• What's an algorithm? - David J. Malan

    View full lesson: http://ed.ted.com/lessons/your-brain-can-solve-algorithms-david-j-malan An algorithm is a mathematical method of solving problems both big and small. Though computers run algorithms constantly, humans can also solve problems with algorithms. David J. Malan explains how algorithms can be used in seemingly simple situations and also complex ones. Lesson by David J. Malan, animation by enjoyanimation.

    published: 20 May 2013
  • Developing Algorithms (Previous Release)

    Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Develop algorithms using the high-level language and development tools in MATLAB. Download code and watch other videos at http://mathworks.com/video

    published: 19 Feb 2009
  • Concepts of Algorithm, Flow Chart & C Programming

    Concepts of Algorithm, Flow Chart & C Programming by Prof. Wongmulin | Dept. of Computer Science Garden City College-Bangalore

    published: 31 Mar 2012
  • How To Learn Algorithms?

    SUBSCRIBE TO THIS CHANNEL: vid.io/xokz Learning Algorithms: Is It Really Necessary?: https://www.youtube.com/watch?v=FJcG-6g4wA4&index=16&list=PLjwWT1Xy3c4XYAbLKJ54J7eC5ft01eFeB Preparing For A Job Interview Pluralsight Course: https://simpleprogrammer.com/pluralsightinterview Becoming A Better Developer With Programming Challenges: https://simpleprogrammer.com/programmingchallenges Top Coder: https://simpleprogrammer.com/topcoder Codility: https://simpleprogrammer.com/codility Cracking The Coding Interview: https://simpleprogrammer.com/codinginterview Programming Pearls: https://simpleprogrammer.com/programmingpearls How To Learn Algorithms? Recently, I did a video about algorithms and whether it was a good idea to learn it or not. I see a lot of programmers making much noise about alg...

    published: 30 Dec 2016
  • How to Get Better At Writing Algorithms

    Sponsors: Dev Mountain Coding Bootcamp https://goo.gl/P4vgKS Other Links: Check out my tutorials, blogs and more at my website https://www.hipstercode.com/ -~-~~-~~~-~~-~- Algorithm Websites: https://www.codewars.com/ https://projecteuler.net/archives Check out my Podcast on iTunes ... https://itunes.apple.com/us/podcast/chris-hawkes/id1127177596?mt=2

    published: 22 Jul 2016
  • Hash Algorithms - Web Development

    This video is part of an online course, Web Development. Check out the course here: https://www.udacity.com/course/cs253.

    published: 23 Feb 2015
  • Solving Programming Problems

    Ge the Code Here: http://goo.gl/R6R1F To finish off my Java Algorithm tutorial, I thought it would be interesting to cover solving programming problems in general. So, in this tutorial I'll answer the question I've been getting, which is how to print a tree data structure. On our journey to better understand how to solve problems I will first solve the basic problem. Then in the next part of the tutorial I will perfect printing any type of tree. The code above will better explain the process of solving this problem.

    published: 05 Apr 2013
  • O'Reilly Webcast: How to Develop Language Annotations for Machine Learning Algorithms

    Text-based data mining and information extraction systems that make use of machine learning techniques require annotated datasets for training the algorithms. In this webcast presented by James Pustejovsky and Amber Stubbs, we will discuss the steps involved in creating your own training corpus for such machine learning algorithms. We walk you through: The annotation cycle Selecting an annotation task Creating the annotation specification Designing the guidelines Creating a "gold standard" corpus Beginning the actual data creation with the annotation process We then mention the most relevant machine learning algorithms for natural language data and tasks, and provide hints for how to choose the right one for your learning task and your own dataset. Finally, we di...

    published: 19 Oct 2012
  • Algorithms Lecture 1 Part 1: Mathematical Preliminaries

    This lecture is delivered by Professor Michael Rieck, Fundamental mathematical concepts including set theory are discussed. Increasing and decreasing functions are explained. Besides learning algorithms to solve a wide range of practical problems, we will also want to develop a strong sense of how efficient these algorithms are.

    published: 15 Mar 2013
  • 9.1: Genetic Algorithm: Introduction - The Nature of Code

