• What is Optimization? + Learning Gradient Descent | Two Minute Papers #82

    Let's talk about what mathematical optimization is, how gradient descent can solve simpler optimization problems, and Google DeepMind's proposed algorithm that automatically learn optimization algorithms. The paper "Learning to learn by gradient descent by gradient descent" is available here: http://arxiv.org/pdf/1606.04474v1.pdf Source code: https://github.com/deepmind/learning-to-learn ______________________________ Recommended for you: Gradients, Poisson's Equation and Light Transport - https://www.youtube.com/watch?v=sSnDTPjfBYU WE WOULD LIKE TO THANK OUR GENEROUS PATREON SUPPORTERS WHO MAKE TWO MINUTE PAPERS POSSIBLE: David Jaenisch, Sunil Kim, Julian Josephs, Daniel John Benton. https://www.patreon.com/TwoMinutePapers We also thank Experiment for sponsoring our series. - https:...

    published: 29 Jul 2016
  • How To Program For Beginners | Episode 1: Algorithms

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

    published: 30 May 2016
  • 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
  • R11. Principles of Algorithm Design

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

    published: 14 Jan 2013
  • Algorithm using Flowchart and Pseudo code Level 1 Flowchart

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

    published: 27 Aug 2013
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Sorting in Python || Learn Python Programming (Computer Science)

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

    published: 08 Oct 2017
  • Java Programming

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

    published: 03 Jun 2014
  • 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 3 | Loss Functions and Optimization

    Lecture 3 continues our discussion of linear classifiers. We introduce the idea of a loss function to quantify our unhappiness with a model’s predictions, and discuss two commonly used loss functions for image classification: the multiclass SVM loss and the multinomial logistic regression loss. We introduce the idea of regularization as a mechanism to fight overfitting, with weight decay as a concrete example. We introduce the idea of optimization and the stochastic gradient descent algorithm. We also briefly discuss the use of feature representations in computer vision. Keywords: Image classification, linear classifiers, SVM loss, regularization, multinomial logistic regression, optimization, stochastic gradient descent Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture3...

    published: 11 Aug 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
  • 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
  • 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
  • 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
  • Ray Kurzweil | Our Brain Is a Blueprint for the Master Algorithm | Singularity Hub

    Ray Kurzweil is an inventor, thinker, and futurist famous for forecasting the pace of technology and predicting the world of tomorrow. In this video, Kurzweil suggests the blueprint for the master algorithm—or a single, general purpose learning algorithm—is hidden in the brain. The brain, according to Kurzweil, consists of repeating modules that self-organize into hierarchies that build simple patterns into complex concepts. We don’t have a complete understanding of how this process works yet, but Kurzweil believes that as we study the brain more and reverse engineer what we find, we’ll learn to write the master algorithm. Hub Article: https://wp.me/phyoN-t5u Subscribe: http://bit.ly/1Wq6gwm Connect with Singularity University: Website: http://su.org Hub: http://singularityhub.com Face...

    published: 30 Jun 2017
  • 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
  • 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
  • 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
  • 12. Clustering

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

    published: 19 May 2017
  • 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
What is Optimization? + Learning Gradient Descent | Two Minute Papers #82

What is Optimization? + Learning Gradient Descent | Two Minute Papers #82

  • Order:
  • Duration: 5:31
  • Updated: 29 Jul 2016
  • views: 7667
videos
Let's talk about what mathematical optimization is, how gradient descent can solve simpler optimization problems, and Google DeepMind's proposed algorithm that automatically learn optimization algorithms. The paper "Learning to learn by gradient descent by gradient descent" is available here: http://arxiv.org/pdf/1606.04474v1.pdf Source code: https://github.com/deepmind/learning-to-learn ______________________________ Recommended for you: Gradients, Poisson's Equation and Light Transport - https://www.youtube.com/watch?v=sSnDTPjfBYU WE WOULD LIKE TO THANK OUR GENEROUS PATREON SUPPORTERS WHO MAKE TWO MINUTE PAPERS POSSIBLE: David Jaenisch, Sunil Kim, Julian Josephs, Daniel John Benton. https://www.patreon.com/TwoMinutePapers We also thank Experiment for sponsoring our series. - https://experiment.com/ Subscribe if you would like to see more of these! - http://www.youtube.com/subscription_center?add_user=keeroyz The chihuahua vs muffin image is a courtesy of teenybiscuit - https://twitter.com/teenybiscuit More fun stuff here: http://twistedsifter.com/2016/03/puppy-or-bagel-meme-gallery/ The thumbnail background image was created by Alan Levine - https://flic.kr/p/vbEd1W Splash screen/thumbnail design: Felícia Fehér - http://felicia.hu Károly Zsolnai-Fehér's links: Facebook → https://www.facebook.com/TwoMinutePapers/ Twitter → https://twitter.com/karoly_zsolnai Web → https://cg.tuwien.ac.at/~zsolnai/
https://wn.com/What_Is_Optimization_Learning_Gradient_Descent_|_Two_Minute_Papers_82
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: 774
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: Graph Search, DFS and BFS

