Search results “Memory based reasoning data mining techniques”
Case-Based Reasoning - AI 101
You can support the making of these videos through the AI and Games Patreon page: http://www.patreon.com/ai_and_games Like us on Facebook: http://www.facebook.com/AIandGames Follows us on Twitter: http://www.twitter.com/AIandGames -- In this AI 101 video we take a moment to explore the rationale, requirements and application of the Case-Based Reasoning technique. How do we use it? Why do we use it? And how does it relate to more traditional aspects of human cognitive behaviour? -- Music in this Video: 'Happy Go Lucky ChipTune' Written and Performed by 'Teknoaxe': http://www.youtube.com/user/teknoaxe http://www.teknoaxe.com http://www.patreon.com/teknoaxe
Views: 11368 AI and Games
What is CASE-BASED REASONING? What does CASE-BASED REASONING mean? CASE-BASED REASONING meaning - CASE-BASED REASONING definition - CASE-BASED REASONING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Case-based reasoning (CBR), broadly construed, is the process of solving new problems based on the solutions of similar past problems. An auto mechanic who fixes an engine by recalling another car that exhibited similar symptoms is using case-based reasoning. A lawyer who advocates a particular outcome in a trial based on legal precedents or a judge who creates case law is using case-based reasoning. So, too, an engineer copying working elements of nature (practicing biomimicry), is treating nature as a database of solutions to problems. Case-based reasoning is a prominent kind of analogy making. It has been argued that case-based reasoning is not only a powerful method for computer reasoning, but also a pervasive behavior in everyday human problem solving; or, more radically, that all reasoning is based on past cases personally experienced. This view is related to prototype theory, which is most deeply explored in cognitive science. Case-based reasoning has been formalized for purposes of computer reasoning as a four-step process: 1. Retrieve: Given a target problem, retrieve from memory cases relevant to solving it. A case consists of a problem, its solution, and, typically, annotations about how the solution was derived. For example, suppose Fred wants to prepare blueberry pancakes. Being a novice cook, the most relevant experience he can recall is one in which he successfully made plain pancakes. The procedure he followed for making the plain pancakes, together with justifications for decisions made along the way, constitutes Fred's retrieved case. 2. Reuse: Map the solution from the previous case to the target problem. This may involve adapting the solution as needed to fit the new situation. In the pancake example, Fred must adapt his retrieved solution to include the addition of blueberries. 3. Revise: Having mapped the previous solution to the target situation, test the new solution in the real world (or a simulation) and, if necessary, revise. Suppose Fred adapted his pancake solution by adding blueberries to the batter. After mixing, he discovers that the batter has turned blue – an undesired effect. This suggests the following revision: delay the addition of blueberries until after the batter has been ladled into the pan. 4. Retain: After the solution has been successfully adapted to the target problem, store the resulting experience as a new case in memory. Fred, accordingly, records his new-found procedure for making blueberry pancakes, thereby enriching his set of stored experiences, and better preparing him for future pancake-making demands. At first glance, CBR may seem similar to the rule induction algorithms of machine learning. Like a rule-induction algorithm, CBR starts with a set of cases or training examples; it forms generalizations of these examples, albeit implicit ones, by identifying commonalities between a retrieved case and the target problem. If for instance a procedure for plain pancakes is mapped to blueberry pancakes, a decision is made to use the same basic batter and frying method, thus implicitly generalizing the set of situations under which the batter and frying method can be used. The key difference, however, between the implicit generalization in CBR and the generalization in rule induction lies in when the generalization is made. A rule-induction algorithm draws its generalizations from a set of training examples before the target problem is even known; that is, it performs eager generalization.
Views: 5029 The Audiopedia
Data Mining: Carvana Lemon Car Prediction using SAS Enterprise Miner
Business Case: To predict if the car purchased at the Auction is a bad buy, using car related and purchase related data. Methods: Logistic regression, Decision Trees, Memory Based Reasoning, Neural Networks using SAS Enterprise Miner.
Views: 1732 Sachin's Tech Corner
Reasoning Tricks || Based on Letter Series ||SSC CGL,BANK PO, IBPS, Railway,CPO, UPSC ||
In This video you learn the tricks to solve letter series questions easily. Like our facebook page https://www.facebook.com/pandorasbox013/
Views: 4655641 Pandora's Box
Rule Based Systems
Artificial Intelligence by Prof. Deepak Khemani,Department of Computer Science and Engineering,IIT Madras.For more details on NPTEL visit http://nptel.ac.in
Views: 10315 nptelhrd
Views: 175 PREP INDIA
Contrast with Case-Based Reasoning - Georgia Tech - KBAI: Part 4
Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud409/l-1934948585/m-1945549504 Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud409 Georgia Tech online Master's program: https://www.udacity.com/georgia-tech
Views: 445 Udacity
Instance Based Learning Before - Georgia Tech - Machine Learning
Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud262/l-666010252/m-672718818 Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud262 Georgia Tech online Master's program: https://www.udacity.com/georgia-tech
Views: 5881 Udacity
Data Verification Point
Data Verification Point Watch More Videos at: https://www.tutorialspoint.com/videotutorials/index.htm Lecture By: Mr. Pavan Lalwani, Tutorials Point India Private Limited.
Shailesh Kumar – Reasoning: The Next Frontier in Data Science
The “Prediction Paradigm” in data science has come a long way. Today, we can build reasonably accurate models for complex prediction problems such as detecting objects in Images, answering Jeopardy questions, translating documents from one language to another, or recognising people from face images. In this talk we will explore the next paradigm in data science - the “Reasoning Paradigm” that tries to optimize a “sequence of actions” leading from a “start state” to an “end state”. Prescribing a treatment plan for a set of symptoms, learning strategies for playing Chess or Go, solving multi-step problems in mathematics, maximizing life-time-value of a customer, having a goal driven conversation with a chat-bot, or connecting the dots on a knowledge graph are different flavours of multi-step reasoning problems that cannot be solved by the single-step prediction paradigm. This talk will focus on two specific reasoning paradigms - Mathematical Reasoning and Reasoning over Knowledge Graphs. We will explore the building blocks for an intelligent reasoning engine that “explores” the space of possible solutions, “discovers” one or more solutions, characterizes the “quality” of each solution, “generalizes” to “similar” reasoning problems, and most importantly “learns” how to generate “better” solutions “faster” with practice - the holy grail of AI. Shailesh Kumar is Chief Scientist and Co-Founder at ThirdLeap. He has 14 years over fifteen years of experience in applying and innovating machine learning, statistical pattern recognition, and data mining algorithms to hard prediction problems in a wide variety of domains including: remote sensing, text mining, bio-informatics, computer vision and image understanding, transaction data mining, retail analytics, neurological data, risk analytics in financial domain,and web analytics. This talk was recorded at The Fifth Elephant 2016, India's premier data analytics conference.
Views: 1212 HasGeek TV
Lecture - 39 Natural Language Processing - I
Lecture Series on Artificial Intelligence by Prof.Sudeshna Sarkar and Prof.Anupam Basu, Department of Computer Science and Engineering,I.I.T, Kharagpur . For more details on NPTEL visit http://nptel.iitm.ac.in.
