• How To Program For Beginners | Episode 1: Algorithms

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

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

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

    published: 12 May 2016
  • 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
  • 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
  • Dynamic Programming Introduction

    Going over the very basics of dynamic programming before we continue the series in more depth.

    published: 16 May 2015
  • 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
  • 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
  • 5 tips to improve your critical thinking - Samantha Agoos

    View full lesson: http://ed.ted.com/lessons/5-tips-to-improve-your-critical-thinking-samantha-agoos Every day, a sea of decisions stretches before us, and it’s impossible to make a perfect choice every time. But there are many ways to improve our chances — and one particularly effective technique is critical thinking. Samantha Agoos describes a 5-step process that may help you with any number of problems. Lesson by Samantha Agoos, animation by Nick Hilditch.

    published: 15 Mar 2016
  • 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
  • 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
  • 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
  • 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
  • '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
  • 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
  • 10. Dynamic Programming: Advanced DP

    MIT 6.046J Design and Analysis of Algorithms, Spring 2015 View the complete course: http://ocw.mit.edu/6-046JS15 Instructor: Srinivas Devadas In this lecture, Professor Devadas introduces the concept of dynamic programming. 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
  • 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
  • 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
  • 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
  • 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
  • Swift Fun Algorithms #4: Most Common Name in Array

    Today, we continue with our series by going over how to get the most common name inside of an array. The main takeaway for today's lesson is to learn how to properly keep track of a running count using a Swift Dictionary object. Complete source code here: http://letsbuildthatapp.com/2016/05/11/swift-fun-algorithms-common-name-array/ Follow me on Twitter: https://twitter.com/buildthatapp

    published: 11 May 2016
  • 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
  • I'M A LOSER, DON'T WATCH ME

    Today I teach everyone how to cut their hair in a messy way. It basically looks like you had a stand off with a push mower and you somehow slipped, rolled your head under it and it chopped your hair halfway off... Anyway, the dialogue in this video is a joke. We have multiple versions of ourselves, and the version I portrayed in this video was of an ungrateful, first world problem-focused, self-pitying white cis male. If you had an actual conversation with me about success on YouTube it would have a very different tone. Fact is many people are successful because they know how to relate with a large percent of people on YouTube/spark their interest. In most cases when someone fails on YouTube, they have themselves to blame. Yes, the YouTube algorithm does have a big part to play, but you...

    published: 03 Aug 2016
  • The Artificial Intelligence revolution

    Support CaspianReport through Patreon: https://www.patreon.com/CaspianReport Nathan's Twitter: https://twitter.com/NathanAB_ WASHINGTON - Over the 20th century, the development of automated machinery has propelled modern industry and manufacturing into new heights of productivity. However, the cost of this technological advancement has been the displacement of millions of blue collar jobs across the world. In contrast, white-collar labour has always been regarded as safe from the kind of automation that contracted the manufacturing workforce. The thought of machines replacing educated and skilled professionals had usually seemed a distant problem of the future - until now. In the past few years, advancement in artificial intelligence has skyrocketed and computers are now learning to s...

    published: 02 Jun 2017
  • Learning to learn and compositionality with deep recurrent neural networks

    Author: Nando de Freitas, Department of Computer Science, University of Oxford Abstract: Deep neural network representations play an important role in computer vision, speech, computational linguistics, robotics, reinforcement learning and many other data-rich domains. In this talk I will show that learning-to-learn and compositionality are key ingredients for dealing with knowledge transfer so as to solve a wide range of tasks, for dealing with small-data regimes, and for continual learning. I will demonstrate this with three examples: learning learning algorithms, neural programmers and interpreters, and learning communication. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/

    published: 01 Sep 2016
  • Tarjan's Toposort Algorithm - Graph Theory 14

    We continue learning about toposort by considering Tarjan's algorithm, another technique to obtain a topological sorting! = 0612 TV = 0612 TV is your one stop for general geekery! Learn about a variety of technology-related subjects, including Photography, General Computing, Audio/Video Production and Image Manipulation! Enjoy your stay, and don't hesitate to drop me a comment or a personal message to my inbox =) If you like my work, don't forget to subscribe! Support me on Patreon: http://patreon.com/lcc0612 More about me: http://about.me/lcc0612 Official Twitter: http://twitter.com/0612tv ----- Disclaimer: Please note that any information is provided on this channel in good faith, but I cannot guarantee 100% accuracy / correctness on all content. Contributors to this channel are not ...

