10:55
  • Updated: 14 Sep 2016
  • views: 75842
videos
Why Facebook News Can’t Escape Bias Tweet us! http://bit.ly/pbsideachanneltwitter Idea Channel Facebook! http://bit.ly/pbsideachannelfacebook Talk about this episode on reddit! http://bit.ly/pbsideachannelreddit Idea Channel IRC! http://bit.ly/pbsideachannelirc Email us! pbsideachannel [at] gmail [dot] com Support Idea Channel on Patreon! http://www.patreon.com/pbsideachannel In case you missed the news because it wasn’t trending on Facebook, Facebook’s Trending News Team has been… in the news. Not long ago the whole department got the axe after Gizmodo reported they’d been suppressing conservative news items and sources. This caused a stir. And perhaps rightfully so: facebook is used by all stripes of people with all manner of beliefs and politics and it is where those people go to get their news. It’d be dismaying, to say the least, to learn your news source suppresses topics most important to you. In light of this whole thing, there are, then, two questions I want to ask. The first is … why is there an expectation of zero bias from facebook? And second… what does facebook do in light of that expectation? Let us know what you think in the comments below! ---SOURCES--- Facebook Swaying Public Opinion http://www1.udel.edu/udaily/2016/sep/politics-social-media-092315.html http://www.nytimes.com/2012/09/13/us/politics/social-networks-affect-voter-turnout-study-finds.html?_r=0 https://www.theguardian.com/commentisfree/2016/apr/19/donald-trump-facebook-election-manipulate-behavior http://www.motherjones.com/politics/2014/10/can-voting-facebook-button-improve-voter-turnout http://mashable.com/2014/07/02/facebook-sandberg-emotions-experiment/#Riq1FvcMqsqT Bias in Language https://freedom-to-tinker.com/2016/08/24/language-necessarily-contains-human-biases-and-so-will-machines-trained-on-language-corpora/ Bias in Computer Algorithms https://socialmediacollective.org/reading-lists/critical-algorithm-studies/ ---FURTHER READING--- http://www.nytimes.com/2016/05/12/technology/facebooks-bias-is-built-in-and-bears-watching.html?_r=0 http://gizmodo.com/former-facebook-workers-we-routinely-suppressed-conser-1775461006 http://www.wsj.com/articles/facebook-refutes-criticisms-about-a-bias-against-conservatives-1462890206 http://money.cnn.com/2016/05/10/technology/facebook-news-senate/index.html http://digiday.com/platforms/former-facebook-trending-news-editor-just-going-get-rid-product-altogether/ http://technical.ly/brooklyn/2016/06/08/fred-benenson-mathwashing-facebook-data-worship/ http://www.wsj.com/articles/facebooks-trending-feature-exhibits-flaws-under-new-algorithm-1473176652 ---CHECK OUT OUR MERCH!--- http://bit.ly/1U8fS1B T-Shirts Designed by: http://artsparrow.com/ ---TWEET OF THE WEEK--- https://twitter.com/1212thedoctor/status/775409364207276032 ---ASSET LINKS--- 00:24 Gizmodo Article http://gizmodo.com/former-facebook-workers-we-routinely-suppressed-conser-1775461006 00:41 NY Times Article http://www.nytimes.com/2016/05/12/technology/facebooks-bias-is-built-in-and-bears-watching.html?_r=0 00:57 The Facebook Effect http://www1.udel.edu/udaily/2016/sep/politics-social-media-092315.html 1:11 Sriracha http://theoatmeal.com/comics/sriracha 2:35 Idea Channel Serial Part 2 https://www.youtube.com/watch?v=xT0yRXWo6UU 3:18 Billy on the Street https://www.youtube.com/watch?v=lz9HhVMAG8E&feature=youtu.be&t=106 3:40 Updated Beginners Guide to Facebook (2015) https://www.youtube.com/watch?v=YMr4M4ponm8 4:53 Media Matters http://mediamatters.org/blog/2012/11/03/fox-news-redefines-unbalanced-by-giving-romney/191118 5:58 Language Bias https://freedom-to-tinker.com/2016/08/24/language-necessarily-contains-human-biases-and-so-will-machines-trained-on-language-corpora/ 6:20 Mathwashing http://technical.ly/brooklyn/2016/06/08/fred-benenson-mathwashing-facebook-data-worship/ 7:52 Facebook Trending Illustration by Jim Cooke http://jimcookeillustration.tumblr.com/ 8:07 Wall Street Journal http://www.wsj.com/articles/facebooks-trending-feature-exhibits-flaws-under-new-algorithm-1473176652 ---MUSIC--- https://soundcloud.com/montone-2/minimalist ----------------------------------------­­­­­­­­­­­­­­­­­------------------------­-­-­-­- Written and hosted by Mike Rugnetta (@mikerugnetta) (who also has a podcast! Reasonably Sound: http://bit.ly/1sCn0BF) Made by Kornhaber Brown (http://www.kornhaberbrown.com)
https://wn.com/Bias_In_My_Algorithms_A_Facebook_News_Story
Dijkstra's Algorithm Single Source Shortest Path Graph Algorithm

Dijkstra's Algorithm Single Source Shortest Path Graph Algorithm

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  • Duration: 16:20
  • Updated: 28 Oct 2015
  • views: 162291
videos
Find single source shortest path using Dijkstra algorithm https://www.facebook.com/tusharroy25 https://github.com/mission-peace/interview/blob/master/src/com/interview/graph/DijkstraShortestPath.java https://github.com/mission-peace/interview/wiki
https://wn.com/Dijkstra's_Algorithm_Single_Source_Shortest_Path_Graph_Algorithm
Amazing Technologies Inspired By Nature

Amazing Technologies Inspired By Nature

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  • Duration: 4:13
  • Updated: 04 Jun 2014
  • views: 71308
videos
Share on Facebook: http://on.fb.me/1sJV9Po We have to thank the scientists and inventors who designed the high-tech gadgets we know and love. But who do THEY have to thank? Spiders, moths and geckos, oh my! From solar panels to adhesives, some of our most advanced technology is dedicated to mimicking what nature already perfected. In the future it will be important that we continue to fund biological sciences -- it might just spark our next big technological innovation! What technology based off of nature do you think is the coolest in the world? Let us know in the comments below and explain your answer! -------------------------------------------------------- Subscribe to Fw:Thinking: http://www.youtube.com/subscription_center?add_user=fwthinking For the audio podcast, blog and more, visit the Fw:Thinking website: http://www.fwthinking.com Fw:Thinking on Twitter: http://www.twitter.com/fwthinking Jonathan Stickland on Twitter: http://www.twitter.com/jonstrickland Fw:Thinking on Facebook: http://www.facebook.com/FWThinking01 Fw:Thinking on Google+: https://plus.google.com/u/0/108500616405453822675/
https://wn.com/Amazing_Technologies_Inspired_By_Nature
Predicting Stock Price Mathematically

Predicting Stock Price Mathematically

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  • Duration: 11:33
  • Updated: 07 Nov 2015
  • views: 51538
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