Views: 89759 nptelhrd
Mining Input Grammars for Security Testing
Knowing which part of a program processes which parts of an input can reveal the structure of the input as well as the structure of the program. In a URL "http://www.example.com/path/", for instance, the protocol “http", the host “www.example.com", and the path “path" would be handled by different functions and stored in different variables. Given a set of sample inputs, we use _dynamic tainting_ to trace the data flow of each input character, and aggregate those input fragments that would be handled by the same function into lexical and syntactical entities. The result is a _context-free grammar_ that accurately reflects valid input structure; as it draws on function and variable names, it can be as readable as textbook examples: URL ::= PROTOCOL "://" HOST "/" PATH PROTOCOL ::= “http” | “https” | … HOST ::= /[a-zA-Z0-9.]+/ ... We expect inferred grammars to considerably ease the understanding of file and input formats. Their most important use, however, will be in automatic fuzz testing, where grammars can easily be turned into producers that help to quickly cover program features. Our grammar-based LANGFUZZ fuzzer is in daily use at Mozilla and has uncovered more than 4,000 defects so far; mining grammars automatically will bring such techniques to a wide range of programs. For details on our work on grammar mining, see https://www.st.cs.uni-saarland.de/models/autogram/  See more on this video at https://www.microsoft.com/en-us/research/video/mining-input-grammars-security-testing/
Views: 510 Microsoft Research
Role of Machine  Learning  in Human  Life
Why Ai or machine intelligence to Us?Introduction [2] Overview of Artificial intelligence- Problems of AI, AI technique, Tic - Tac - Toe problem. Intelligent Agents [2] Agents & environment, nature of environment, structure of agents, goal based agents, utility based agents, learning agents. Problem Solving [2] Problems, Problem Space & search: Defining the problem as state space search, production system, problem characteristics, issues in the design of search programs. Search techniques [5] Solving problems by searching :problem solving agents, searching for solutions; uniform search strategies: breadth first search, depth first search, depth limited search, bidirectional search, comparing uniform search strategies. Heuristic search strategies [5] Greedy best-first search, A* search, memory bounded heuristic search: local search algorithms & optimization problems: Hill climbing search, simulated annealing search, local beam search, genetic algorithms; constraint satisfaction problems, local search for constraint satisfaction problems. Adversarial search [3] Games, optimal decisions & strategies in games, the minimax search procedure, alpha-beta pruning, additional refinements, iterative deepening. Knowledge & reasoning [3] Knowledge representation issues, representation & mapping, approaches to knowledge representation, issues in knowledge representation. Using predicate logic [2] C
Views: 275 Last Night Study
Mining the Crowd - Tova Milo - Technion Computer Engineering lecture
Mining the Crowd Lecture by Prof. Tova Milo, Head of the Computer Science Department, Tel Aviv University Harnessing a crowd of Web users for data collection has recently become a wide-spread phenomenon. A key challenge is that the human knowledge forms an open world and it is thus difficult to know what kind of information we should be looking for. Classic databases have addressed this problem by data mining techniques that identify interesting data patterns. These techniques, however, are not suitable for the crowd. This is mainly due to properties of the human memory, such as the tendency to remember simple trends and summaries rather than exact details. Following these observations, we develop here a novel model for crowd mining. We will consider in the talk the logical, algorithmic, and methodological foundations needed for such a mining process, as well as the applications that can benefit from the knowledge mined from crowd.
Views: 837 Technion
Identifying Suspicious URLs: An Application of Large-Scale Online Learning
Google Tech Talk May 5, 2010 ABSTRACT Presented by Justin Ma. We explore online learning approaches for detecting malicious Web sites (those involved in criminal scams) using lexical and host-based features of the associated URLs. We show that this application is particularly appropriate for online algorithms as the size of the training data is larger than can be efficiently processed in batch and because the distribution of features that typify malicious URLs is changing continuously. Using a real-time system we developed for gathering URL features, combined with a real-time source of labeled URLs from a large Web mail provider, we demonstrate that recently-developed online algorithms can be as accurate as batch techniques, achieving daily classification accuracies up to 99% over a balanced data set. Slides: http://cseweb.ucsd.edu/~jtma/google_talk/jtma-google10.pdf Justin Ma is a PhD candidate at UC San Diego advised by Stefan Savage, Geoff Voelker and Lawrence Saul. His research interests are in systems and networking with an emphasis on network security, and his current focus is the application of machine learning to problems in security. He will be joining UC Berkeley as a postdoc after graduation. [Home page: http://www.cs.ucsd.edu/~jtma/ ]
Views: 10410 GoogleTechTalks
15. Learning: Near Misses, Felicity Conditions
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston To determine whether three blocks form an arch, we use a model which evolves through examples and near misses; this is an example of one-shot learning. We also discuss other aspects of how students learn, and how to package your ideas better. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 41795 MIT OpenCourseWare
NIPS 2011 Learning Semantics Workshop: From Machine Learning to Machine Reasoning
Learning Semantics Workshop at NIPS 2011 Invited Talk: From Machine Learning to Machine Reasoning by Léon Bottou Léon Bottou is a research scientist with broad interests in practical and theoretical machine learning. His work on large scale learning and stochastic gradient algorithms has received attention in the recent years. Léon is also known for the DjVu document compression system. Abstract: A plausible definition of "reasoning" could be "algebraically manipulating previously acquired knowledge in order to answer a new question". This definition covers first-order logical inference or probabilistic inference. It also includes much simpler manipulations commonly used to build large learning systems. For instance, we can build an optical character recognition system by first training a character segmenter, an isolated character recognizer, and a language model, using appropriate labeled training sets. Adequately concatenating these modules and fine tuning the resulting system can be viewed as an algebraic operation in a space of models. The resulting model answers a new question, that is, converting the image of a text page into a computer readable text. This observation suggests a conceptual continuity between algebraically rich inference systems, such as logical or probabilistic inference, and simple manipulations, such as the mere concatenation of trainable learning systems. Therefore, instead of trying to bridge the gap between machine learning systems and sophisticated "all-purpose" inference mechanisms, we can instead algebraically enrich the set of manipulations applicable to training systems, and build reasoning capabilities from the ground up.
Views: 859 GoogleTechTalks
Tobias Kuhn, Nakul Selvaraj: Real-Time Monitoring of Distributed Systems
Instrumentation has seen explosive adoption on the cloud in recent years. With the rise of micro-services we are now in an era where we measure the most trivial events in our systems. At Trademob, a mobile DSP with upwards of 125k requests per second across +700 instances, we generate and collect millions of  time-series data points. Gaining key insights from this data has proven to be a huge challenge.Outlier and Anomaly detection are two techniques that help us comprehend the behavior of our systems and allow us to take actionable decisions with little or no human intervention. Outlier Detection is the identification of misbehavior across multiple subsystems and/or aggregation layers on a machine level, whereas Anomaly Detection lets us identify issues by detecting deviations against normal behavior on a temporal level. The analysis of these deviations is simplified through the use of a time and memory efficient data structure called a t-digest. With t-digests we are able to store error distributions with high accuracy, especially for extreme quantile values.At Trademob, we developed a Python-based real-time monitoring system to conquer those challenges in order to reduce false positive alerts and increase overall business performance. By correlating a multitude of metrics we can determine system interdependencies, preemptively detect issues and also gain key insights to causality. This session will provide insights into both the system’s architecture and the algorithms used to detect unwanted behaviors. Tobias Kuhn, Nakul Selvaraj
Views: 691 PyData
On the Anonymization of Sparse High-Dimensional Data
