Înapoi la articole →

Cel mai recent articol

Ultima verificare

Urmăritori 0 urmăritori
Articole: 200
  1. Examples of Influential Power

    Night Ride - April 18, 1775 Paul Revere and William Dawes.

  2. Other Measures of Similarity in NN

    Note: similarity indexes may not specify the 4 NIST specifications of a similarity metric .

  3. Other Measures of Similarity in NN

    This list is not exhaustive.

  4. Return the Average Value One possible solution is to return the average value in the neighborhood, i.e. $$ \mathbb{M}_{k}(q) = \frac{1}{k} \sum_{i=1}^{k} t_i $$ We can improve this by using weighted \(k-NN\): $$ \mathbb{M}_{k}(q) = \frac{ \sum_{i=1}^{k} \…

  5. Return the Average Value One possible solution is to return the average value in the neighborhood, i.e. $$ \mathbb{M}_{k}(q) = \frac{1}{k} \sum_{i=1}^{k} t_i $$ We can improve this by using weighted \(k-NN\): $$ \mathbb{M}_{k}(q) = \frac{ \sum_{i=1}^{k} \…

  6. The Case for Range Normalization

    When you have features taking different range if values, you may have odd predictions.

  7. The Case for Range Normalization

    When you have features taking different range if values, you may have odd predictions.

  8. Majority Voting The \(k\) nearest neighbors model predicts the target level from the majority vote from the set of the \(k\) nearest neighbors to the query \(q\).

  9. Majority Voting The \(k\) nearest neighbors model predicts the target level from the majority vote from the set of the \(k\) nearest neighbors to the query \(q\).

  10. Caveats on Similarity Learning

    Similarity-based learning is intuitive and gives people confidence in the model.

  11. Caveats on Similarity Learning

    Similarity-based learning is intuitive and gives people confidence in the model.

  12. Bayesian Rating

    Allows us to weight by review population size.

  13. The Binomial Random Variable

    \(X\) is a binomial random variable if it takes the values \(0, 1, 2, …, n\) and $$ \mathbb{P}\{X = k\} = { n \choose k } \cdot p^k \cdot (1 - p)^{n-k} $$ Sanity Check: Do the probabilities sum to 1?

  14. Bernoulli Processes

    Bernoulli Process A Bernoulli Process is a sequence of independent \({0, 1}\) - valued random variables \(X_1, X_2, X_3, …\), e.g. \(0, 0, 1, 0, 1, 1\) A Bernoulli Process does not mandate that the probability distributions of the \(X_i\) be identical.

  15. The Bayes Formula

    The Formula By definition… $$ \mathbb{P}(A) = \mathbb{P}(A \cap B) + \mathbb{P}(A \cap B^{c}) $$ From conditional probability … $$ \mathbb{P}(A) = \mathbb{P}(A|B) \ \mathbb{P}(B) + \mathbb{P}(A|B^c) \ \mathbb{P}(B^c) $$ Therefore $$ \mathbb{P}(B|A) = \fra…

  16. Conditional Probability

    Definition Where \( \mathbb{P}(A|B) \) is the probability of event \(A\) given that event \(B\) occurs: $$ \mathbb{P}(A|B) = \frac{\mathbb{P}(A \cap B)}{\mathbb{P}(B)} $$ Notice that if \(P(B) = 0\) then it’s meaningless to condition \(A\) on \(B\).

  17. 000. Longest Two-Character Substring

    Description Given a string, find the longest sub-string that contains, at most, two characters.

  18. 06. Momentum

    Momentum involves giving the reader adequately developed and reasonably arranged material for a smooth ride.

  19. 05. Expansion

    Brevity has its limitations.

  20. 04. Beginning and Ending

    Beginnings Your shot at intriguing or acclimating the reader.

  21. 03. Classification and Order

    Classification Remove material that’s not related to the specific subject-thesis.

  22. 02. The Subject

    The Subject The number of words aren’t intuitive to me.

  23. Formal & Red Herring Fallacies

    Argument from Consequences If God does not exist, then everything is permitted.

  24. Formal & Red Herring Fallacies

    Argument from Consequences If God does not exist, then everything is permitted.

  25. Informal Fallacies

    Ambiguity Equivocation How can you be against faith when you take leaps of faith all the time: making investments, trusting friends, and even getting engaged?

  26. Informal Fallacies

    Ambiguity Equivocation How can you be against faith when you take leaps of faith all the time: making investments, trusting friends, and even getting engaged?

  27. 3. Salesmen

    Emotion Going Outside-In Listeners who listened to the raise-fees message while nodding their heads wanted tution to rise to $646 and claimed that they found the editorial insightful.

  28. 2. Mavens

    While Connectors connect us with new people, Mavens connect us with information.

  29. 1. The 3 Rules of Epidemics

    The Law of the Few From 1995 to 1996, the # of children born in Baltimore with syphillis increased 500%.

  30. 1. Connectors

    The Midnight Ride Afternoon of April 18, 1775: Paul Revere, a silversmith, got word of British planning to raise hell the next day.

  31. 0. Introduction

    Introduction The Tipping Point is the biography of how little things had big effects.

  32. 03. Those Grey Swans of Extremistan

    Notes Mediocristan vs Extremistan: In Extremistan, one single observation can disproportionately affect the outcome.

  33. 02. We Just Can't Predict

    Couples know that a third to a half of all marriages fail, but surely not them!

  34. 01. Umberto Eco's Antilibrary

    The problem of induction: a turkey cannot possibly predict what happens on the afternoon before Thanksgiving.

  35. 01. Invention

    Why Invention?

  36. 00. An Introduction to Writing

    You need to develop a broad spectrum of style and then be able to move from one end of the spectrum to the other as occasion demands.

  37. Against Rationalization

    Gathering evidence IRL may be expensive.

  38. Overly Convenient Excuses

    The Proper Use of Humility Confessing your fallibility but doing nothing about it braggery, not humility.

  39. Myths and Fallacies of 'Personally Identifiable Information'.

  40. Deeper Into Bullshit

    Deeper Into Bullshit Unlike Frankfurt’s argument that “the lack of concern to the truth” is the essence of bullshit , unclarifiability is the essence of bullshit.

  41. The Tipping Point [Malcolm Gladwell]

  42. On Bullshit [Frankfurt]

    Need to develop a theoretical understanding of bullshit.

  43. 03. The Surgical Team

    The Dilemma There are wide productivity variations between good programmers and poor ones.

  44. 02. The Mythical Man-Month

    More software projects have gone awry for lack of calendar time than for all other causes combined.

  45. 01. The Tar Pit

    The fiercer the struggle, the more entangling the tar, and no beast is so strong or so skillful but that he ultimately sinks.