• 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
  • 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
  • How to make your greatest investment | Rachel Fox | TEDxTeen

    Rachel became involved with investing and trading at 15. Before trading, she was looking for ways to grow her savings and become financially independent at a young age. Her parents were very financially aware and infused that into her life early on. At 15, Rachel began researching how to invest and trade on the Internet. She opened her first joint online trading account with her parents, started with stocks, and 3.5 years later, she is currently trading stocks, futures and options successfully. Rachel has been working as an entertainer since she was 5-years-old. She has grown up performing and entertaining via music and film, and will continue to do so for the rest of her life. Rachel and her band, No Babies No Bullets have played at different music festivals and venues through the West C...

    published: 16 Jun 2015
  • 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
  • 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
  • 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
  • Boosting

    This video is part of the Udacity course "Machine Learning for Trading". Watch the full course at https://www.udacity.com/course/ud501

    published: 06 Jun 2016
  • 1. Algorithmic Thinking, Peak Finding

    MIT 6.006 Introduction to Algorithms, Fall 2011 View the complete course: http://ocw.mit.edu/6-006F11 Instructor: Srini Devadas 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
  • 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
  • What Makes a Good Feature? - Machine Learning Recipes #3

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

    published: 27 Apr 2016
  • Algorithms & Data Structures & C programming = Power

    The first 100 Students will get a 40% price discounts. https://www.udemy.com/clang-algo-ds/?couponCode=CLANG_ALGO_DS_POWER Enroll now and let the journey begins

    published: 28 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
  • 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
  • Dijkstra's Algorithm Single Source Shortest Path Graph Algorithm

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

    published: 28 Oct 2015
  • 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
Algorithms: Graph Search, DFS and BFS

Algorithms: Graph Search, DFS and BFS

  • Order:
  • Duration: 11:49
  • Updated: 27 Sep 2016
  • views: 83602
videos
Learn the basics of graph search and common operations; Depth First Search (DFS) and Breadth First Search (BFS). This video is a part of HackerRank's Cracking The Coding Interview Tutorial with Gayle Laakmann McDowell. http://www.hackerrank.com/domains/tutorials/cracking-the-coding-interview?utm_source=video&utm_medium=youtube&utm_campaign=ctci
https://wn.com/Algorithms_Graph_Search,_Dfs_And_Bfs
Brian Christian & Tom Griffiths: "Algorithms to Live By" | Talks at Google

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

  • Order:
  • Duration: 1:07:28
  • Updated: 12 May 2016
  • views: 40440
videos
Practical, everyday advice which will easily provoke an interest in computer science. In a dazzlingly interdisciplinary work, acclaimed author Brian Christian and cognitive scientist Tom Griffiths show how the algorithms used by computers can also untangle very human questions. They explain how to have better hunches and when to leave things to chance, how to deal with overwhelming choices and how best to connect with others. From finding a spouse to finding a parking spot, from organizing one's inbox to understanding the workings of memory, Algorithms to Live By transforms the wisdom of computer science into strategies for human living. Brian Christian is the author of The Most Human Human, a Wall Street Journal bestseller, New York Times editors’ choice, and a New Yorker favorite book of the year. His writing has appeared in The New Yorker, The Atlantic, Wired, The Wall Street Journal, The Guardian, and The Paris Review, as well as in scientific journals such as Cognitive Science, and has been translated into eleven languages. He lives in San Francisco. Tom Griffiths is a professor of psychology and cognitive science at UC Berkeley, where he directs the Computational Cognitive Science Lab. He has published more than 150 scientific papers on topics ranging from cognitive psychology to cultural evolution, and has received awards from the National Science Foundation, the Sloan Foundation, the American Psychological Association, and the Psychonomic Society, among others. He lives in Berkeley. On behalf of Talks at Google this talk was hosted by Boris Debic. eBook https://play.google.com/store/books/details/Brian_Christian_Algorithms_to_Live_By?id=yvaLCgAAQBAJ
https://wn.com/Brian_Christian_Tom_Griffiths_Algorithms_To_Live_By_|_Talks_At_Google
How to make your greatest investment | Rachel Fox | TEDxTeen

