• How Do Machines Learn?

    How do all the algorithms around us learn to do their jobs? SHARE ON THE TWEETBOOK: https://goo.gl/dGUHMV Discuss this video: http://reddit.com/r/cgpgrey 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.co...

    published: 18 Dec 2017
  • Why The YouTube Algorithm Will Always Be A Mystery

    The mysterious YouTube algorithm. It's confused people for years, and will continue to do so. So why isn't YouTube more transparent? It used to be that they wouldn't tell anyone how it works - but now, it's that they can't. Let's talk about deep learning algorithms, neural networks, and search engine optimisation. CREDITS: Thanks to animator Matt Ley for the wonderful cartoon of me: https://www.youtube.com/user/Thelaserbearguy I put this together in three days, plus a day of checking and proofing, in Adobe After Effects. It took about eight hours to render, but that's because every frame has keying, lighting, camera, and motion blur effects, and because the original footage of me was in 4.6k lossless. Yes, the sound of the black box working is the sound of a microwave (it's the one in ...

    published: 15 May 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
  • 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: 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
  • 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
  • Learn Python - Intro to Programming - What is an Algorithm? - 7

    In this video we'll discuss what an algorithm is and how to begin thinking in algorithms. This is a key concept for learning to program. FREE Intro To Programming Video Course taught by a self-taught professional software engineer (Sylvester Morgan). Learn how to program using Python, one of the most popular programming languages. This course is for complete beginners. No prior knowledge or experience needed. Requirements: A computer (nothing fancy or expensive necessary), access to the internet, and a willingness to learn! :) Read my full story here: http://www.sylvestermorgan.com/about/ Recommended Resources: Pluralsight: http://www.shareasale.com/r.cfm?B=971419&U=1472282&M=53701&urllink= In addition to many free resources, this is the online developer training I used to learn soft...

    published: 09 Jan 2018
  • 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
  • '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
  • 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
  • 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
  • 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
  • 13. Classification

    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 introduces supervised learning with nearest neighbor classification using feature scaling and decision trees. 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
  • 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
  • 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
  • Master Class with Prof. Monica Higgins | "Learning to Lead Through Case Discussion"

    The Harvard Graduate School of Education is pleased to continue "Master Class," a series that celebrates inspiring teaching at Harvard. Each event involves a demonstration of teaching followed by a reflective discussion with the participants. The “demonstration” part of the time will be an authentic experience of learning for members of the audience, drawing on the faculty member’s chosen teaching approach and topic; the “reflection” part will be a dialogue in which the faculty member shares his or her pedagogical assumptions, intentions, and moves, and engages in a conversation with a discussant and the audience that “pulls back the curtain” on his or her teaching. This event precedes the Ed School's annual Teaching and Learning Week, which will run from October 6 - 10. In the third clas...

    published: 09 Oct 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
  • 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
  • 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
  • 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
  • Introduction To Optimization: Gradient Based Algorithms

    A conceptual overview of gradient based optimization algorithms. This video is part of an introductory optimization series. QUIZ: https://goo.gl/forms/1NaFUcqCnWgWbrQh1 TRANSCRIPT: Hello, and welcome to Introduction To Optimization. This video covers gradient based algorithms. Gradient based algorithms and gradient free algorithms are the two main types of methods for solving optimization problems. In this video, we will learn the basic ideas behind how gradient based solvers work. Gradient based solvers use derivatives to find the optimum value of a function. To understand how this works, imagine that you are hiking on a mountainside, trying to find your way to a campsite at the bottom of the mountain. How would you know where to go? Perhaps you could follow a trail, look at ...

    published: 29 Mar 2017
  • Lecture - 1 Introduction to Data Structures and Algorithms

    Lecture Series on Data Structures and Algorithms by Dr. Naveen Garg, Department of Computer Science & Engineering ,IIT Delhi.

    published: 24 Sep 2008
  • Practical Machine Learning Tutorial with Python Intro p.1

    For a pre-compiled version of Python with optimized data science libraries, check out our Sponsor ActiveState's distribution of Python: https://goo.gl/dEZvv8 ... rather than manually installing the laundry list of libraries for this series. The objective of this course is to give you a holistic 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 ...

    published: 11 Apr 2016
developed with YouTube
How Do Machines Learn?

How Do Machines Learn?