Title: On the Anonymization of Sparse High-Dimensional Data Domain: Data Mining Description: 1, Privacy preservation is the most focussed issue in information publication, on the grounds that the sensitive data shouldn't be disclosed. For this regard, several privacy preservation data mining algorithms are proposed. 2, Generalisation, Bucketisation and Anatomisation techniques are used as a part of this regard. They ensure the privacy of the user,either by modifying quasi identifier values or by including noise. 3, These techniques are well suited for low dimensional data and they expel the most valuable information from the dataset.In this work,we concentrate on protection against identity disclosure in the publication of sparse high dimensional data. 4, The sparse dataset which is scanty has less information distributed in the entire dataset.So,in the first phase we transform the dataset into a band matrix framework by coordinating Genetic algorithm with Cuckoo search algorithm.This makes the nearest rows associated and makes the non zero components near to the diagonal and lessens the search space and also memory. 5, In the other phase a novel anatomisation technique based on disassociation is introduced to safeguard privacy.This technique isolates the quasi identifier values with sensitive attributes and publishes quasi identifiers straightforwardly.Then density based clustering is employed to anonymise the underlying data,ands protects against identity disclosure and increases data utility The adversary cannot relate the sensitive value with high probability.Experimental results demonstrate that this technique decreases information loss, reconstruction error and increases data utility. For more details contact: E-Mail: [email protected] Buy Whole Project Kit for Rs 5000%. Project Kit: • 1 Review PPT • 2nd Review PPT • Full Coding with described algorithm • Video File • Full Document Note: *For bull purchase of projects and for outsourcing in various domains such as Java, .Net, .PHP, NS2, Matlab, Android, Embedded, Bio-Medical, Electrical, Robotic etc. contact us. *Contact for Real Time Projects, Web Development and Web Hosting services. *Comment and share on this video and win exciting developed projects for free of cost. Search Terms: 1. 2017 ieee projects 2. latest ieee projects in java 3. latest ieee projects in data mining 4. 2017 – 2018 data mining projects 5. 2017 – 2018 best project center in Chennai 6. best guided ieee project center in Chennai 7. 2017 – 2018 ieee titles 8. 2017 – 2018 base paper 9. 2017 – 2018 java projects in Chennai, Coimbatore, Bangalore, and Mysore 10. time table generation projects 11. instruction detection projects in data mining, network security 12. 2017 – 2018 data mining weka projects 13. 2017 – 2018 b.e projects 14. 2017 – 2018 m.e projects 15. 2017 – 2018 final year projects 16. affordable final year projects 17. latest final year projects 18. best project center in Chennai, Coimbatore, Bangalore, and Mysore 19. 2017 Best ieee project titles 20. best projects in java domain 21. free ieee project in Chennai, Coimbatore, Bangalore, and Mysore 22. 2017 – 2018 ieee base paper free download 23. 2017 – 2018 ieee titles free download 24. best ieee projects in affordable cost 25. ieee projects free download 26. 2017 data mining projects 27. 2017 ieee projects on data mining 28. 2017 final year data mining projects 29. 2017 data mining projects for b.e 30. 2017 data mining projects for m.e 31. 2017 latest data mining projects 32. latest data mining projects 33. latest data mining projects in java 34. data mining projects in weka tool 35. data mining in intrusion detection system 36. intrusion detection system using data mining 37. intrusion detection system using data mining ppt 38. intrusion detection system using data mining technique 39. data mining approaches for intrusion detection 40. data mining in ranking system using weka tool 41. data mining projects using weka 42. data mining in bioinformatics using weka 43. data mining using weka tool 44. data mining tool weka tutorial 45. data mining abstract 46. data mining base paper 47. data mining research papers 2017 - 2018 48. 2017 - 2018 data mining research papers 49. 2017 data mining research papers 50. data mining IEEE Projects 52. data mining and text mining ieee projects 53. 2017 text mining ieee projects 54. text mining ieee projects 55. ieee projects in web mining 56. 2017 web mining projects 57. 2017 web mining ieee projects 58. 2017 data mining projects with source code 59. 2017 data mining projects for final year students 60. 2017 data mining projects in java 61. 2017 data mining projects for students
Data Analysis & Interpretation OF  PO  Exam
Views: 1026 Anil Aggarwal
Universal Schema for Knowledge Representation from Text and Structured Data
Entities and the relations among them are central for representing our knowledge about the world. My work concentrates on discovering relations from information sources available to us, including unstructured text corpora and structured data. Previous work for relation extraction can be classified into two categories in terms of how they represent relations. The first is based on supervised learning, representing relations using pre-defined types from knowledge bases. This approach relies on human efforts to define relation types. The second generalizes the Open IE style relation extraction, representing relations as clusters of textual patterns. This assumes semantic equivalence among patterns falling into the same cluster, failing to represent the diversity and ambiguity of the patterns. I will present a new approach, Universal Schema – the union of all relations seen among surface patterns and available structured knowledge bases. This representation preserves the diversity and ambiguity of textual patterns and allows us to generalize among them. In this talk, I will explain how to perform relation extraction in universal schema. We formalize the task as matrix completion and employ matrix factorization to learn implications among relations. Experiments demonstrate that using universal schema for relation extraction provides new state-of-the-art accuracy. We also extend universal schema to entity type extraction.
Views: 846 Microsoft Research
Rete Algorithm
Artificial Intelligence by Prof. Deepak Khemani,Department of Computer Science and Engineering,IIT Madras.For more details on NPTEL visit http://nptel.ac.in
Views: 14781 nptelhrd
Lecture - 17 Rule Based Systems II
Lecture Series on Artificial Intelligence by Prof.Sudeshna Sarkar and Prof.Anupam Basu, Department of Computer Science & Engineering,I.I.T, Kharagpur . For more details on NPTEL visit http://nptel.iitm.ac.in.
Views: 12873 nptelhrd
"A New Understanding of Prediction Markets Via No-Regret Learning" (CRCS Lunch Seminar)
CRCS Lunch Seminar (Wednesday, April 21, 2010) Speakers: Jenn Wortman Vaughan Title: Title: A New Understanding of Prediction Markets Via No-Regret Learning Abstract: Suppose that you are interested in estimating the probability that Google will reinstate its Chinese search engine within the next two years. You might choose to spend hours digging through news articles, reading commentaries, and weighing various opinions against each other, eventually coming up with a reasonably well-informed guess. But you might be able to save yourself a lot of hassle (and potentially obtain a better estimate) by appealing to the wisdom of crowds. A prediction market is a financial market designed to aggregate information. A typical binary prediction market allows bets along a single dimension, for example, for or against Google reinstating its Chinese search engine by the end of 2011. In this case, bettors might trade securities that pay $1 if and only if Google moves back to China by the specified date. If the current market price of a share of this security is $p, then a rational, risk-neutral bettor should be willing to buy shares if he believes the true probability is greater than p. Conversely, he should be willing to sell shares if he believes the true probability is lower. In this sense, the current price per share provides an estimate of the population's collective belief about how likely it is that Google will reinstate its search engine. These estimates have proved quite accurate in practice in a wide variety of domains. Equilibrium theory offers some insight into why prediction markets should converge to accurate prices, but is plagued by strong assumptions and no-trade theorems. Furthermore, this theory says nothing of why particular prediction market mechanisms, such as Hanson's increasingly popular Logarithmic Market Scoring Rule, might produce more accurate estimates than others in practice. In this talk, I will describe some recent work aimed at understanding the learning power of particular market mechanisms by examining the deep mathematical connections that exist between prediction market mechanisms and common algorithms for "no-regret" learning. I will then describe how this synergy between prediction markets and machine learning can be leveraged to run an efficient market when the space of possible outcomes is complex. This talk is based primarily on joint work with Yiling Chen. It additionally includes ideas from earlier work with Lance Fortnow, Nicolas Lambert, and David Pennock. Bio: Jenn Wortman Vaughan is a Computing Innovation Fellow at Harvard University. She completed her Ph.D. at the University of Pennsylvania in 2009. Her research interests are in machine learning, computational aspects of economics, social network theory, and algorithms, all of which she studies using techniques from theoretical computer science. Her recent research has won several best student paper awards, as well as Penn's 2009 Rubinoff dissertation award for innovative applications of computer technology. In her spare time, she is involved in a variety of efforts to provide support for women in computer science; most notably, she co-founded the Annual Workshop for Women in Machine Learning, which will be held for the fifth time this year. In the fall, Jenn will join the Computer Science Department at UCLA as an assistant professor. http://people.seas.harvard.edu/~jenn/
Views: 754 Harvard's CRCS
Knowledge Management - Write short note on “KM Life Cycle”.