How to make your greatest investment | Rachel Fox | TEDxTeen

  • Order:
  • Duration: 17:21
  • Updated: 16 Jun 2015
  • views: 96029
videos
Rachel became involved with investing and trading at 15. Before trading, she was looking for ways to grow her savings and become financially independent at a young age. Her parents were very financially aware and infused that into her life early on. At 15, Rachel began researching how to invest and trade on the Internet. She opened her first joint online trading account with her parents, started with stocks, and 3.5 years later, she is currently trading stocks, futures and options successfully. Rachel has been working as an entertainer since she was 5-years-old. She has grown up performing and entertaining via music and film, and will continue to do so for the rest of her life. Rachel and her band, No Babies No Bullets have played at different music festivals and venues through the West Coast, including Sundance Film Festival 2015, NAMM, The Viper Room and others. She has also worked on films and television shows with Daniel Craig, Naomi Watts, Patricia Arquette, Daryl Hannah, Michael Chiklis and many more. Since Rachel was home-schooled for high school and finished early, she began blogging about finance and investing to continue while in-between working on sets and playing music. Her blog is read by people both young and old who want basic answers to their finance questions and want to start investing. Rachel was on CNBC, YahooFinance and Fox Business to discuss Fox on Stocks and trading as well as named one of Time Magazine's 25 Top Most Influential Teens of 2014. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
https://wn.com/How_To_Make_Your_Greatest_Investment_|_Rachel_Fox_|_Tedxteen
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: 6593
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: 467
videos
This is the start to a new series, and I hope to teach you guys all the tricks and tips you need to becoming a successful programmer! If you're interested in more videos, and you want to continue to get better at programming, please subscribe for all future episodes!
https://wn.com/How_To_Program_For_Beginners_|_Episode_1_Algorithms
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: 2605
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
How Random Forest algorithm works

How Random Forest algorithm works

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

Boosting

  • Order:
  • Duration: 2:25
  • Updated: 06 Jun 2016
  • views: 21528
videos
This video is part of the Udacity course "Machine Learning for Trading". Watch the full course at https://www.udacity.com/course/ud501
https://wn.com/Boosting
1. Algorithmic Thinking, Peak Finding

1. Algorithmic Thinking, Peak Finding

  • Order:
  • Duration: 53:22
  • Updated: 14 Jan 2013
  • views: 1031031
videos
MIT 6.006 Introduction to Algorithms, Fall 2011 View the complete course: http://ocw.mit.edu/6-006F11 Instructor: Srini Devadas License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
https://wn.com/1._Algorithmic_Thinking,_Peak_Finding
Practical Machine Learning Tutorial with Python Intro p.1