    published: 07 Mar 2016
  • AirBnB's Secret Weapon to Grow: Artificial Intelligence (AI) and Machine Learning (ML)

    AirBnb's VP of Engineering discussed how the company continues to innovate by applying machine learning (ML) to create superior algorithms both for searching (for guests) and for pricing (for hosts) -- both of which increase conversions and drive significant growth. July 2017

    published: 25 Jul 2017
developed with YouTube
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: 628
videos
This is the start to a new series, and I hope to teach you guys all the tricks and tips you need to becoming a successful programmer! If you're interested in more videos, and you want to continue to get better at programming, please subscribe for all future episodes!
https://wn.com/How_To_Program_For_Beginners_|_Episode_1_Algorithms
Brian Christian & Tom Griffiths: "Algorithms to Live By" | Talks at Google

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

  • Order:
  • Duration: 1:07:28
  • Updated: 12 May 2016
  • views: 44425
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
R11. Principles of Algorithm Design

R11. Principles of Algorithm Design

  • Order:
  • Duration: 58:26
  • Updated: 14 Jan 2013
  • views: 26555
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
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: 1250326
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
Dynamic Programming Introduction

Dynamic Programming Introduction

  • Order:
  • Duration: 8:43
  • Updated: 16 May 2015
  • views: 91777
videos
Going over the very basics of dynamic programming before we continue the series in more depth.
https://wn.com/Dynamic_Programming_Introduction
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: 343321
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
Algorithms: Graph Search, DFS and BFS

Algorithms: Graph Search, DFS and BFS

  • Order:
  • Duration: 11:49
  • Updated: 27 Sep 2016
  • views: 112040
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
5 tips to improve your critical thinking - Samantha Agoos

5 tips to improve your critical thinking - Samantha Agoos

  • Order:
  • Duration: 4:30
  • Updated: 15 Mar 2016
  • views: 2807881
videos
View full lesson: http://ed.ted.com/lessons/5-tips-to-improve-your-critical-thinking-samantha-agoos Every day, a sea of decisions stretches before us, and it’s impossible to make a perfect choice every time. But there are many ways to improve our chances — and one particularly effective technique is critical thinking. Samantha Agoos describes a 5-step process that may help you with any number of problems. Lesson by Samantha Agoos, animation by Nick Hilditch.
https://wn.com/5_Tips_To_Improve_Your_Critical_Thinking_Samantha_Agoos
Let’s Write a Decision Tree Classifier from Scratch: Machine Learning Recipes #8

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

  • Order:
  • Duration: 9:53
  • Updated: 13 Sep 2017
  • views: 26508
videos
Hey everyone! Glad to be back! Decision Tree classifiers are intuitive, interpretable, and one of my favorite supervised learning algorithms. In this episode, I’ll walk you through writing a Decision Tree classifier from scratch, in pure Python. I’ll introduce concepts including Decision Tree Learning, Gini Impurity, and Information Gain. Then, we’ll code it all up. Understanding how to accomplish this was helpful to me when I studied Machine Learning for the first time, and I hope it will prove useful to you as well. You can find the code from this video here: https://goo.gl/UdZoNr https://goo.gl/ZpWYzt Books! Hands-On Machine Learning with Scikit-Learn and TensorFlow https://goo.gl/kM0anQ Follow Josh on Twitter: https://twitter.com/random_forests Check out more Machine Learning Recipes here: https://goo.gl/KewA03 Subscribe to the Google Developers: http://goo.gl/mQyv5L
https://wn.com/Let’S_Write_A_Decision_Tree_Classifier_From_Scratch_Machine_Learning_Recipes_8
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: 26782
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
Lecture 10 - Neural Networks