Need Answer Sheet of this Question paper, contact [email protected] www.mbacasestudyanswers.com ARAVIND – 09901366442 – 09902787224 Knowledge Management Multiple Choices: Q1. UCC stands for: a. Universal Commercial Code b. Uniform Commercial Code c. Unique Commercial Code d. United Commercial Code Q2. E-business connects critical business systems and constituencies directly via: a. Internet b. Extranet c. Intranet d. All of the above Q3. Unusable rule are also called as: a. User rule b. Conflicting rule c. Subsumed rule d. None of the above Q4. Fact in knowledge codification refers to: a. Value of an object or a slot b. Codification scheme c. Both (a) & (b) d. Filling of slots Q5. An individual with skills & solutions that work some of the time but not all of the time is: a. Scribe b. Validity c. Novice d. None of the above Q6. CBR is: a. Case based reasoning b. Case based reliability c. Case based repository d. None of the above Q7. An unskilled employee trying to learn or gain some understanding of the captures knowledge is a: a. Pupil user b. Tutor user c. People user d. None of the above Q8. A rule of thumb based on years of experience is called: a. Procedural rule b. Tacit knowledge c. Heuristic d. None of the above Q9. Episodic knowledge is: a. The knowledge based on the fundamentals structure functions & behavior of objects b. The knowledge based on experimental information or episodes c. The knowledge based on the unrelated facts d. None of the above Q10. A directory that points to people, documents and repositories is: a. Knowledge map b. Knowledge codification c. Rapid prototyping d. None of the above Part Two: Q1. Write short note on “KM Life Cycle”. Q2. Write short note on “The Knowing Doing Gap”. Q3. What is Nominal Group Techniques (NGT)? Q4. What do you mean by Delphi Method? Q5. Which factors contributed to motivate the troops to go ahead for such a difficult task as recovering a damaged vehicle from such a difficult and treacherous terrain and getting it repaired in such a short time? Q6. Which incidents indicate the importance of good interpersonal relationships with juniors, peers and superiors and what is the importance of good interpersonal relationships? Q7. What other data-driven promotions could Carrier come up with using other data mining techniques? Q8. What manufacturing-driven applications can Carrier implement using data mining? Q9. Explain the concept of Tacit Knowledge. List the different techniques of capturing Tacit Knowledge. Q10. Explain Global Knowledge Leadership. What are the driving forces behind global expansion of knowledge management? Need Answer Sheet of this Question paper, contact [email protected] www.mbacasestudyanswers.com ARAVIND – 09901366442 – 09902787224
Views: 160 Mba Casestudyhelp
Views: 5457 Data Mining - IITKGP
Machine Rebellion
The possibility of Artificial Intelligence turning on humanity has been a concern for as long as we've had computers. Today we will look at some of those fears and see which ones might be valid and which might not be cause for alarm. Use my link http://www.audible.com/isaac and get a free audio book with a 30 day trial! Visit our Website: http://www.isaacarthur.net Join the Facebook Group: https://www.facebook.com/groups/1583992725237264/ Support the Channel on Patreon: https://www.patreon.com/IsaacArthur Visit the sub-reddit: https://www.reddit.com/r/IsaacArthur/ Listen or Download the audio of this episode from Soundcloud: https://soundcloud.com/isaac-arthur-148927746/machine-rebellion Cover Art by Jakub Grygier: https://www.artstation.com/artist/jakub_grygier Graphics Team: Edward Nardella Jarred Eagley Justin Dixon Jeremy Jozwik Katie Byrne Kris Holland Misho Yordanov Murat Mamkegh Pierre Demet Sergio Botero Stefan Blandin Script Editing: Andy Popescu Connor Hogan Edward Nardella Eustratius Graham Gregory Leal Jefferson Eagley Keith Blockus Luca de Rosa Mark Warburton Michael Gusevsky Mitch Armstrong MolbOrg Naomi Kern Philip Baldock Sigmund Kopperud Tiffany Penner Music AJ Prasad, "Cold Shadows" Lee Rosevere, "It's such a beautiful day" Kai Engel, "Morbid Imagination" Sergey Cheremisinov, "Jump in Infinity" Markus Junnikkala, "A Memory of Earth" Kai Engel, "Crying Earth" Sergey Cheremisinov, "Labyrinth" Brandon Liew, "Into the Storm" Lombus, "Doppler Shores"
Views: 221239 Isaac Arthur
Data Interpretation Basics by Rohit Agarwal
Data Interpretation, a tool that is required for day to day decisions for us, is also a key area which is tested for selecting fresh talent in the various organizations, specifically Banks. State Bank of India's Probationary Officers (SBI PO) Test largely focuses on this area. In fact, The quant section of SBI PO is actually labelled as Data Interpretation & Analysis truly reflecting the importance of this skill in their selection process. With the SBI PO exam round the corner, many of the aspirants have requested for covering this topic in our webcast. Hence, this is being taken up on a top priority basis. In this session by Rohit Agarwal, you will learn those concepts of Percentages, Ratio and Proportions and Averages which form the basics of Data Interpretation and will be used extensively for solving various exam questions. The session will also focus on Approaches to solve the questions in this section quickly and correctly.
Views: Rohit Agarwal
Top 30 C++ -1 cse technical interview questions and answers tutorial for fresher experienced
Top 30 C++ -1 cse technical interview questions and answers tutorial for fresher experienced c++ interview questions and answers c++ interview questions and answers in hindi c++ programming interview questions c++ programming interview questions and answers c++ interview questions and answers for experienced c++ interview questions c and c++ interview questions interview questions for c++ c c++ interview questions interview questions on c++ vc++ interview questions difference between c and c++ difference between c and c++ and java difference between c and c++ in tamil difference between c and c++ in telugu difference between c and c++ in hindi the difference between c and c++ what is difference between c and c++ basic oops concepts basic oops concepts with examples basic oops concepts in java basic oops concepts in c# basic oops concepts in hindi basic oops concept c++ oops basic concept basic concept of oops what is class and object in java what is class in c++ what is classification what is class and object in java by durga what is classification in data mining what is classical music what is class in java by durga what is classpath in java what is class in java in hindi what is class in oops what is class what is class in java what is class and object what is class diagram what is class 10 sd card what is class and object in c++ what is class a amplifier what is class and object in java in hindi what is class a surfacing what is class armor in destiny what is class consciousness what is class conflict what is abstract class in c# what is class in c what is class diagram in hindi what is class d amplifier what is class dojo what is class in html what is class in net what is class in hindi what is class in java telugu what is class in php what is class in java in tamil what is abstract class in java what is wrapper class in java what is singleton class in java what is scanner class in java what is abstract class in java in hindi what is static class in java what is anonymous class in java what is class loader what is class link what is class in python what is class sociology what is encapsulation in java what is encapsulation in c# what is encapsulation in oops what is encapsulation in hindi what is encapsulation in c++ in hindi what is encapsulation in networking what is encapsulation in java in hindi what is data encapsulation what is encapsulation what is encapsulation in c++ Placement Papers,Test Pattern,Placement Paper, Interview experience,Campus Placement Process, Placement Papers with Solution,Technical, hr Interview tips, Questions and Answers,aptitude papers,company details,pdf,for cse,ece,eee,Interview Experience and Selection Process,online Aptitude Test,bpo interview questions,Interview Puzzles,Company Profile.Learn and practice Aptitude questions and answers with explanation for interview, competitive examination and entrance test,aptitude,questions, answers, interview, placement, papers, engineering, interview videos,tips,videos for freshers,questions,prank,,attitude,at google,actress,about yourself,answers,electronics, civil, mechanical, networking, 2015, 2016, 2017,2018, reasoning, program, verbal, gk, knowledge, language, explanation, solution, problem, online, test, exam,quiz hr, interview, human, resource, questions, answers, freshers, discussion, topics, companies, Technical interview questions pdf, Technical online test - all answers are posted by experts. interview body language | conversation | communication skills | cracking tips | crack | videos | tips interview videos for freshers | of great personalities | attitude is everything | skills interview questions and answers for freshers in india | about technical support interview questions and answers for freshers | cse |ece technical manager interview questions and answers, technical job interview questions and answers, best technical interview questions and answers java | for freshers in india, For more details visit: http://www.wikitechy.com/
Views: 17668 Wikitechy
Digital Reforms in India: Payment & Governance – Major Computer Terminologies
Dr. Manishika Jain explains the various digital reforms and key terminologies in computers Data Analytics Explore facts Give specific answer to specific questions Has test hypothesis framework Data visualization tools are required Requires languages like Python & R Rooted in business analytics & business intelligence models Data Mining Generate new information & unlock insights • Descriptive: Information about existing data • Predictive: Forecast based on the data Explore new trends Requires classical and advanced artificial intelligence, pattern distribution and traditional statistics Done without any preconceived hypothesis - information from the data is not used to answer specific questions of organisation. Is close to machine learning & use scientific and mathematical techniques For paper 1 postal course - https://www.examrace.com/CBSE-UGC-NET/CBSE-UGC-NET-FlexiPrep-Program/Postal-Courses/Examrace-CBSE-UGC-NET-Paper-I-Series.htm Data Analytics @0:10 Data Mining @0:12 Prepaid Instruments @2:42 Types @5:18 Bitcoin @7:37 Worldwide Payment Systems @7:41 NPCI @8:00 NFS @8:19 UPI @8:51 BHIM @10:22 IMPS @10:36 AEPS (Aadhar Enabled Payment System) @10:47 *99# @11:28 Less Case Townships @12:51 Bharat Bill Payment System #13:30 Bharat QR @14:03 RuPay @14:37 Micro ATM @14:58 Digital India @15:22 9 Pillars @15:56 E-Sign: Online Digital Signature Service @17:54 DigiLocker @19:08 E-Hospital @19:48 Digitize India Platform (DIP) @20:01 PRAGATI @20:24 National Cloud – MeghRaj @21:02 Public Wi-Fi Hotspots @21:19 National Optical Fibre Network @21:42 BharaNet @22:07 Computer Malware @22:50 Endpoint Security Solutions – by – C-DAC @23:09 Secure e-Mail @24:52 #Remittance #Merchant #Financial #Instruments #Consumer #Instrument #Descriptive #Hypothesis #Visualization #Terminologies #Manishika #Examrace Examrace is number 1 education portal for competitive and scholastic exam like UPSC, NET, SSC, Bank PO, IBPS, NEET, AIIMS, JEE and more. We provide free study material, exam & sample papers, information on deadlines, exam format etc. Our vision is to provide preparation resources to each and every student even in distant corners of the globe. Dr. Manishika Jain served as visiting professor at Gujarat University. Earlier she was serving in the Planning Department, City of Hillsboro, Hillsboro, Oregon, USA with focus on application of GIS for Downtown Development and Renewal. She completed her fellowship in Community-focused Urban Development from Colorado State University, Colorado, USA.