  • Order:
  • Duration: 8:55
  • Updated: 18 Dec 2017
  • views: 39182
videos
How do all the algorithms around us learn to do their jobs? SHARE ON THE TWEETBOOK: https://goo.gl/dGUHMV Discuss this video: http://reddit.com/r/cgpgrey 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_Do_Machines_Learn
Why The YouTube Algorithm Will Always Be A Mystery

Why The YouTube Algorithm Will Always Be A Mystery

  • Order:
  • Duration: 4:59
  • Updated: 15 May 2017
  • views: 703223
videos
The mysterious YouTube algorithm. It's confused people for years, and will continue to do so. So why isn't YouTube more transparent? It used to be that they wouldn't tell anyone how it works - but now, it's that they can't. Let's talk about deep learning algorithms, neural networks, and search engine optimisation. CREDITS: Thanks to animator Matt Ley for the wonderful cartoon of me: https://www.youtube.com/user/Thelaserbearguy I put this together in three days, plus a day of checking and proofing, in Adobe After Effects. It took about eight hours to render, but that's because every frame has keying, lighting, camera, and motion blur effects, and because the original footage of me was in 4.6k lossless. Yes, the sound of the black box working is the sound of a microwave (it's the one in my kitchen). Also, those aren't faked desktop screenshots, I had to install a copy of Windows ME to make this. SOURCES: "Deep Neural Networks for YouTube Recommendations", https://research.google.com/pubs/archive/45530.pdf [PDF] — some people are saying this link 404s, but it works for me? Search for the title and you'll find it. There's a good layperson summary of the paper here: http://www.tubefilter.com/2017/02/16/youtube-algorithm-reverse-engineering-part-ii/ The Defamation Act is published under the Open Government License 3.0: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/ and is available at http://www.legislation.gov.uk/ukpga/2013/26/contents/enacted The music in the cartoon section is called 'Ukulele Beach', and it's in the YouTube audio library. REFERENCES: There are a lot of references and in-jokes in here, and hopefully people will spot most of them in the comments. But if anyone wants confirmation: yes, there are references to Billy Joel, Aqua, a He-Man remix, and Elton John. The last one's pretty obscure, well done to you at home if you got that. VFX breakdown and references explained: https://www.youtube.com/watch?v=6s9aGt2Lkgw ABOUT ME: I'm at http://tomscott.com on Twitter at http://twitter.com/tomscott on Facebook at http://facebook.com/tomscott and on Snapchat and Instagram as tomscottgo
https://wn.com/Why_The_Youtube_Algorithm_Will_Always_Be_A_Mystery
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: 1180
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
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: 2887
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: Graph Search, DFS and BFS

Algorithms: Graph Search, DFS and BFS

  • Order:
  • Duration: 11:49
  • Updated: 27 Sep 2016
  • views: 160804
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
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: 394438
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
Learn Python - Intro to Programming - What is an Algorithm? - 7