Lecture 10 - Neural Networks

  • Order:
  • Duration: 1:25:16
  • Updated: 06 May 2012
  • views: 297319
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
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: 2489
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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
'The Algorithm' - How YouTube Search & Discovery Works

'The Algorithm' - How YouTube Search & Discovery Works

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  • Duration: 2:02
  • Updated: 28 Aug 2017
  • views: 76562
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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
How Random Forest algorithm works

How Random Forest algorithm works

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  • Duration: 5:47
  • Updated: 04 Apr 2014
  • views: 196787
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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
10. Dynamic Programming: Advanced DP

10. Dynamic Programming: Advanced DP

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  • Duration: 1:20:08
  • Updated: 04 Mar 2016
  • views: 17439
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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: Srinivas Devadas In this lecture, Professor Devadas introduces the concept of dynamic programming. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
https://wn.com/10._Dynamic_Programming_Advanced_Dp
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: 206018
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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
13. Classification

13. Classification

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  • Duration: 49:54
  • Updated: 19 May 2017
  • views: 4242
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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
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: 277063
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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: 76700
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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
Amazing Technologies Inspired By Nature

Amazing Technologies Inspired By Nature

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  • Duration: 4:13
  • Updated: 04 Jun 2014
  • views: 71815
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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
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: 505806
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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
Swift Fun Algorithms #4: Most Common Name in Array

Swift Fun Algorithms #4: Most Common Name in Array

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  • Duration: 10:16
  • Updated: 11 May 2016
  • views: 3295
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Today, we continue with our series by going over how to get the most common name inside of an array. The main takeaway for today's lesson is to learn how to properly keep track of a running count using a Swift Dictionary object. Complete source code here: http://letsbuildthatapp.com/2016/05/11/swift-fun-algorithms-common-name-array/ Follow me on Twitter: https://twitter.com/buildthatapp
https://wn.com/Swift_Fun_Algorithms_4_Most_Common_Name_In_Array
Predicting Stock Price Mathematically

Predicting Stock Price Mathematically

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  • Duration: 11:33
  • Updated: 07 Nov 2015
  • views: 66948
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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
I'M A LOSER, DON'T WATCH ME

I'M A LOSER, DON'T WATCH ME

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  • Duration: 8:15
  • Updated: 03 Aug 2016
  • views: 361554
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Today I teach everyone how to cut their hair in a messy way. It basically looks like you had a stand off with a push mower and you somehow slipped, rolled your head under it and it chopped your hair halfway off... Anyway, the dialogue in this video is a joke. We have multiple versions of ourselves, and the version I portrayed in this video was of an ungrateful, first world problem-focused, self-pitying white cis male. If you had an actual conversation with me about success on YouTube it would have a very different tone. Fact is many people are successful because they know how to relate with a large percent of people on YouTube/spark their interest. In most cases when someone fails on YouTube, they have themselves to blame. Yes, the YouTube algorithm does have a big part to play, but you can either complain about it, and continue to be unsuccessful, or you can learn it & figure out how to make it work for you. Long story short, like I said, this video is a joke. Especially when it comes to DanIsNotOnFire, he is successful because he sounds amazing, looks amazing & has a lovable personality. Duh. Side note: Jokes are something said to make people laugh, doesn't mean they're all totally unrelated to reality. I said what I did for laughs, to make points from a self-loathing perspective & generally act pathetic. It's fun for me.
https://wn.com/I'M_A_Loser,_Don'T_Watch_Me
The Artificial Intelligence revolution