    Welcome to part 1 of a new series of videos focused on Evolutionary Computing, and more specifically, Genetic Algorithms. In this tutorial, I introduce the concept of a genetic algorithm, how it can be used to approach "search" problems and how it relates to brute force algorithms. Support this channel on Patreon: https://www.patreon.com/codingrainbow Send me your questions and coding challenges!: https://github.com/CodingRainbow/Rainbow-Topics Contact: https://twitter.com/shiffman Links discussed in this video: The Nature of Code: http://natureofcode.com/ BoxCar2D: http://boxcar2d.com/ Source Code for the Video Lessons: https://github.com/CodingRainbow/Rainbow-Code p5.js: https://p5js.org/ Processing: https://processing.org For More Genetic Algorithm videos: https://www.youtube.com...

    published: 29 Jul 2016
  • Java Algorithms

    Get the Code Here: http://goo.gl/2AJYt Support me on Patreon : https://www.patreon.com/derekbanas?ty=h Welcome to my Java Algorithms tutorial. In this series I will cover everything there is to know about Java algorithms and data structures. An algorithm is just the steps you take to manipulate data. A data structure is the way data is arranged in memory. There are 3 main data structure operations I will focus on first being inserting, deleting and searching for data. Like all of my tutorials, everything is simple at first and then I cover more complex topics.

    published: 25 Feb 2013
  • Machine Learning Algorithms – Part 1

    To learn more about creating a modern IT environment, click: http://aka.ms/GuideModernIT. For any novice in machine learning – the biggest challenge is to determine the algorithm to use to train the model. This tutorial attempts to identify the various use-cases for training models and which algorithm one can use in a particular use-case.

    published: 01 Jul 2016
  • Algorithms I Part 001 Dynamic Connectivity

    This course is useful for those developers who wants to develop optimized. For those who want to for for high tech companies. This course is offered on Coursera ; Princeton university, by Kevin Wayne, Robert Sedgewick Please register there for further information https://class.coursera.org/algs4partI-005

    published: 09 Jul 2014
  • Developing Training Algorithms for Convolutional Neural Networks

    Developing Training Algorithms for Convolutional Neural Networks by Susan Lamoitier ECTE953 Advanced Project November 2013 School of Electrical, Computer & Telecommunications Engineering (SECTE) at the University of Wollongong (UOW) ABSTRACT During the last decades several visual recognition problems have been investigated. Image processing permits for example face detection, face recognition, facial expression analysis, car detection, optical character recognition, or hand written digit recognition. Neural networks (NN) have been found almost unavoidable in pattern recognition. In fact recognition systems are more efficient when they focus on learning techniques. LeCun proposed convolutional neural networks (CNN) which are NN with three key architectural ideas: local receptive fields, we...

    published: 05 Nov 2013
  • 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
  • The Big Bang Theory - The Friendship Algorithm

    Season 2, episode 13 Sheldon displays his friendship algorithm as a flow chart, and tests it. (this belongs to CBS, not me, I'm just enlightening you with Sheldon's awesomeness)

    published: 20 Jan 2009
What's an algorithm? - David J. Malan

What's an algorithm? - David J. Malan

  • Order:
  • Duration: 4:58
  • Updated: 20 May 2013
  • views: 416938
videos
View full lesson: http://ed.ted.com/lessons/your-brain-can-solve-algorithms-david-j-malan An algorithm is a mathematical method of solving problems both big and small. Though computers run algorithms constantly, humans can also solve problems with algorithms. David J. Malan explains how algorithms can be used in seemingly simple situations and also complex ones. Lesson by David J. Malan, animation by enjoyanimation.
https://wn.com/What's_An_Algorithm_David_J._Malan
Developing Algorithms (Previous Release)

Developing Algorithms (Previous Release)

  • Order:
  • Duration: 6:31
  • Updated: 19 Feb 2009
  • views: 38241
videos
Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Develop algorithms using the high-level language and development tools in MATLAB. Download code and watch other videos at http://mathworks.com/video
https://wn.com/Developing_Algorithms_(Previous_Release)
Concepts of Algorithm, Flow Chart & C Programming

Concepts of Algorithm, Flow Chart & C Programming

  • Order:
  • Duration: 33:33
  • Updated: 31 Mar 2012
  • views: 394668
videos
Concepts of Algorithm, Flow Chart & C Programming by Prof. Wongmulin | Dept. of Computer Science Garden City College-Bangalore
https://wn.com/Concepts_Of_Algorithm,_Flow_Chart_C_Programming
How To Learn Algorithms?