Learn Python - Intro to Programming - What is an Algorithm? - 7

  • Order:
  • Duration: 5:24
  • Updated: 09 Jan 2018
  • views: 80
videos
In this video we'll discuss what an algorithm is and how to begin thinking in algorithms. This is a key concept for learning to program. FREE Intro To Programming Video Course taught by a self-taught professional software engineer (Sylvester Morgan). Learn how to program using Python, one of the most popular programming languages. This course is for complete beginners. No prior knowledge or experience needed. Requirements: A computer (nothing fancy or expensive necessary), access to the internet, and a willingness to learn! :) Read my full story here: http://www.sylvestermorgan.com/about/ Recommended Resources: Pluralsight: http://www.shareasale.com/r.cfm?B=971419&U=1472282&M=53701&urllink= In addition to many free resources, this is the online developer training I used to learn software developer. Read my review here: http://www.sylvestermorgan.com/resources/ Programming textbook that I started out with: http://amzn.to/2ppZzCV This book played a part in inspiring me to become a programmer. This author does a great job of teaching the basics. These books will help you achieve success beyond software development: The 7 Habits of Highly Effective People: Power Lessons in Personal Change: http://amzn.to/2pRfM5z Linchpin: Are you Indispensable: http://amzn.to/2pRclfs QBQ! The Question Behind the Question: Practicing Personal Accountability at Work and in Life: http://amzn.to/2oZK9SM How Successful People Think: Change Your Thinking, Change Your Life: http://amzn.to/2pq71Oz How Successful People Grow: 15 Ways to Get Ahead In Life: http://amzn.to/2ppRA9e How Successful People Win: Turn Every Setback into a Step Forward: http://amzn.to/2pq33W7 Soft Skills: The Software Developer’s Life Manual: http://amzn.to/2qrJZoY Connect: http://www.sylvestermorgan.com/ https://www.linkedin.com/in/sqlsylvester/ https://www.facebook.com/SQLSylvester/ https://twitter.com/SQLSylvester https://www.youtube.com/channel/UCVj_s6XbQcwlRMZjeO_7QSw Equipment I use for my YouTube Channel: Nikon D3200: http://amzn.to/2pRiLLu Ravelli AVTP Pro Tripod: http://amzn.to/2oZZdj0 CowboyStudio Backdrop: http://amzn.to/2pq62hz Fancierstudio Pro Lighting Kit: http://amzn.to/2ppVUoK Rode Video Mic Go: http://amzn.to/2pRcHCO Insignia - Lapel Mic: http://amzn.to/2oZUFJB Logitech HD Pro Webcam C920: http://amzn.to/2qzWi2e Shop Lights: http://amzn.to/2rieezB Blue Snowball USB Microphone: http://amzn.to/2pzAcgH External Monitor: http://amzn.to/2rmo05R GorillaPod: http://amzn.to/2qzzYpA Ravelli ATD Professional Tripod Dolly: http://amzn.to/2scIQT6 ASUS ZenBook Pro: http://amzn.to/2sctFJJ Disclaimer: This description contains affiliate links. This means that if you click on the links and purchase a product, I do receive a small commission. This helps support the work I do and allows me to continue bringing you guys valuable life changing content.
https://wn.com/Learn_Python_Intro_To_Programming_What_Is_An_Algorithm_7
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: 51191
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
'The Algorithm' - How YouTube Search & Discovery Works

'The Algorithm' - How YouTube Search & Discovery Works

  • Order:
  • Duration: 2:02
  • Updated: 28 Aug 2017
  • views: 157870
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
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

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  • Duration: 10:42
  • Updated: 19 May 2014
  • views: 1338924
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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
Programming For Beginners | Episode 1: Algorithms

Programming For Beginners | Episode 1: Algorithms

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  • Duration: 24:10
  • Updated: 30 May 2016
  • views: 18
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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
ALGORITHMS - Official Trailer

ALGORITHMS - Official Trailer

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  • Duration: 2:09
  • Updated: 03 Dec 2013
  • views: 947
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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: 52099
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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 channel: http://goo.gl/mQyv5L
https://wn.com/Let’S_Write_A_Decision_Tree_Classifier_From_Scratch_Machine_Learning_Recipes_8
13. Classification

13. Classification

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  • Duration: 49:54
  • Updated: 19 May 2017
  • views: 8383
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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 introduces supervised learning with nearest neighbor classification using feature scaling and decision trees. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
https://wn.com/13._Classification
Lecture 10 - Neural Networks

Lecture 10 - Neural Networks

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  • Duration: 1:25:16
  • Updated: 06 May 2012
  • views: 313630
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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
Randomized algorithms (intro) | Journey into cryptography | Computer Science | Khan Academy

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

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  • Duration: 9:23
  • Updated: 30 Apr 2014
  • views: 28970
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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
Master Class with Prof. Monica Higgins | "Learning to Lead Through Case Discussion"

Master Class with Prof. Monica Higgins | "Learning to Lead Through Case Discussion"

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  • Duration: 1:19:01
  • Updated: 09 Oct 2014
  • views: 13030
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The Harvard Graduate School of Education is pleased to continue "Master Class," a series that celebrates inspiring teaching at Harvard. Each event involves a demonstration of teaching followed by a reflective discussion with the participants. The “demonstration” part of the time will be an authentic experience of learning for members of the audience, drawing on the faculty member’s chosen teaching approach and topic; the “reflection” part will be a dialogue in which the faculty member shares his or her pedagogical assumptions, intentions, and moves, and engages in a conversation with a discussant and the audience that “pulls back the curtain” on his or her teaching. This event precedes the Ed School's annual Teaching and Learning Week, which will run from October 6 - 10. In the third class of the series Professor Monica Higgins will teach a session entitled, "Learning to Lead through Case Discussion." HGSE Senior Lecturer James Honan will serve as the discussant.
https://wn.com/Master_Class_With_Prof._Monica_Higgins_|_Learning_To_Lead_Through_Case_Discussion
12. Greedy Algorithms: Minimum Spanning Tree