Views: 15282 Examrace
Moral Math of Robots: Can Life and Death Decisions Be Coded?
A self-driving car has a split second to decide whether to turn into oncoming traffic or hit a child who has lost control of her bicycle. An autonomous drone needs to decide whether to risk the lives of busload of civilians or lose a long-sought terrorist. How does a machine make an ethical decision? Can it “learn” to choose in situations that would strain human decision making? Can morality be programmed? We will tackle these questions and more as the leading AI experts, roboticists, neuroscientists, and legal experts debate the ethics and morality of thinking machines. This program is part of the Big Ideas Series, made possible with support from the John Templeton Foundation. Subscribe to our YouTube Channel for all the latest from WSF. Visit our Website: http://www.worldsciencefestival.com/ Like us on Facebook: https://www.facebook.com/worldsciencefestival Follow us on twitter: https://twitter.com/WorldSciFest Original Program Date: June 4, 2016 MODERATOR: Bill Blakemore PARTICIPANTS: Fernando Diaz, Colonel Linell Letendre, Gary Marcus, Matthias Scheutz, Wendell Wallach Can Life and Death Decisions Be Coded? 00:06 Siri... What is the meaning of life? 1:49 Participant introductions 4:01 Asimov's Three Laws of Robotics 6:22 In 1966 ELIZA was one of the first artificial intelligence systems. 10:20 What is ALPHAGO? 15:43 TAY Tweets the first AI twitter bot. 19:25 Can you test learning Systems? 26:31 Robots and automatic reasoning demonstration 30:31 How do driverless cars work? 39:32 What is the trolley problem? 49:00 What is autonomy in military terms? 56:40 Are landmines the first automated weapon? 1:10:30 Defining how artificial intelligence learns 1:16:03 Using Minecraft to teach AI about humans and their interactions 1:22:27 Should we be afraid that AI will take over the world? 1:25:08
Views: 47379 World Science Festival
Lecture 15: Coreference Resolution
Lecture 15 covers what is coreference via a working example. Also includes research highlight "Summarizing Source Code", an introduction to coreference resolution and neural coreference resolution. ------------------------------------------------------------------------------- Natural Language Processing with Deep Learning Instructors: - Chris Manning - Richard Socher Natural language processing (NLP) deals with the key artificial intelligence technology of understanding complex human language communication. This lecture series provides a thorough introduction to the cutting-edge research in deep learning applied to NLP, an approach that has recently obtained very high performance across many different NLP tasks including question answering and machine translation. It emphasizes how to implement, train, debug, visualize, and design neural network models, covering the main technologies of word vectors, feed-forward models, recurrent neural networks, recursive neural networks, convolutional neural networks, and recent models involving a memory component. For additional learning opportunities please visit: http://stanfordonline.stanford.edu/
Tapping into the Potential of Natural Language Processing in Healthcare
Table of Contents Act 1 - The Possibilities are Endless 01:53 Act 2 - NLP to the Rescue (aka The Hype) 05:14 Act 3 - A Peek Under the Hood (aka The Reality) 16:40 Act 4 - You Can Do It! 25:22 Q&A - 40:10 Gathering insight from clinical notes remains one of the areas of untapped healthcare intelligence with tremendous potential. But extracting that value is difficult. Still, a few organizations across the country are demonstrating success using advanced technology tied to intuitive processes and procedures. Leading one such organizational effort is Wendy Chapman, PhD, chair of the Department of Biomedical Informatics at the University of Utah. Dr. Chapman’s research has driven discovery in new ways to disseminate resources for modeling and understanding information described in narrative clinical reports. Her teams have demonstrated phenotyping for precision medicine, quality improvement, and decision support. Joining Dr. Chapman in a shared discussion is Mike Dow who leads the Natural Language Processing (NLP) technology team at Health Catalyst. Mike and team have several years of experience engaging with a variety of health system organizations across the country who are realizing statistical insight by incorporating text notes along with discrete data analysis. Together, Mike and Dr. Chapman will provide an NLP primer sharing principle-driven stories so you can get going with NLP whether you are just beginning or considering processes, tools or how to build support with key leadership. Learning Objectives: - Understand NLP, both its challenges, and potential to drive clinical insight using social determinants of health - Gain insight into the technology that makes NLP possible - Consider the future potential of NLP View this webin to better understand the potential of NLP through existing applications, the challenges of making NLP a real and scalable solution, and walk away with concrete actions you can take to use NLP for the good of your organization.
Views: 229 Health Catalyst
Writing a customer database system (3a/4) using Human Level Artificial Intelligence
This video shows a robot writing a customer database system using a binary tree. There are no sound in the video because I wanted to show the viewers how the robot thinks while writing a software program. Writing a database system using a binary tree is one of the hardest assignments a college professor will give to a student. If a student can write this program, then he can essentially write any computer program. The robot has to take knowledge from memory on binary trees and apply them to assignment 8. Assignment 8 is to write a database system using a binary tree. It's very easy to cut, copy, and paste codes, but the robot is using his mind to remember what a binary tree is and to write the codes based on memory and not based on codes in a book. I think the most important thing to remember is that the robot understands what a binary tree is and how it works. If the robot only had a rudimentary understanding of a binary tree and he relies on cut,copy,and paste to do his coding, there is no way he will be able to accomplish assignment 8. If the robot understands how a binary tree works, then he can change the codes according to an assignment. A binary tree has many variations and there are many different ways of writing the program. The robot has to understand all the different variations of a binary tree. If a student had trouble with his program and asked the robot to check for errors, the robot has to identify the error and tell the student what is wrong with his program. In order to do that, the robot has to have a universal understanding of a binary tree, regardless of how the program is written or what programming language was used. In the video, the robot uses a notebook to: write words, draw diagrams, recall information, show flow charts, design method structures, and outline linear steps. The notebook serves as a guide to help the robot manage a complex task. He looks at the notes repeatedly to remind himself of what tasks to do next and what tasks are already done. He is able to write this complex software by creating a general framework and to follow the steps in the framework, linearly. If the robot get's lost he can always look at his notes. This video is very long and spans 4 segments. As you can see, at the end the robot does indeed accomplish assignment 8. For more information about human level artificial intelligence visit my website: http://www.humanlevelartificialintelligence.com tags: human level artificial intelligence, ai, artificial intelligence, artificial general intelligence, true ai, strong ai, human level ai, cognitive science, ai plays video game, robot plays video game, agi, digital human brain, human intelligence, human brain, human mind, human thought, ai plays role playing games, ai play rpg, demo ai, general ai.