Algorithms: Graph Search, DFS and BFS

  • Order:
  • Duration: 11:49
  • Updated: 27 Sep 2016
  • views: 127043
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
R11. Principles of Algorithm Design

R11. Principles of Algorithm Design

  • Order:
  • Duration: 58:26
  • Updated: 14 Jan 2013
  • views: 27307
videos
MIT 6.006 Introduction to Algorithms, Fall 2011 View the complete course: http://ocw.mit.edu/6-006F11 Instructor: Victor Costan License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
https://wn.com/R11._Principles_Of_Algorithm_Design
Algorithm using Flowchart and Pseudo code Level 1 Flowchart

Algorithm using Flowchart and Pseudo code Level 1 Flowchart

  • Order:
  • Duration: 5:41
  • Updated: 27 Aug 2013
  • views: 361485
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
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: 46050
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
13. Classification

13. Classification

  • Order:
  • Duration: 49:54
  • Updated: 19 May 2017
  • views: 5410
videos
MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: John Guttag Prof. Guttag 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
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: 2575
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
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: 1283437
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
How Random Forest algorithm works

How Random Forest algorithm works

  • Order:
  • Duration: 5:47
  • Updated: 04 Apr 2014
  • views: 205250
videos
In this video I explain very briefly how the Random Forest algorithm works with a simple example composed by 4 decision trees.
https://wn.com/How_Random_Forest_Algorithm_Works
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: 35483
videos
Hey everyone! Glad to be back! Decision Tree classifiers are intuitive, interpretable, and one of my favorite supervised learning algorithms. In this episode, I’ll walk you through writing a Decision Tree classifier from scratch, in pure Python. I’ll introduce concepts including Decision Tree Learning, Gini Impurity, and Information Gain. Then, we’ll code it all up. Understanding how to accomplish this was helpful to me when I studied Machine Learning for the first time, and I hope it will prove useful to you as well. You can find the code from this video here: https://goo.gl/UdZoNr https://goo.gl/ZpWYzt Books! Hands-On Machine Learning with Scikit-Learn and TensorFlow https://goo.gl/kM0anQ Follow Josh on Twitter: https://twitter.com/random_forests Check out more Machine Learning Recipes here: https://goo.gl/KewA03 Subscribe to the Google Developers channel: http://goo.gl/mQyv5L
https://wn.com/Let’S_Write_A_Decision_Tree_Classifier_From_Scratch_Machine_Learning_Recipes_8
Sorting in Python  ||  Learn Python Programming  (Computer Science)

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

  • Order:
  • Duration: 6:24
  • Updated: 08 Oct 2017
  • views: 10871
videos
Sorting is a fundamental task in software engineering. In Python, there are a variety of ways to sort lists, tuples, and other objects. Today we talk about the sort() method which is an in-place algorithm for sorting lists. We also cover the sorted() function which can be used on more objects, and creates a sorted copy, leaving the original object unchanged. We were able to make this Python video with the help of our Patrons on Patreon! We would like to recognize the generosity of our VIP Patrons Matt Peters, Andrew Mengede, Martin Stephens, and Markie Ward. Thank you so much for helping us continue our work! ➢➢➢➢➢➢➢➢➢➢ To​ ​help​ us continue making videos,​ ​you​ ​can​ ​support​ Socratica at: ​Patreon​: https://www.patreon.com/socratica Socratica Paypal: https://www.paypal.me/socratica We also accept Bitcoin! :) Our​ ​address​ ​is: 1EttYyGwJmpy9bLY2UcmEqMJuBfaZ1HdG9 Thank​ ​you!! ➢➢➢➢➢➢➢➢➢➢ If you’d like a reference book, we recommend “Python Cookbook, 3rd Edition” from O’Reilly: http://amzn.to/2sCNYlZ The Mythical Man Month - Essays on Software Engineering & Project Management http://amzn.to/2tYdNeP ➢➢➢➢➢➢➢➢➢➢ You​ ​can​ ​also​ ​follow​ ​Socratica​ ​on: -​ ​Twitter:​ ​@socratica -​ ​Instagram:​ ​@SocraticaStudios -​ ​Facebook:​ ​@SocraticaStudios ➢➢➢➢➢➢➢➢➢➢ Python instructor: Ulka Simone Mohanty (@ulkam on Twitter) Written & Produced by Michael Harrison (@mlh496 on Twitter)
https://wn.com/Sorting_In_Python_||_Learn_Python_Programming_(Computer_Science)
Java Programming