How To Learn Algorithms?

  • Order:
  • Duration: 4:50
  • Updated: 30 Dec 2016
  • views: 7424
videos
SUBSCRIBE TO THIS CHANNEL: vid.io/xokz Learning Algorithms: Is It Really Necessary?: https://www.youtube.com/watch?v=FJcG-6g4wA4&index=16&list=PLjwWT1Xy3c4XYAbLKJ54J7eC5ft01eFeB Preparing For A Job Interview Pluralsight Course: https://simpleprogrammer.com/pluralsightinterview Becoming A Better Developer With Programming Challenges: https://simpleprogrammer.com/programmingchallenges Top Coder: https://simpleprogrammer.com/topcoder Codility: https://simpleprogrammer.com/codility Cracking The Coding Interview: https://simpleprogrammer.com/codinginterview Programming Pearls: https://simpleprogrammer.com/programmingpearls How To Learn Algorithms? Recently, I did a video about algorithms and whether it was a good idea to learn it or not. I see a lot of programmers making much noise about algorithms and well... In the end, it might not be ALL OF THIS for you to worry about it so much. I know this topic divides opinions on the internet. Some programmers might say that learning algorithms will definitely make your career stand out from the crowd, while others might say that you should not invest your time in learning algorithms because it is useless. An algorithm is a self-contained step-by-step set of operations to be performed. Algorithms perform calculation, data processing, and/or automated reasoning tasks. So, a lot of programmers started to ask me: Okay John, how do I learn algorithms after all? In this video, I'll provide you the best resources on the internet for learning algorithms and everything you need to know. If you have a question, email me at john@simpleprogrammer.com If you liked this video, share, like and, of course, subscribe! Subscribe To My YouTube Channel: http://bit.ly/1zPTNLT Visit Simple Programmer Website: http://simpleprogrammer.com/ Connect with me on social media: Facebook: https://www.facebook.com/SimpleProgrammer Twitter: https://twitter.com/jsonmez Other Links: Sign up for the Simple Programmer Newsletter: http://simpleprogrammer.com/email Simple Programmer blog: http://simpleprogrammer.com/blog Learn how to learn anything quickly: http://10stepstolearn.com Boost your career now: http://devcareerboost.com #programming #algorithms #learnalgorithms #programmingalgorithms
https://wn.com/How_To_Learn_Algorithms
How to Get Better At Writing Algorithms

How to Get Better At Writing Algorithms

  • Order:
  • Duration: 8:38
  • Updated: 22 Jul 2016
  • views: 8671
videos
Sponsors: Dev Mountain Coding Bootcamp https://goo.gl/P4vgKS Other Links: Check out my tutorials, blogs and more at my website https://www.hipstercode.com/ -~-~~-~~~-~~-~- Algorithm Websites: https://www.codewars.com/ https://projecteuler.net/archives Check out my Podcast on iTunes ... https://itunes.apple.com/us/podcast/chris-hawkes/id1127177596?mt=2
https://wn.com/How_To_Get_Better_At_Writing_Algorithms
Hash Algorithms - Web Development

Hash Algorithms - Web Development

  • Order:
  • Duration: 3:55
  • Updated: 23 Feb 2015
  • views: 354
videos
This video is part of an online course, Web Development. Check out the course here: https://www.udacity.com/course/cs253.
https://wn.com/Hash_Algorithms_Web_Development
Solving Programming Problems

Solving Programming Problems

  • Order:
  • Duration: 16:16
  • Updated: 05 Apr 2013
  • views: 67553
videos
Ge the Code Here: http://goo.gl/R6R1F To finish off my Java Algorithm tutorial, I thought it would be interesting to cover solving programming problems in general. So, in this tutorial I'll answer the question I've been getting, which is how to print a tree data structure. On our journey to better understand how to solve problems I will first solve the basic problem. Then in the next part of the tutorial I will perfect printing any type of tree. The code above will better explain the process of solving this problem.
https://wn.com/Solving_Programming_Problems
O'Reilly Webcast: How to Develop Language Annotations for Machine Learning Algorithms

O'Reilly Webcast: How to Develop Language Annotations for Machine Learning Algorithms