Views: 243 electronicdave2
Utilizing Neo4j with AI Applications
Patrick Smith and Brian Rodrigue, Excella, dive into how to use neo4j for AI applications.
Views: 617 Neo4j
CppCon 2017: Tobias Fuchs “Multidimensional Index Sets for Data Locality in HPC Applications”
The Point of Views: Multidimensional Index Sets for Data Locality in HPC Applications http://CppCon.org — Presentation Slides, PDFs, Source Code and other presenter materials are available at: https://github.com/CppCon/CppCon2017 — In High Performance Computing, data access has complex implications and requires concepts that are fundamentally different from those provided in the STL. Iterators as we know them just are not enough. The proposed range concepts for the standard library are a significant improvement but are designed for the mental model of iterating and mapping values, not hierarchical domain decomposition. Even for a seemingly trivial array there are countless ways to partition and store its elements in distributed memory, and algorithms are required to behave and scale identically for all of them. It also does not help that most applications operate on multidimensional data structures where efficient access to neighborhood regions is crucial. Among HPC developers, it is therefore widely accepted that canonical iteration space and physical memory layout must be specified as separate concepts. For this, we use views based on multidimensional index sets, inspired by the proposed range concepts. In this session, we will explain the challenges when distributing container elements for thousands of cores and how modern C++ allows to achieve portable efficiency. As an HPC afficionado, you know you want this: copy( matrix_a | local() | block({ 2,3 }), matrix_b | block({ 4,5 }) ) If this does not look familiar to you: we give a gentle introduction to High Performance Computing along the way. — Tobias Fuchs: LMU Munich, Leibniz Supercomputing Centre, Research Associate Tobi is a freelancer in embedded systems and real-time applications for over 10 years, mostly for medical devices, and went back to academia for PhD studies in High Performance Computing at LMU Munich. He is the lead developer of the DASH C++ template library, a project of the German Research Foundation (DFG), and currently focuses on models and programming abstractions for data locality. As a hobby, he lures unsuspecting students into his C++ programming course to entrap them in category theory. — Videos Filmed & Edited by Bash Films: http://www.BashFilms.com
Views: 3758 CppCon
Decimal to Binary Conversion (Hindi)
Decimal to Binary Conversion How to convert Decimal to Binary Method 1 Note: - Agar 256 se jyada bada number hai then aapko apne format me column badhana hai 256*2 = 512 then 512*2 = 1024 aapke Table ka format hoga 1024 512 256 128 64 32 16 8 4 2 1 Feel free to share this video: Number System Complete Series Playlist: https://goo.gl/VvYHL9 Check Out Our Other Playlists: https://www.youtube.com/user/GeekyShow1/playlists SUBSCRIBE to Learn Programming Language ! http://goo.gl/glkZMr Learn more about subject: http://www.geekyshows.com/ __________________________________________________________ If you found this video valuable, give it a like. If you know someone who needs to see it, share it. If you have questions ask below in comment section. Add it to a playlist if you want to watch it later. ___________________________________________________________ T A L K W I T H M E ! Business Email: [email protected] Youtube Channel: https://www.youtube.com/c/geekyshow1 Facebook: https://www.facebook.com/GeekyShow Twitter: https://twitter.com/Geekyshow1 Google Plus: https://plus.google.com/+Geekyshowsgeek Website: http://www.geekyshows.com/ ___________________________________________________________ Make sure you LIKE, SUBSCRIBE, COMMENT, and REQUEST A VIDEO! :) ___________________________________________________________ Keywords: Number System Binary Number System Decimal Number System Octal Number System Hexadecimal Number System Decimal to Binary Conversion Binary to Decimal Conversion Decimal to Hexadecimal Conversion Hexadecimal to Binary Conversion ____________________________________________________________
Views: 706779 Geeky Shows
DIMENSIONAL ANALYSIS PHYSICS TUTORIAL - Two methods of writing dimensional formula of any physical quantity with the very good example to understand simply. One is quick method while other is little lengthier. ▻For Systematic Videos Playlist of Dimensional analysis - http://goo.gl/OHfW3 ▻Concept of Dimensional Analysis - https://www.youtube.com/watch?v=40Q0XR0vcVc&t=2s ▻First Use of Dimensional Analysis https://www.youtube.com/watch?v=tyrlZgL_qUA ▻Second use of dimensional formula - https://www.youtube.com/watch?v=kJvsmUO2nlo 1. Quick Method of Writing Dimensional Formula -- For this, we should know the unit of the physical quantity. We break the units into base form, into fundamental physical quantities form and then we just write the dimensional formula as we come to know, what makes that physical quantity. For e.g.- Speed has unit m/s -- which means length/time so, dimensional formula of speed will be [L,T-1]. 2. Longer method of Writing Dimensional Formula -- for this we need to know the formula of the physical quantity. Then , we put the dimensional formula of each and every physical quantity involved in the formula, that solving results us the dimensional formula of the required physical quantity. For e.g. -- acceleration = speed/ time = dimensional formula of speed/ dimensional formula of time = [L,T-1]/[T] = [L, T-2]
Views: 343675 IMA Videos
IBPS SO 2018 | IBPS IT officer professional knowledge detailed syllabus [in Hindi] IBPS SO 2018
Please watch: "mp patwari Topicwise syllabus 2017, म प्र पटवारी परीक्षा पाठ्यक्रम By Rohit khera Sir" https://www.youtube.com/watch?v=tZf_t0Qykak --~-- it officer, ibps, ibps it officer, it officer exam preparation, sbi, bank exam preparation, sbi it officer, ssc, adda247, so, officer, specialist officer, system officer job profile, trending, bankclerk, examnews, trendingvideos, youtube, facebook, ibps it officer 2017 review, ibps it officer 2017 interview experience - lucknow, professional knowledge, the grand jbr, ibps it officer experience, आईबीपीएस यह अधिकारी 2017 साक्षात्कार अनुभव, ibps it officer 2017 interview experience, bank interview, bank it officer role, it officer role, talentsprint, it officer job profile, bank it officer job profile, how to crack ibps it officer, ibps it officer strategy, ibps it officer 2017, it officer exam notes, preparation for it officer exam, material for ibps it officer exam, crack it officer exam, crack ibps it officer exam IBPS IT officer professional knowledge detailed syllabus [in Hindi] IBPS SO 2017. for more study material visit my Website: http://www.allexamplace.com/ If you enjoyed this video and would like to receive more similar content, For technology tricks and unboxing videos and gadget review visit http://www.alldigitalplace.com/ youtube:- https://www.youtube.com/channel/UCe5UBn2_4MB59PbvnCo3oEQ Join me at: Follow him on Twitter - https://twitter.com/errohitkhera Add him on Facebook - https://www.facebook.com/rohitkhera22 Follow him Instagram - http://instagram.com/errohitkhera MY GEAR MY BIG CAMERA: http://amzn.to/2ellwAw MY DSLR MIC: http://amzn.to/2eydYau MY MIC: http://amzn.to/2elpLfi MY CAR TRIPOD: http://amzn.to/2eypceW MY OTHER PHONE TRIPODS: MY SMALL TRIPOD: http://amzn.to/2eyjcTu MY SMALL CAMERA: http://amzn.to/2eWYYWo SECOND MIC: http://amzn.to/2ex42MZ MY TABLE TRIPOD: http://amzn.to/2elq7ma CHEAPER ACTION CAMERA: - http://amzn.to/2dzsMHm SMARTPHONE TRIPOD: http://amzn.to/2eX0Jmd MY DESKTOP MIC: http://amzn.to/2eloogV MY VLOG CAMERA: http://amzn.to/2dzuo40 MY SECOND DESKTOP MIC: http://amzn.to/2eX19cu Best Gaming Laptops (Best Buy Links) 1st: Lenovo Laptops Under 40,000 INR at http://fkrt.it/dRoMyTuuuN 2nd: Asus Laptops Under 40,000 INR at http://fkrt.it/Ch5Pb!NNNN 3rd: Other Best Gaming Laptops at http://amzn.to/2ellF6T Best Earphones Under 2000 INR 1st: Altec Lansing MZW100 http://fkrt.it/ChA6R!NNNN 2nd: HEAD X DYNAMIC http://amzn.to/2e1igVz 3rd: Senheiser CX 180 Street II http://amzn.to/2eX0E28 Best Headphones Over The Head 1st: Sennheiser HD 202 II Professional