Practical Machine Learning Tutorial with Python Intro p.1

  • Order:
  • Duration: 5:55
  • Updated: 11 Apr 2016
  • views: 382073
videos
The objective of this course is to give you a wholistic understanding of machine learning, covering theory, application, and inner workings of supervised, unsupervised, and deep learning algorithms. In this series, we'll be covering linear regression, K Nearest Neighbors, Support Vector Machines (SVM), flat clustering, hierarchical clustering, and neural networks. For each major algorithm that we cover, we will discuss the high level intuitions of the algorithms and how they are logically meant to work. Next, we'll apply the algorithms in code using real world data sets along with a module, such as with Scikit-Learn. Finally, we'll be diving into the inner workings of each of the algorithms by recreating them in code, from scratch, ourselves, including all of the math involved. This should give you a complete understanding of exactly how the algorithms work, how they can be tweaked, what advantages are, and what their disadvantages are. In order to follow along with the series, I suggest you have at the very least a basic understanding of Python. If you do not, I suggest you at least follow the Python 3 Basics tutorial until the module installation with pip tutorial. If you have a basic understanding of Python, and the willingness to learn/ask questions, you will be able to follow along here with no issues. Most of the machine learning algorithms are actually quite simple, since they need to be in order to scale to large datasets. Math involved is typically linear algebra, but I will do my best to still explain all of the math. If you are confused/lost/curious about anything, ask in the comments section on YouTube, the community here, or by emailing me. You will also need Scikit-Learn and Pandas installed, along with others that we'll grab along the way. Machine learning was defined in 1959 by Arthur Samuel as the "field of study that gives computers the ability to learn without being explicitly programmed." This means imbuing knowledge to machines without hard-coding it. https://pythonprogramming.net/machine-learning-tutorial-python-introduction/ https://twitter.com/sentdex https://www.facebook.com/pythonprogra... https://plus.google.com/+sentdex
https://wn.com/Practical_Machine_Learning_Tutorial_With_Python_Intro_P.1
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: 174758
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
Algorithms & Data Structures & C programming = Power

Algorithms & Data Structures & C programming = Power

  • Order:
  • Duration: 2:21
  • Updated: 28 Jun 2017
  • views: 705
videos
The first 100 Students will get a 40% price discounts. https://www.udemy.com/clang-algo-ds/?couponCode=CLANG_ALGO_DS_POWER Enroll now and let the journey begins
https://wn.com/Algorithms_Data_Structures_C_Programming_Power
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: 75842
videos
Why Facebook News Can’t Escape Bias Tweet us! http://bit.ly/pbsideachanneltwitter Idea Channel Facebook! http://bit.ly/pbsideachannelfacebook Talk about this episode on reddit! http://bit.ly/pbsideachannelreddit Idea Channel IRC! http://bit.ly/pbsideachannelirc Email us! pbsideachannel [at] gmail [dot] com Support Idea Channel on Patreon! http://www.patreon.com/pbsideachannel In case you missed the news because it wasn’t trending on Facebook, Facebook’s Trending News Team has been… in the news. Not long ago the whole department got the axe after Gizmodo reported they’d been suppressing conservative news items and sources. This caused a stir. And perhaps rightfully so: facebook is used by all stripes of people with all manner of beliefs and politics and it is where those people go to get their news. It’d be dismaying, to say the least, to learn your news source suppresses topics most important to you. In light of this whole thing, there are, then, two questions I want to ask. The first is … why is there an expectation of zero bias from facebook? And second… what does facebook do in light of that expectation? Let us know what you think in the comments below! ---SOURCES--- Facebook Swaying Public Opinion http://www1.udel.edu/udaily/2016/sep/politics-social-media-092315.html http://www.nytimes.com/2012/09/13/us/politics/social-networks-affect-voter-turnout-study-finds.html?_r=0 https://www.theguardian.com/commentisfree/2016/apr/19/donald-trump-facebook-election-manipulate-behavior http://www.motherjones.com/politics/2014/10/can-voting-facebook-button-improve-voter-turnout http://mashable.com/2014/07/02/facebook-sandberg-emotions-experiment/#Riq1FvcMqsqT Bias in Language https://freedom-to-tinker.com/2016/08/24/language-necessarily-contains-human-biases-and-so-will-machines-trained-on-language-corpora/ Bias in Computer Algorithms https://socialmediacollective.org/reading-lists/critical-algorithm-studies/ ---FURTHER READING--- http://www.nytimes.com/2016/05/12/technology/facebooks-bias-is-built-in-and-bears-watching.html?_r=0 http://gizmodo.com/former-facebook-workers-we-routinely-suppressed-conser-1775461006 http://www.wsj.com/articles/facebook-refutes-criticisms-about-a-bias-against-conservatives-1462890206 http://money.cnn.com/2016/05/10/technology/facebook-news-senate/index.html http://digiday.com/platforms/former-facebook-trending-news-editor-just-going-get-rid-product-altogether/ http://technical.ly/brooklyn/2016/06/08/fred-benenson-mathwashing-facebook-data-worship/ http://www.wsj.com/articles/facebooks-trending-feature-exhibits-flaws-under-new-algorithm-1473176652 ---CHECK OUT OUR MERCH!--- http://bit.ly/1U8fS1B T-Shirts Designed by: http://artsparrow.com/ ---TWEET OF THE WEEK--- https://twitter.com/1212thedoctor/status/775409364207276032 ---ASSET LINKS--- 00:24 Gizmodo Article http://gizmodo.com/former-facebook-workers-we-routinely-suppressed-conser-1775461006 00:41 NY Times Article http://www.nytimes.com/2016/05/12/technology/facebooks-bias-is-built-in-and-bears-watching.html?_r=0 00:57 The Facebook Effect http://www1.udel.edu/udaily/2016/sep/politics-social-media-092315.html 1:11 Sriracha http://theoatmeal.com/comics/sriracha 2:35 Idea Channel Serial Part 2 https://www.youtube.com/watch?v=xT0yRXWo6UU 3:18 Billy on the Street https://www.youtube.com/watch?v=lz9HhVMAG8E&feature=youtu.be&t=106 3:40 Updated Beginners Guide to Facebook (2015) https://www.youtube.com/watch?v=YMr4M4ponm8 4:53 Media Matters http://mediamatters.org/blog/2012/11/03/fox-news-redefines-unbalanced-by-giving-romney/191118 5:58 Language Bias https://freedom-to-tinker.com/2016/08/24/language-necessarily-contains-human-biases-and-so-will-machines-trained-on-language-corpora/ 6:20 Mathwashing http://technical.ly/brooklyn/2016/06/08/fred-benenson-mathwashing-facebook-data-worship/ 7:52 Facebook Trending Illustration by Jim Cooke http://jimcookeillustration.tumblr.com/ 8:07 Wall Street Journal http://www.wsj.com/articles/facebooks-trending-feature-exhibits-flaws-under-new-algorithm-1473176652 ---MUSIC--- https://soundcloud.com/montone-2/minimalist ----------------------------------------­­­­­­­­­­­­­­­­­------------------------­-­-­-­- Written and hosted by Mike Rugnetta (@mikerugnetta) (who also has a podcast! Reasonably Sound: http://bit.ly/1sCn0BF) Made by Kornhaber Brown (http://www.kornhaberbrown.com)
https://wn.com/Bias_In_My_Algorithms_A_Facebook_News_Story
Lecture 10 - Neural Networks

