Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Learning About Machine Learning

173 views

Published on

Slides for my talk at PHP[tek] 2017 in Atlanta, GA
From chatbots to your home thermostat, it seems like machine learning algorithms are everywhere nowadays. How about understanding how this works now? In this talk, you will learn the basics of machine learning through various basic examples, without the need for a Ph.D. At the end of this talk, you will know what the Naive Bayes classifiers, sentiment analysis, and basic genetic algorithms are and how they work. You will also see how to create your own implementations in PHP.

Published in: Technology
  • Be the first to comment

Learning About Machine Learning

  1. 1. Learning Machine Learning A little intro to a (not that complex) world
  2. 2. @joel__lord #phptek About Me
  3. 3. @joel__lord #phptek Our Agenda for today… • AI vs ML • Deep Learning & Neural Networks • Supervised vs unsupervised • Naïve Bayes Classifier • Genetic Algorithms • Q&A
  4. 4. @joel__lord #phptek
  5. 5. @joel__lord #phptek Artificial Intelligence Artificial intelligence (AI) is intelligence exhibited by machines. In computer science, the field of AI research defines itself as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of success at some goal.
  6. 6. @joel__lord #phptek Artificial Intelligence “takes actions that maximize its chance of success at some goal”
  7. 7. @joel__lord #phptek Examples in real life
  8. 8. @joel__lord #phptek Machine Learning Machine learning (ML) is the subfield of computer science that gives "computers the ability to learn without being explicitly programmed."
  9. 9. @joel__lord #phptek
  10. 10. @joel__lord #phptek
  11. 11. @joel__lord #phptek
  12. 12. @joel__lord #phptek
  13. 13. @joel__lord #phptek
  14. 14. @joel__lord #phptek “Don’t be afraid of artificial intelligence, be afraid of humanity.”
  15. 15. @joel__lord #phptek Deep Learning & Big Data • Explosion of digital data • Can’t be processed with traditional methods anymore
  16. 16. @joel__lord #phptek Neural Networks • Breaking big problems in small layers
  17. 17. @joel__lord #phptek Supervised Learning • Requires feedback • Starts with nothing and increases its understanding • Useless if the data is of bad quality • Use cases: • Classification
  18. 18. @joel__lord #phptek Unsupervised Learning • There is no feedback • Good in the case of no right or wrong answer • Helps to identify patterns or data structures • Use case: • You might also be interested in… • Grouping customers by purchasing behaviors
  19. 19. @joel__lord #phptek The Naïve Bayes Classifier
  20. 20. @joel__lord #phptek Bayes Theorem • 𝑃 𝐴 𝐵 = % & % & '% (
  21. 21. @joel__lord #phptek Bayes Theorem • 𝑃 𝐴 𝐵 = % 𝐵 𝐴 % & % 𝐵 𝐴 % & ' )*% 𝐵 𝐴 )*% &
  22. 22. @joel__lord #phptek Bayes Theorem • 𝑃 𝐴 𝐵 = ∏ % 𝐴 𝑊- . /01 ∏ % 𝐴 𝑊- . /01 ' ∏ )*% 𝐴 𝑊- . /01
  23. 23. @joel__lord #phptek Bayes Theorem • 𝑃 𝐴 𝐵 = 𝑊2∱
  24. 24. @joel__lord #phptek Naive Bayes Classifier • Let’s look at a concrete example. • You never know what you’re gonna get
  25. 25. @joel__lord #phptek Probability that a chocolate has nuts Nuts No Nuts Round 25% 75% Square 75% 25% Dark 10% 90% Light 90% 10%
  26. 26. @joel__lord #phptek Do round, light chocolates have nuts? Nuts No Nuts Round 25% 75% 0.25 0.75 Square 75% 25% - - Dark 10% 90% - - Light 90% 10% 0.9 0.1
  27. 27. @joel__lord #phptek Do round, light chocolates have nuts? Nuts No Nuts Round 25% 75% 0.25 0.75 Square 75% 25% - - Dark 10% 90% - - Light 90% 10% 0.9 0.1 4 𝑷𝒊 𝒏 𝒊8𝟏 0.225 0.075
  28. 28. @joel__lord #phptek Do round, light chocolates have nuts? Nuts No Nuts Round 25% 75% 0.25 0.75 Square 75% 25% - - Dark 10% 90% - - Light 90% 10% 0.9 0.1 4 𝑷𝒊 𝒏 𝒊8𝟏 0.225 0.075 𝑃 🌰 = 0.225 0.225 + 0.075 = 0.75 = 75%
  29. 29. @joel__lord #phptek Naïve Bayes Classifier in code var Classifier = function() { this.dictionaries = {}; }; Classifier.prototype.classify = function(text, group) { }; Classifier.prototype.categorize = function(text) { };
  30. 30. @joel__lord #phptek
  31. 31. @joel__lord #phptek Sentiment Analysis • Not Machine Learning • Uses classifiers and AFINN-165 (and emojis)
  32. 32. @joel__lord #phptek Sentiment Analysis • Javascript: • npm install sentiment • PHP: • composer require risan/sentiment- analysis
  33. 33. @joel__lord #phptek Genetic Algorithm • Awesome shit!
  34. 34. @joel__lord #phptek Genetic Algorithm • Create a population of random individuals • Keep the closest individuals • Keep a few random individuals • Introduce random mutations • Randomly create ”children” • Magically end up with a valid solution
  35. 35. @joel__lord #phptek Genetic Algorithm • Create a population of random individuals • Keep the closest individuals • Keep a few random individuals • Introduce random mutations • Randomly create ”children” • Magically end up with a valid solution
  36. 36. @joel__lord #phptek Genetic Algorithm Credit: AutoDesk https://autodeskresearch.com/projects/Dreamcatcher
  37. 37. @joel__lord #phptek https://www.youtube.com/watch?v=yci5FuI1ovk
  38. 38. @joel__lord #phptek Genetic Algorithm in code //Declare Consts function randomInt(min, max) {…} function random(min, max) {…} function randomIndividual() {…} function randomPopulation(size) {…} function fitness(individual) {…} function sortByFitness(population) {…} function mutate(population) {…} function reproduce(father, mother) {…} function evolve(population) {…} function findSolution() { var population = randomPopulation(POP_SIZE); var generation = 0; while (fitness(population[0]) > CLOSE_ENOUGH) { generation++; population = evolve(population); } return {solution: population[0], generations: generation}; } var sol = findSolution();
  39. 39. Impact of parameters on Genetic Algorithms
  40. 40. @joel__lord #phptek What did we learn? • Machine Learning and Artificial Intelligence • Big Data and Deep Learning • Supervised vs unsupervised • Basic Algorithms • Naïve Bayes Classifier • Sentiment Analysis • Genetic Algorithm • Hopefully, you don’t feel intimidated by ML anymore
  41. 41. Presented By JOEL LORD PHP [tek], May 26th 2017 @joel__lord joellord Thank you https://joind.in/talk/47902
  42. 42. Presented By JOEL LORD PHP [tek], May 26th 2017 @joel__lord joellord Questions? https://joind.in/talk/47902

×