Java Programming

  • Order:
  • Duration: 34:30
  • Updated: 03 Jun 2014
  • views: 2994133
videos
Cheat Sheet is Here : http://goo.gl/OPMjte Slower Java Tutorial : http://goo.gl/UHdlyP How to Install Java & Eclipse : http://goo.gl/vEEEJE Best Java Book : http://amzn.to/2l27h2h Support Me on Patreon : https://www.patreon.com/derekbanas In this Java programming Tutorial I'll teach you all of the core knowledge needed to write Java code in 30 minutes. This is the most popular request from everyone. I specifically cover the following topics: primitive data types, comments, class, import, Scanner, final, Strings, static, private, protected, public, constructors, math, hasNextLine, nextLine, getters, setters, method overloading, Random, casting, toString, conversion from Strings to primitives, converting from primitives to Strings, if, else, else if, print, println, printf, logical operators, comparison operators, ternary operator, switch, for, while, break, continue, do while, polymorphism, arrays, for each, multidimensional arrays and more.
https://wn.com/Java_Programming
Introduction To Optimization: Gradient Based Algorithms

Introduction To Optimization: Gradient Based Algorithms

  • Order:
  • Duration: 5:27
  • Updated: 29 Mar 2017
  • views: 1172
videos
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 3 | Loss Functions and Optimization

Lecture 3 | Loss Functions and Optimization

  • Order:
  • Duration: 1:14:40
  • Updated: 11 Aug 2017
  • views: 39148
videos
Lecture 3 continues our discussion of linear classifiers. We introduce the idea of a loss function to quantify our unhappiness with a model’s predictions, and discuss two commonly used loss functions for image classification: the multiclass SVM loss and the multinomial logistic regression loss. We introduce the idea of regularization as a mechanism to fight overfitting, with weight decay as a concrete example. We introduce the idea of optimization and the stochastic gradient descent algorithm. We also briefly discuss the use of feature representations in computer vision. Keywords: Image classification, linear classifiers, SVM loss, regularization, multinomial logistic regression, optimization, stochastic gradient descent Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture3.pdf -------------------------------------------------------------------------------------- Convolutional Neural Networks for Visual Recognition Instructors: Fei-Fei Li: http://vision.stanford.edu/feifeili/ Justin Johnson: http://cs.stanford.edu/people/jcjohns/ Serena Yeung: http://ai.stanford.edu/~syyeung/ Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. Website: http://cs231n.stanford.edu/ For additional learning opportunities please visit: http://online.stanford.edu/
https://wn.com/Lecture_3_|_Loss_Functions_And_Optimization
'The Algorithm' - How YouTube Search & Discovery Works

'The Algorithm' - How YouTube Search & Discovery Works

  • Order:
  • Duration: 2:02
  • Updated: 28 Aug 2017
  • views: 107429
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
Lecture 10 - Neural Networks

Lecture 10 - Neural Networks

  • Order:
  • Duration: 1:25:16
  • Updated: 06 May 2012
  • views: 303277
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
Swift Fun Algorithms #4: Most Common Name in Array

Swift Fun Algorithms #4: Most Common Name in Array

  • Order:
  • Duration: 10:16
  • Updated: 11 May 2016
  • views: 3427
videos
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
12. Greedy Algorithms: Minimum Spanning Tree

12. Greedy Algorithms: Minimum Spanning Tree

  • Order:
  • Duration: 1:22:10
  • Updated: 04 Mar 2016
  • views: 42086
videos
MIT 6.046J Design and Analysis of Algorithms, Spring 2015 View the complete course: http://ocw.mit.edu/6-046JS15 Instructor: Erik Demaine In this lecture, Professor Demaine introduces greedy algorithms, which make locally-best choices without regards to the future. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
https://wn.com/12._Greedy_Algorithms_Minimum_Spanning_Tree
Ray Kurzweil | Our Brain Is a Blueprint for the Master Algorithm | Singularity Hub

Ray Kurzweil | Our Brain Is a Blueprint for the Master Algorithm | Singularity Hub