http://amzn.to/2dzvzQT 2nd: Sennheiser HD-201 Lightweight http://amzn.to/2e1eV97 Best Mobile Phones Under 5000 INR ( Best Buy Links ) New 1st: Xolo Era 1X http://fkrt.it/d0jLrTuuuN 2nd: Yu Yunique http://amzn.to/2exbkAd 3rd: Swipe Elite Plus http://fkrt.it/CViMb!NNNN 4th: InFocus bingo 21 http://amzn.to/2dzw39R Old 1st: Phicomm Energy 653 -http://amzn.to/2exa6oy 2nd: XOLO Era 4G - http://amzn.to/2eyl5zO Best Mobile Phones Under 5000 INR to 8000 INR ( Best Buy Links ) New 1st: Redmi 3S http://fkrt.it/duk79TuuuN 2nd: Coolpad Mega http://amzn.to/2elq0qP 3rd: Moto E3 Power http://fkrt.it/dwlIXTuuuN 4th: Lenovo A6000 Shot http://fkrt.it/CBLg5!NNNN 5th Coolpad Note 3 Lite http://amzn.to/2eX3h3Z Old 1st: YU Yuphoria - http://fkrt.it/CBZXT!NNNN 2nd: Coolpad Note 3 Lite - http://amzn.to/2eX2JLu 3rd: Lenovo K5 Plus - http://fkrt.it/CVGL6!NNNN 4th: Meizu M2 - http://fkrt.it/TcdffTuuuN Best Mobile Phones Under 8000 INR to 10000 INR ( Best Buy Links ) 1st: Redmi Note 3 http://amzn.to/2ezYaW9 2nd: Coolpad Note 3 http://amzn.to/2eiDVKP 3rd: Redmi 3S Prime http://fkrt.it/!fsfr!NNNN 4th: Le 1S http://fkrt.it/Tc!QbTuuuN 5th Asus Zenfone Max http://amzn.to/2eBI23r 6th Moto G4 Play http://amzn.to/2eAhBiY Best Mobile Phones Under 10000 INR to 13000 INR ( Best Buy Links ) 1st: LeEco Le 2 http://fkrt.it/!dNlq!NNNN 2nd: Redmi Note 3 32 Version http://fkrt.it/TCI7fTuuuN 3rd: Redmi 3S Prime http://fkrt.it/Tm4hzTuuuN 4th: Le 1S http://fkrt.it/TmQ2zTuuuN 5th Asus Zenfone Max http://amzn.to/2epVdcp 6th Moto G4 Plus http://amzn.to/2eAljt2 2. Best Smartphone Offers: Best Phone Deals on Flipkart - http://fkrt.it/TIxynTuuuN Best Phone Deals on Amazon - http://amzn.to/2epVyfb
Views: 3335 ALL EXAM PLACE
Deep Learning on Graphs (Neo4j Online Meetup #41)
Knowledge graphs generation is outpacing the ability to intelligently use the information that they contain. Octavian's work is pioneering Graph Artificial Intelligence to provide the brains to make knowledge graphs useful. Our neural networks can take questions and knowledge graphs and return answers. Imagine: a google assistant that reads your own knowledge graph (and actually works) a BI tool reads your business' knowledge graph a legal assistant that reads the graph of your case Taking a neural network approach is important because neural networks deal better with the noise in data and variety in schema. Using neural networks allows people to ask questions of the knowledge graph in their own words, not via code or query languages. Octavian's approach is to develop neural networks that can learn to manipulate graph knowledge into answers. This approach is radically different to using networks to generate graph embeddings. We believe this approach could transform how we interact with databases. Prior knowledge of Neural Networks is not required and the talk will include a simple demonstration of how a Neural Network can use graph data. ----------------------------- ABOUT THE SPEAKER ----------------------------- Andy believes that graphs have the potential to provide both a representation of the world and a technical interface that allows us to develop better AI and to turn it rapidly into useful products. Andy combines expertise in machine learning with experience building and operating distributed software systems and an understanding of the scientific process. Before he worked as a software engineer, Andy was a chemist, and he enjoys using the tensor algebra that he learned in quantum chemistry when working on neural networks. ----------------------------- ONLINE DISCUSSIONS ----------------------------- We'll be taking questions live during the session, but if you have any before or after be sure to post them in the project's thread in the Neo4j Community Site (https://community.neo4j.com/t/online-meetup-deep-learning-with-knowledge-graphs/2963). ---------------------------------------------------------------------------------------- WANT TO BE FEATURED IN OUR NEXT NEO4J ONLINE MEETUP? ---------------------------------------------------------------------------------------- We select talks from our Neo4j Community site! https://community.neo4j.com/ To submit your talk, post in in the #projects (if including a link to github or website) or #content (if linking to a blog post, slideshow, video, or article) categories. ------------------------------------------------------------------------- VOTE FOR THE PRESENTATIONS YOU'D LIKE TO SEE! ------------------------------------------------------------------------- 'VOTE' for the projects and content you'd like to see! Browse the the projects and content categories in our community site and 'heart' the ones you're interested in seeing! community.neo4j.com
Views: 3170 Neo4j
LEARNING WITH MOOCS 2015 | Behavior I | PAPERS SESSIONS Analyses leveraging MOOC learner behavior data •Evidence of Short Term Learning from “Drag and Drop” Deliberate Practice Activities Zhongzhou Chen, Christopher Chudzicki, Qian Zhou and Dave Pritchard, MIT & Tsinghua University •Social Network Analysis of MOOCs: Illuminating Learning Networks Drew Paulin, Caroline Haythornthwaite and Leah P. Macfadyen. University of British Columbia •Leveraging the Event-Centered Perspective on Student Behavior Data Eni Mustafaraj, Wellesley College •Towards Support of Collaborative Reflection, Help Exchange, and Group Learning in MOOCs Miaomiao Wen, Oliver Ferschke, and Carolyn Penstein Rosé, Carnegie Mellon University
Views: 181 ColumbiaLearn
11 - Knowledge Management
Lecture Series on Management Information System by Prof. Biswajit Mahanty, Department of Industrial Engineering & Management,IIT Kharagpur. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 24839 nptelhrd
Secret Satellite -- Засекреченный спутник [СУБТИТРЫ]
Spy in the sky. One of the best documentaries ever made! Blast-off into an Era of vacuum tubes, computer punch-cards, International Uncertainty, Political Instability and mutual distrust. We must know the past to comprehend our present completely. But in order to understand something, it has to be compared. What was going on back then shows clearly what is happening in the World today. See the rare archival footage. Hear those who've been there, done that. Do your analysis, logical reasoning and thinking. Enjoy the story of The Cold War and Espionage. The story of people, machines, views and believes. And it goes much deeper!.. See for yourself!! http://en.wikipedia.org/wiki/Cold_War http://en.wikipedia.org/wiki/Espionage http://en.wikipedia.org/wiki/Space_Race http://en.wikipedia.org/wiki/Reconnaissance_satellites en.wikipedia.org/wiki/Corona_(satellite) http://www.daviddarling.info/encyclopedia/C/Corona_satellite.html http://en.wikipedia.org/wiki/Murphy%27s_law https://en.wikipedia.org/wiki/Sod's_law https://en.wikipedia.org/wiki/Central_Intelligence_Agency https://en.wikipedia.org/wiki/National_Reconnaissance_Office https://en.wikipedia.org/wiki/Itek https://en.wikipedia.org/wiki/RAND_Corporation https://en.wikipedia.org/wiki/Lockheed_Corporation https://en.wikipedia.org/wiki/Skunk_Works https://en.wikipedia.org/wiki/Lockheed_U-2 https://en.wikipedia.org/wiki/Thor-Agena Who Owns Space? A Primer on Space Law! https://youtu.be/0pC7Aa3s00Y ✈ ✈ ✈ ✈ ✈ ✈ ✈ ✈ ✈ ✈ ✈ ✈ ✈ ✈ ✈ ✈ ✈ ✈ ✈ I, at any rate, am convinced that God doesn't play dice with the world. — A. Einstein Every time I get to know something new I learn that I know none. Fly the plane. Train your brain. Play the Game. Feel no pain. Narde — Lost fruit of the Tree of Knowledge. To Commemorate, Inspire, Educate... © © © © © © © © © © © © © © © © © © © Produced in association with Discovery Chanel All rights belong to their respective owners. I do not own, nor do I (or) intend to infringe or profit from this content whatsoever. FAIR USE~ Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for "fair use" for purposes such as criticism, comment, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use. → ☠ Video might contain graphic scenes of super-natural phenomena, intensive aerial / psychological combat, engineering construction, deadly weapons in action, mind boggling destructions, brain disturbing reactions, distinctive historical facts, creative human / scientific achievements, real and(or) fictional artifacts. Viewer discretion (ill) advised.