12. Greedy Algorithms: Minimum Spanning Tree

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  • Duration: 1:22:10
  • Updated: 04 Mar 2016
  • views: 48827
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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
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: 241812
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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
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: 77858
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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
How Random Forest algorithm works

How Random Forest algorithm works

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  • Duration: 5:47
  • Updated: 04 Apr 2014
  • views: 220451
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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
Introduction To Optimization: Gradient Based Algorithms

Introduction To Optimization: Gradient Based Algorithms

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  • Duration: 5:27
  • Updated: 29 Mar 2017
  • views: 2364
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A conceptual overview of gradient based optimization algorithms. This video is part of an introductory optimization series. QUIZ: https://goo.gl/forms/1NaFUcqCnWgWbrQh1 TRANSCRIPT: Hello, and welcome to Introduction To Optimization. This video covers gradient based algorithms. Gradient based algorithms and gradient free algorithms are the two main types of methods for solving optimization problems. In this video, we will learn the basic ideas behind how gradient based solvers work. Gradient based solvers use derivatives to find the optimum value of a function. To understand how this works, imagine that you are hiking on a mountainside, trying to find your way to a campsite at the bottom of the mountain. How would you know where to go? Perhaps you could follow a trail, look at a map, or use a GPS. You might even be able to see your destination, and head straight there. Now imagine that you have no map, no GPS, no trail, and there are trees all around that keep you from seeing anything but the area immediately around you. Now what? Knowing nothing except for the fact that the campsite is at the bottom of the mountain, one possible option is to head downhill. You could look around, evaluate the slope of the mountain in the small area you can see, and walk in the direction with the steepest downhill slope. You could continue doing this, pausing once in awhile to find the best path forward, and eventually make it to the campsite. On a basic level, this is the same thing that gradient based algorithms do. There are three main steps: Search Direction: The first step is to pick a direction to go. The solver evaluates the slope by taking the derivative at its current location. In one dimension this derivative is the slope. In more than one dimension, this is called the gradient. The solver then uses this information together with other rules to pick a direction to go. This is called the search direction. Step Size: The next step is to decide how far to go in the chosen direction. You don’t want to go too far in one direction, or you might end up going back up a different mountain. However, you do want to go far enough to make some progress towards your goal. The value the solver chooses is called the step size. Convergence Check: Once a direction and a step size are chosen, the solver moves in the chosen direction. Then it checks to see if it has reached the bottom. If not, it uses the slope again to pick a new direction and step size. This continues until the solver reaches the bottom of the mountain, or the minimum. We call this convergence. There are many variations on the way that these steps are performed, but these are the basic ideas behind how a gradient based optimization algorithm works. Let’s take a look at what this might look like on an actual function. We’ll try to find the minimum of the equation x^3 + 15x^2 + y^3 +15y^2. We’ll start out by visualizing the function. This is a plot of the function values over a range from -10 to 10 in both directions. Notice how the function slopes down towards a minimum in the center. To begin, we’ll need to give the optimizer an initial guess. Let’s choose (8,8). Another way we can represent this information is with a contour plot, where the lines represent constant levels or function values. We can watch now as the optimizer chooses a search direction, and takes a step, a direction, and a step. Eventually it reaches the minimum point at x = 0, y = 0. Gradient based algorithms have their own strengths and weaknesses. They are widely used, have fast performance, and scale well to large problems. However, they do require smooth, continuous function gradients, and computing those gradients can be computationally expensive. Many gradient based optimizers are also susceptible to finding local minima rather than a global optimum, meaning that they will find the bottom of the closest valley, rather than the lowest point on the whole map. Gradient based optimizers are a powerful tool, but as with any optimization problem, it takes experience and practice to know which method is the right one to use in your situation.
https://wn.com/Introduction_To_Optimization_Gradient_Based_Algorithms
Lecture - 1 Introduction to Data Structures and Algorithms

Lecture - 1 Introduction to Data Structures and Algorithms

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  • Duration: 53:31
  • Updated: 24 Sep 2008
  • views: 1450273
videos https://wn.com/Lecture_1_Introduction_To_Data_Structures_And_Algorithms
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: 669729
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For a pre-compiled version of Python with optimized data science libraries, check out our Sponsor ActiveState's distribution of Python: https://goo.gl/dEZvv8 ... rather than manually installing the laundry list of libraries for this series. The objective of this course is to give you a holistic 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
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