  • Order:
  • Duration: 7:50
  • Updated: 30 Jun 2017
  • views: 7352
videos
Ray Kurzweil is an inventor, thinker, and futurist famous for forecasting the pace of technology and predicting the world of tomorrow. In this video, Kurzweil suggests the blueprint for the master algorithm—or a single, general purpose learning algorithm—is hidden in the brain. The brain, according to Kurzweil, consists of repeating modules that self-organize into hierarchies that build simple patterns into complex concepts. We don’t have a complete understanding of how this process works yet, but Kurzweil believes that as we study the brain more and reverse engineer what we find, we’ll learn to write the master algorithm. Hub Article: https://wp.me/phyoN-t5u Subscribe: http://bit.ly/1Wq6gwm Connect with Singularity University: Website: http://su.org Hub: http://singularityhub.com Facebook: https://www.facebook.com/singularityu Twitter: https://twitter.com/singularityu Linkedin: https://www.linkedin.com/company/singularity-university About Singularity University: Singularity University is a benefit corporation headquartered at NASA’s research campus in Silicon Valley. We provide educational programs, innovative partnerships and a startup accelerator to help individuals, businesses, institutions, investors, NGOs and governments understand cutting-edge technologies, and how to utilize these technologies to positively impact billions of people. Singularity University http://www.youtube.com/user/SingularityU
https://wn.com/Ray_Kurzweil_|_Our_Brain_Is_A_Blueprint_For_The_Master_Algorithm_|_Singularity_Hub
Bias? In My Algorithms? A Facebook News Story

Bias? In My Algorithms? A Facebook News Story

  • Order:
  • Duration: 10:55
  • Updated: 14 Sep 2016
  • views: 77107
videos
Why Facebook News Can’t Escape Bias Tweet us! http://bit.ly/pbsideachanneltwitter Idea Channel Facebook! http://bit.ly/pbsideachannelfacebook Talk about this episode on reddit! http://bit.ly/pbsideachannelreddit Idea Channel IRC! http://bit.ly/pbsideachannelirc Email us! pbsideachannel [at] gmail [dot] com Support Idea Channel on Patreon! http://www.patreon.com/pbsideachannel In case you missed the news because it wasn’t trending on Facebook, Facebook’s Trending News Team has been… in the news. Not long ago the whole department got the axe after Gizmodo reported they’d been suppressing conservative news items and sources. This caused a stir. And perhaps rightfully so: facebook is used by all stripes of people with all manner of beliefs and politics and it is where those people go to get their news. It’d be dismaying, to say the least, to learn your news source suppresses topics most important to you. In light of this whole thing, there are, then, two questions I want to ask. The first is … why is there an expectation of zero bias from facebook? And second… what does facebook do in light of that expectation? Let us know what you think in the comments below! ---SOURCES--- Facebook Swaying Public Opinion http://www1.udel.edu/udaily/2016/sep/politics-social-media-092315.html http://www.nytimes.com/2012/09/13/us/politics/social-networks-affect-voter-turnout-study-finds.html?_r=0 https://www.theguardian.com/commentisfree/2016/apr/19/donald-trump-facebook-election-manipulate-behavior http://www.motherjones.com/politics/2014/10/can-voting-facebook-button-improve-voter-turnout http://mashable.com/2014/07/02/facebook-sandberg-emotions-experiment/#Riq1FvcMqsqT Bias in Language https://freedom-to-tinker.com/2016/08/24/language-necessarily-contains-human-biases-and-so-will-machines-trained-on-language-corpora/ Bias in Computer Algorithms https://socialmediacollective.org/reading-lists/critical-algorithm-studies/ ---FURTHER READING--- http://www.nytimes.com/2016/05/12/technology/facebooks-bias-is-built-in-and-bears-watching.html?_r=0 http://gizmodo.com/former-facebook-workers-we-routinely-suppressed-conser-1775461006 http://www.wsj.com/articles/facebook-refutes-criticisms-about-a-bias-against-conservatives-1462890206 http://money.cnn.com/2016/05/10/technology/facebook-news-senate/index.html http://digiday.com/platforms/former-facebook-trending-news-editor-just-going-get-rid-product-altogether/ http://technical.ly/brooklyn/2016/06/08/fred-benenson-mathwashing-facebook-data-worship/ http://www.wsj.com/articles/facebooks-trending-feature-exhibits-flaws-under-new-algorithm-1473176652 ---CHECK OUT OUR MERCH!--- http://bit.ly/1U8fS1B T-Shirts Designed by: http://artsparrow.com/ ---TWEET OF THE WEEK--- https://twitter.com/1212thedoctor/status/775409364207276032 ---ASSET LINKS--- 00:24 Gizmodo Article http://gizmodo.com/former-facebook-workers-we-routinely-suppressed-conser-1775461006 00:41 NY Times Article http://www.nytimes.com/2016/05/12/technology/facebooks-bias-is-built-in-and-bears-watching.html?_r=0 00:57 The Facebook Effect http://www1.udel.edu/udaily/2016/sep/politics-social-media-092315.html 1:11 Sriracha http://theoatmeal.com/comics/sriracha 2:35 Idea Channel Serial Part 2 https://www.youtube.com/watch?v=xT0yRXWo6UU 3:18 Billy on the Street https://www.youtube.com/watch?v=lz9HhVMAG8E&feature=youtu.be&t=106 3:40 Updated Beginners Guide to Facebook (2015) https://www.youtube.com/watch?v=YMr4M4ponm8 4:53 Media Matters http://mediamatters.org/blog/2012/11/03/fox-news-redefines-unbalanced-by-giving-romney/191118 5:58 Language Bias https://freedom-to-tinker.com/2016/08/24/language-necessarily-contains-human-biases-and-so-will-machines-trained-on-language-corpora/ 6:20 Mathwashing http://technical.ly/brooklyn/2016/06/08/fred-benenson-mathwashing-facebook-data-worship/ 7:52 Facebook Trending Illustration by Jim Cooke http://jimcookeillustration.tumblr.com/ 8:07 Wall Street Journal http://www.wsj.com/articles/facebooks-trending-feature-exhibits-flaws-under-new-algorithm-1473176652 ---MUSIC--- https://soundcloud.com/montone-2/minimalist ----------------------------------------­­­­­­­­­­­­­­­­­------------------------­-­-­-­- Written and hosted by Mike Rugnetta (@mikerugnetta) (who also has a podcast! Reasonably Sound: http://bit.ly/1sCn0BF) Made by Kornhaber Brown (http://www.kornhaberbrown.com)
https://wn.com/Bias_In_My_Algorithms_A_Facebook_News_Story
What Makes a Good Feature? - Machine Learning Recipes #3