The Artificial Intelligence revolution

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  • Duration: 15:38
  • Updated: 02 Jun 2017
  • views: 89759
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Support CaspianReport through Patreon: https://www.patreon.com/CaspianReport Nathan's Twitter: https://twitter.com/NathanAB_ WASHINGTON - Over the 20th century, the development of automated machinery has propelled modern industry and manufacturing into new heights of productivity. However, the cost of this technological advancement has been the displacement of millions of blue collar jobs across the world. In contrast, white-collar labour has always been regarded as safe from the kind of automation that contracted the manufacturing workforce. The thought of machines replacing educated and skilled professionals had usually seemed a distant problem of the future - until now. In the past few years, advancement in artificial intelligence has skyrocketed and computers are now learning to solve complex problems better and faster than human beings. As learnings algorithms and computing power continue to become more powerful, many jobs that were once thought to be impossible to automate are slowly but surely shifting towards a silicon workforce. Andrew Ng lecture: https://youtu.be/21EiKfQYZXc References: https://www.stlouisfed.org/on-the-economy/2016/january/jobs-involving-routine-tasks-arent-growing Soundtrack: Infados by Kevin MacLeod (incompetech.com) Licensed under Creative Commons: By Attribution 4.0 http://creativecommons.org/licenses/by/4.0/ Artifact by Kevin MacLeod (incompetech.com) Licensed under Creative Commons: By Attribution 4.0 http://creativecommons.org/licenses/by/4.0/ Crunk Knight by Kevin MacLeod (incompetech.com) Licensed under Creative Commons: By Attribution 4.0 http://creativecommons.org/licenses/by/4.0/ Cycles by Audionautix (audionautix.com) Licensed under Creative Commons: By Attribution 4.0 http://creativecommons.org/licenses/by/4.0/ Follow CaspianReport on social media. Facebook: https://www.facebook.com/caspianreport Twitter: https://twitter.com/caspianreport
https://wn.com/The_Artificial_Intelligence_Revolution
Learning to learn and compositionality with deep recurrent neural networks

Learning to learn and compositionality with deep recurrent neural networks

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  • Duration: 1:23:45
  • Updated: 01 Sep 2016
  • views: 12515
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Author: Nando de Freitas, Department of Computer Science, University of Oxford Abstract: Deep neural network representations play an important role in computer vision, speech, computational linguistics, robotics, reinforcement learning and many other data-rich domains. In this talk I will show that learning-to-learn and compositionality are key ingredients for dealing with knowledge transfer so as to solve a wide range of tasks, for dealing with small-data regimes, and for continual learning. I will demonstrate this with three examples: learning learning algorithms, neural programmers and interpreters, and learning communication. More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
https://wn.com/Learning_To_Learn_And_Compositionality_With_Deep_Recurrent_Neural_Networks
Tarjan's Toposort Algorithm - Graph Theory 14

Tarjan's Toposort Algorithm - Graph Theory 14

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  • Duration: 9:55
  • Updated: 07 Mar 2016
  • views: 6307
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We continue learning about toposort by considering Tarjan's algorithm, another technique to obtain a topological sorting! = 0612 TV = 0612 TV is your one stop for general geekery! Learn about a variety of technology-related subjects, including Photography, General Computing, Audio/Video Production and Image Manipulation! Enjoy your stay, and don't hesitate to drop me a comment or a personal message to my inbox =) If you like my work, don't forget to subscribe! Support me on Patreon: http://patreon.com/lcc0612 More about me: http://about.me/lcc0612 Official Twitter: http://twitter.com/0612tv ----- Disclaimer: Please note that any information is provided on this channel in good faith, but I cannot guarantee 100% accuracy / correctness on all content. Contributors to this channel are not to be held responsible for any possible outcomes from your use of the information.
https://wn.com/Tarjan's_Toposort_Algorithm_Graph_Theory_14
AirBnB's Secret Weapon to Grow: Artificial Intelligence (AI) and Machine Learning (ML)

AirBnB's Secret Weapon to Grow: Artificial Intelligence (AI) and Machine Learning (ML)

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  • Duration: 22:06
  • Updated: 25 Jul 2017
  • views: 2199
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AirBnb's VP of Engineering discussed how the company continues to innovate by applying machine learning (ML) to create superior algorithms both for searching (for guests) and for pricing (for hosts) -- both of which increase conversions and drive significant growth. July 2017
https://wn.com/Airbnb's_Secret_Weapon_To_Grow_Artificial_Intelligence_(Ai)_And_Machine_Learning_(Ml)
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