Lecture 10 - Neural Networks

  • Order:
  • Duration: 1:25:16
  • Updated: 06 May 2012
  • views: 286621
videos
Neural Networks - A biologically inspired model. The efficient backpropagation learning algorithm. Hidden layers. Lecture 10 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes.apple.com/us/course/machine-learning/id515364596 and on the course website - http://work.caltech.edu/telecourse.html Produced in association with Caltech Academic Media Technologies under the Attribution-NonCommercial-NoDerivs Creative Commons License (CC BY-NC-ND). To learn more about this license, http://creativecommons.org/licenses/by-nc-nd/3.0/ This lecture was recorded on May 3, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA.
https://wn.com/Lecture_10_Neural_Networks
Dijkstra's Algorithm Single Source Shortest Path Graph Algorithm

Dijkstra's Algorithm Single Source Shortest Path Graph Algorithm

  • Order:
  • Duration: 16:20
  • Updated: 28 Oct 2015
  • views: 162291
videos
Find single source shortest path using Dijkstra algorithm https://www.facebook.com/tusharroy25 https://github.com/mission-peace/interview/blob/master/src/com/interview/graph/DijkstraShortestPath.java https://github.com/mission-peace/interview/wiki
https://wn.com/Dijkstra's_Algorithm_Single_Source_Shortest_Path_Graph_Algorithm
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: 243382
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
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