  • Order:
  • Duration: 1:28:06
  • Updated: 19 Oct 2012
  • views: 1439
videos
Text-based data mining and information extraction systems that make use of machine learning techniques require annotated datasets for training the algorithms. In this webcast presented by James Pustejovsky and Amber Stubbs, we will discuss the steps involved in creating your own training corpus for such machine learning algorithms. We walk you through: The annotation cycle Selecting an annotation task Creating the annotation specification Designing the guidelines Creating a "gold standard" corpus Beginning the actual data creation with the annotation process We then mention the most relevant machine learning algorithms for natural language data and tasks, and provide hints for how to choose the right one for your learning task and your own dataset. Finally, we discuss testing and evaluation of the algorithm, along with suggestions for how to revise your system depending on the resulting performance. This is a unique, up-close, step-by-step look at the entire development cycle for NLP system design, from your initial idea, to spec, through annotation and corpus development, to training and testing your algorithm. Don't miss this informative webcast. About James Pustejovsky James Pustejovsky holds the TJX/Felberg Chair in Computer Science at Brandeis University, where he directs the Lab for Linguistics and Computation, and chairs both the Program in Language and Linguistics and the Computational Linguistics MA Program. He has conducted research in computational linguistics, AI, lexical semantics, temporal reasoning, and corpus linguistics and language annotation. He is currently head of a working group within ISO/TC37/SC4 to develop a Semantic Annotation Framework, and is the author of the recently approved ISO specification for time annotation (SemAF-Time, ISO-TimeML) and the draft specification for space annotation (SemAF-Space, ISO-Space). Pustejovsky was PI of a large NSF-funded effort, "Towards a Comprehensive Linguistic Annotation of Language," that involved merging several diverse linguistic annotations (PropBank, NomBank, the Discourse Treebank, TimeBank, and Opinion Corpus) into a unified representation. Currently, he is Co-PI of a major project funded by the NSF to address interoperability for NLP data and tools. He has taught computational linguistics to both graduates and undergraduates for 20 years, and corpus linguistics for eight years. http://twitter.com/jamespusto About Amber Stubbs Amber Stubbs recently completed her Ph.D. in Computer Science at Brandeis University, and is currently a Postdoctoral Associate at SUNY Albany. Her dissertation focused on creating an annotation methodology to aid in extracting high-level information from natural language files, particularly biomedical texts. Her website can be found at http://pages.cs.brandeis.edu/~astubbs/ Produced by: Yasmina Greco
https://wn.com/O'Reilly_Webcast_How_To_Develop_Language_Annotations_For_Machine_Learning_Algorithms
Algorithms Lecture 1 Part 1: Mathematical Preliminaries

Algorithms Lecture 1 Part 1: Mathematical Preliminaries

  • Order:
  • Duration: 9:59
  • Updated: 15 Mar 2013
  • views: 1171
videos
This lecture is delivered by Professor Michael Rieck, Fundamental mathematical concepts including set theory are discussed. Increasing and decreasing functions are explained. Besides learning algorithms to solve a wide range of practical problems, we will also want to develop a strong sense of how efficient these algorithms are.
https://wn.com/Algorithms_Lecture_1_Part_1_Mathematical_Preliminaries
9.1: Genetic Algorithm: Introduction - The Nature of Code

9.1: Genetic Algorithm: Introduction - The Nature of Code

  • Order:
  • Duration: 12:17
  • Updated: 29 Jul 2016
  • views: 12118
videos
Welcome to part 1 of a new series of videos focused on Evolutionary Computing, and more specifically, Genetic Algorithms. In this tutorial, I introduce the concept of a genetic algorithm, how it can be used to approach "search" problems and how it relates to brute force algorithms. Support this channel on Patreon: https://www.patreon.com/codingrainbow Send me your questions and coding challenges!: https://github.com/CodingRainbow/Rainbow-Topics Contact: https://twitter.com/shiffman Links discussed in this video: The Nature of Code: http://natureofcode.com/ BoxCar2D: http://boxcar2d.com/ Source Code for the Video Lessons: https://github.com/CodingRainbow/Rainbow-Code p5.js: https://p5js.org/ Processing: https://processing.org For More Genetic Algorithm videos: https://www.youtube.com/playlist?list=PLRqwX-V7Uu6bJM3VgzjNV5YxVxUwzALHV For More Nature of Code videos: https://www.youtube.com/playlist?list=PLRqwX-V7Uu6aFlwukCmDf0-1-uSR7mklK Help us caption & translate this video! http://amara.org/v/Sld6/
https://wn.com/9.1_Genetic_Algorithm_Introduction_The_Nature_Of_Code
Java Algorithms