Views: 516202 Babaj Aga
Near-Optimal Parallel Join Processing in MapReduce
Google Tech Talk (more info below) May 5, 2011 Presented by Dr Mirek Riedewald, Associate Professor College of Computer and Information Science Northeastern University http://www.ccs.neu.edu/home/mirek/ ABSTRACT As the amount and complexity of data in many fields increases rapidly, new tools are needed for exploratory analysis and scientific discovery. Our Scolopax system's goal is to address these challenges with novel techniques for large-scale parallel data management. In this talk, we will present an overview of Scolopax and then focus on parallel processing of joins. Joins combine information across data sets, e.g., to discover correlations. Our proposed join model simplifies reasoning about how to assign computation tasks to processors in MapReduce and other parallel environments. Using this model, we derive a surprisingly simple randomized algorithm, called 1-Bucket-Theta, for implementing arbitrary joins in a single MapReduce job. This algorithm only requires minimal statistics (input cardinality) and we provide proofs and strong evidence that for a variety of join problems, its latency is either close to optimal or the best realizable option. For some popular joins we show how to improve over 1-Bucket-Theta by exploiting additional input statistics. Most of these results will appear at SIGMOD 2011.
Views: 4618 GoogleTechTalks
Artificial Intelligence Documentary
Please order ebook/audiobook of this video to support our channel https://www.smashwords.com/books/view/677377, https://www.amazon.co.uk/Artificial-Intelligence-IntroBooks/dp/154878706X/ref=sr_1_1?ie=UTF8&qid=1539002459&sr=8-1&keywords=Artificial+Intelligence+introbooks or https://www.audible.com/pd/Artificial-Intelligence-Audiobook/B01K1BODQO?qid=1539002465&sr=sr_1_1&ref=a_search_c3_lProduct_1_1&pf_rd_p=e81b7c27-6880-467a-b5a7-13cef5d729fe&pf_rd_r=X49BTW0J103A2QSHVKHR& Artificial Intelligence (AI) is to make computers think like humans or that are as intelligent as humans. Thus, the ultimate goal of the research on this topic is to develop a machine that can simulate some human skills and to replace them with some activities.
Views: 7145 Education Channel
Lecture - 25 Rule Based Expart System
Lecture Series on Artificial Intelligence by Prof.Sudeshna Sarkar and Prof.Anupam Basu, Department of Computer Science and Engineering,I.I.T, Kharagpur . For more details on NPTEL visit http://nptel.iitm.ac.in.
Views: 24636 nptelhrd
RailsConf 2014 - An Ode to 17 Databases in 33 Minutes by Toby Hede
A detailed, deep-diving, in-the-deep-end and occasionally humorous whirlwind introduction and analysis of a suite of modern (and sometimes delightfully archaic) database technologies. How they work, why they work, and when you might want them to work in your Rails application. Including but not limited to: PostgreSQL (now with JSON!) Redis Cassandra Hyperdex MongoDb Riak Animated Gifs Toby is a mild-mannered Rails Developer & occasionally Polyglot Programmer, based in Sydney, Australia. He really likes databases. Toby's hobbies include collecting programming languages and databases, playing the drums, cutting code and pondering the nature of existence. Help us caption & translate this video! http://amara.org/v/FGZi/
Views: 4810 Confreaks
The Galaxy Primes by E. E. "Doc" Smith
They were four of the greatest minds in the Universe: Two men, two women, lost in an experimental spaceship billions of parsecs from home. And as they mentally charted the Cosmos to find their way back to earth, their own loves and hates were as startling as the worlds they encountered. Chapter 1 - 00:00 Chapter 2 - 1:01:23 Chapter 3 - 1:39:51 Chapter 4 - 2:26:40 Chapter 5 - 3:09:57 Chapter 6 - 4:06:35 Chapter 7 - 4:59:15 Chapter 8 - 5:54:35 Chapter 9 - 6:42:55 Read by: Mark Nelson (https://librivox.org/reader/251)
Views: 4125 Audiobooks Unleashed
Concerns for Data Scholarship
Although data scholarship has increased the capacity for discovery, concerns such as biased design, participation barriers, and dehumanization remain. Three presentations explore such concerns and propose solutions to this growing dilemma at the Library's symposium, "Collections as Data: Stewardship and Use Models to Enhance Access." "Documenting the Now Project," "Engaging with Communities and API-Driven Accessioning of Digital Folklife" and "Deep-Fried Data." Speaker Biography: Bergis Jules is an archivist at the University of California, Riverside library, where he manages university archives, political papers, African American collections and community archives projects. Speaker Biography: Nicole Saylor is head of the American Folklife Center Archives at the Library of Congress. Speaker Biography: Painter and computer guy Maciej Ceglowski runs Pinboard, a bookmarking site. For transcript, captions, and more information, visit http://www.loc.gov/today/cyberlc/feature_wdesc.php?rec=7621
Views: 295 LibraryOfCongress
NIPS 2011 Learning Semantics Workshop: Towards More Human-like Machine Learning of Word Meanings
Learning Semantics Workshop at NIPS 2011 Invited Talk: Towards More Human-like Machine Learning of Word Meanings by Josh Tenenbaum Josh Tenenbaum is a Professor in the Department of Brain and Cognitive Sciences at Massachusetts Institute of Technology. Him and his colleagues in the Computational Cognitive Science group study one of the most basic and distinctively human aspects of cognition: the ability to learn so much about the world, rapidly and flexibly. Abstract: How can we build machines that learn the meanings of words more like the way that human children do? I will talk about several challenges and how we are beginning to address them using sophisticated probabilistic models. Children can learn words from minimal data, often just one or a few positive examples (one-shot learning). Children learn to learn: they acquire powerful inductive biases for new word meanings in the course of learning their first words. Children can learn words for abstract concepts or types of concepts that have no little or no direct perceptual correlate. Children's language can be highly context-sensitive, with parameters of word meaning that must be computed anew for each context rather than simply stored. Children learn function words: words whose meanings are expressed purely in how they compose with the meanings of other words. Children learn whole systems of words together, in mutually constraining ways, such as color terms, number words, or spatial prepositions. Children learn word meanings that not only describe the world but can be used for reasoning, including causal and counterfactual reasoning. Bayesian learning defined over appropriately structured representations — hierarchical probabilistic models, generative process models, and compositional probabilistic languages — provides a basis for beginning to address these challenges.
Views: 2537 GoogleTechTalks