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

  • Order:
  • Duration: 5:41
  • Updated: 27 Apr 2016
  • views: 217949
videos
Good features are informative, independent, and simple. In this episode, we'll introduce these concepts by using a histogram to visualize a feature from a toy dataset. Updates: many thanks for the supportive feedback! I’d love to release these episodes faster, but I’m writing them as we go. That way, I can see what works and (more importantly) where I can improve. We've covered a lot of ground already, so next episode I'll review and reinforce concepts, introduce clearer syntax, spend more time on testing, and continue building intuition for supervised learning. I also realize some folks had dependency bugs with Graphviz (my fault!). Moving forward, I won't use any libraries not already installed by Anaconda or Tensorflow. Last: my code in this cast is similar to these great examples. You can use them to produce a more polished chart, if you like: http://matplotlib.org/examples/statistics/histogram_demo_multihist.html Follow https://twitter.com/random_forests for updates on new episodes! Subscribe to the Google Developers: http://goo.gl/mQyv5L - Subscribe to the brand new Firebase Channel: https://goo.gl/9giPHG And here's our playlist: https://goo.gl/KewA03
https://wn.com/What_Makes_A_Good_Feature_Machine_Learning_Recipes_3
Tensorflow and deep learning - without a PhD by Martin Görner

Tensorflow and deep learning - without a PhD by Martin Görner

  • Order:
  • Duration: 2:35:53
  • Updated: 09 Nov 2016
  • views: 289635
videos
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
12. Clustering

12. Clustering

  • Order:
  • Duration: 50:40
  • Updated: 19 May 2017
  • views: 13238
videos
MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: John Guttag Prof. Guttag discusses clustering. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
https://wn.com/12._Clustering
I'M A LOSER, DON'T WATCH ME

I'M A LOSER, DON'T WATCH ME

  • Order:
  • Duration: 8:15
  • Updated: 03 Aug 2016
  • views: 361554
videos
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

  • Order:
  • Duration: 15:38
  • Updated: 02 Jun 2017
  • views: 89759
videos
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

  • Order:
  • Duration: 1:23:45
  • Updated: 01 Sep 2016
  • views: 12515
videos
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

  • Order:
  • Duration: 9:55
  • Updated: 07 Mar 2016
  • views: 6307
videos
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)

  • Order:
  • Duration: 22:06
  • Updated: 25 Jul 2017
  • views: 2199
videos
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)