Java Algorithms

  • Order:
  • Duration: 15:09
  • Updated: 25 Feb 2013
  • views: 258240
videos
Get the Code Here: http://goo.gl/2AJYt Support me on Patreon : https://www.patreon.com/derekbanas?ty=h Welcome to my Java Algorithms tutorial. In this series I will cover everything there is to know about Java algorithms and data structures. An algorithm is just the steps you take to manipulate data. A data structure is the way data is arranged in memory. There are 3 main data structure operations I will focus on first being inserting, deleting and searching for data. Like all of my tutorials, everything is simple at first and then I cover more complex topics.
https://wn.com/Java_Algorithms
Machine Learning Algorithms – Part 1

Machine Learning Algorithms – Part 1

  • Order:
  • Duration: 15:53
  • Updated: 01 Jul 2016
  • views: 8651
videos
To learn more about creating a modern IT environment, click: http://aka.ms/GuideModernIT. For any novice in machine learning – the biggest challenge is to determine the algorithm to use to train the model. This tutorial attempts to identify the various use-cases for training models and which algorithm one can use in a particular use-case.
https://wn.com/Machine_Learning_Algorithms_–_Part_1
Algorithms I  Part 001  Dynamic Connectivity

Algorithms I Part 001 Dynamic Connectivity

  • Order:
  • Duration: 10:23
  • Updated: 09 Jul 2014
  • views: 1711
videos
This course is useful for those developers who wants to develop optimized. For those who want to for for high tech companies. This course is offered on Coursera ; Princeton university, by Kevin Wayne, Robert Sedgewick Please register there for further information https://class.coursera.org/algs4partI-005
https://wn.com/Algorithms_I_Part_001_Dynamic_Connectivity
Developing Training Algorithms for Convolutional Neural Networks

Developing Training Algorithms for Convolutional Neural Networks

  • Order:
  • Duration: 10:07
  • Updated: 05 Nov 2013
  • views: 4322
videos
Developing Training Algorithms for Convolutional Neural Networks by Susan Lamoitier ECTE953 Advanced Project November 2013 School of Electrical, Computer & Telecommunications Engineering (SECTE) at the University of Wollongong (UOW) ABSTRACT During the last decades several visual recognition problems have been investigated. Image processing permits for example face detection, face recognition, facial expression analysis, car detection, optical character recognition, or hand written digit recognition. Neural networks (NN) have been found almost unavoidable in pattern recognition. In fact recognition systems are more efficient when they focus on learning techniques. LeCun proposed convolutional neural networks (CNN) which are NN with three key architectural ideas: local receptive fields, weight sharing, and sub-sampling in the spatial domain. Networks are designed for the recognition of two-dimensional visual patterns. CNN have many strengths. Firstly, feature extraction and classification are integrated into one structure and are fully adaptive. Secondly, the network extracts two-dimensional image features at increasing dyadic scales. Thirdly, it is relatively invariant to geometric, local distortions in the image. This project aims to develop fast training algorithms for CNN. It is based on Phung and Bouzerdoum's CNN library and develops two algorithms: the improved resilient back-propagation and the algorithm for pattern recognition. They are compared on a face versus non-face classification with the two existing algorithms (resilient back-propagation and gradient descent). The programming is done in MATLAB.
https://wn.com/Developing_Training_Algorithms_For_Convolutional_Neural_Networks
1. Algorithmic Thinking, Peak Finding

1. Algorithmic Thinking, Peak Finding

  • Order:
  • Duration: 53:22
  • Updated: 14 Jan 2013
  • views: 725140
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
The Big Bang Theory - The Friendship Algorithm

The Big Bang Theory - The Friendship Algorithm

  • Order:
  • Duration: 2:28
  • Updated: 20 Jan 2009
  • views: 2984568
videos
Season 2, episode 13 Sheldon displays his friendship algorithm as a flow chart, and tests it. (this belongs to CBS, not me, I'm just enlightening you with Sheldon's awesomeness)
https://wn.com/The_Big_Bang_Theory_The_Friendship_Algorithm
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