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Learning Machine
Learning
A little intro to a (not that complex) world
@joel__lord
#phpbnl18
About Me
@joel__lord
joellord
@joel__lord
#phpbnl18
Our Agenda for today…
• AI vs ML
• Deep Learning &
Neural Networks
• Supervised vs
unsupervised
• Na...
@joel__lord
#phpbnl18
@joel__lord
#phpbnl18
Artificial Intelligence
Artificial intelligence (AI)
is intelligence exhibited by machines.
In compu...
@joel__lord
#phpbnl18
Artificial Intelligence
“takes actions that
maximize its chance of
success at some goal”
@joel__lord
#phpbnl18
Examples in real life
@joel__lord
#phpbnl18
Machine Learning
Machine learning (ML) is the subfield
of computer science that gives "computers
the...
@joel__lord
#phpbnl18
@joel__lord
#phpbnl18
@joel__lord
#phpbnl18
@joel__lord
#phpbnl18
@joel__lord
#phpbnl18
@joel__lord
#phpbnl18
“Don’t be afraid of artificial
intelligence, be afraid of humanity.”
@joel__lord
#phpbnl18
Deep Learning
& Big Data
• Explosion of digital data
• Can’t be processed with
traditional methods
a...
@joel__lord
#phpbnl18
Neural
Networks
• Breaking big
problems in small
layers
@joel__lord
#phpbnl18
Supervised
Learning
• Requires feedback
• Starts with nothing
and increases its
understanding
• Usel...
@joel__lord
#phpbnl18
Unsupervised
Learning
• There is no feedback
• Good in the case of no right or
wrong answer
• Helps ...
@joel__lord
#phpbnl18
The Naïve Bayes Classifier
@joel__lord
#phpbnl18
Bayes Theorem
@joel__lord
#phpbnl18
Bayes Theorem
where
@joel__lord
#phpbnl18
Bayes Theorem
•  
@joel__lord
#phpbnl18
Bayes Theorem
•  
@joel__lord
#phpbnl18
Naive Bayes
Classifier
• Let’s look at a concrete
example.
• You never know what
you’re gonna get
@joel__lord
#phpbnl18
Probability that a chocolate has nuts
Nuts No Nuts
Round 25% 75%
Square 75% 25%
Dark 10% 90%
Light 9...
@joel__lord
#phpbnl18
Do round, light chocolates have nuts?
Nuts No Nuts
Round 25% 75% 0.25 0.75
Square 75% 25% - -
Dark 1...
@joel__lord
#phpbnl18
Do round, light chocolates have nuts?
Nuts No Nuts
Round 25% 75% 0.25 0.75
Square 75% 25% - -
Dark 1...
@joel__lord
#phpbnl18
Do round, light chocolates have nuts?
Nuts No Nuts
Round 25% 75% 0.25 0.75
Square 75% 25% - -
Dark 1...
@joel__lord
#phpbnl18
Naïve Bayes Classifier in code
var Classifier = function() {
this.dictionaries = {};
};
Classifier.p...
@joel__lord
#phpbnl18
@joel__lord
#phpbnl18
Sentiment
Analysis
• Not Machine
Learning
• Uses classifiers and
AFINN-165 (and
emojis)
@joel__lord
#phpbnl18
Sentiment
Analysis
• Javascript:
• npm install
sentiment
• PHP:
• composer require
risan/sentiment-
...
@joel__lord
#phpbnl18
Genetic
Algorithm
• Awesome shit!
@joel__lord
#phpbnl18
Genetic
Algorithm
• Create a population of
random individuals
• Keep the closest individuals
• Keep ...
@joel__lord
#phpbnl18
Genetic
Algorithm
• Create a population of
random individuals
• Keep the closest individuals
• Keep ...
@joel__lord
#phpbnl18
Genetic Algorithm
Credit: AutoDesk https://autodeskresearch.com/projects/
Dreamcatcher
@joel__lord
#phpbnl18
https://www.youtube.com/watch?v=pgaEE27nsQw
@joel__lord
#phpbnl18
Genetic Algorithm in code
//Declare Consts
function randomInt(min, max) {…}
function random(min, max...
@joel__lord
#phpbnl18
What did we learn?
• Machine Learning and Artificial Intelligence
• Big Data and Deep Learning
• Sup...
Presented By
JOEL LORD
PHP Benelux, January 26th 2018
@joel__lord
joellord
Thank you!
Presented By
JOEL LORD
PHP Benelux, January 26th 2018
@joel__lord
joellord
Questions?
Impact of parameters on Genetic Algorithms
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Learning Machine Learning

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Slides from my talk at PHP Benelux 2018. Abstract:
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 about the basics of machine learning through various basic examples, without the need for a PhD or deep knowledge of assembly. 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 Javascript.

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Learning Machine Learning

  1. 1. Learning Machine Learning A little intro to a (not that complex) world
  2. 2. @joel__lord #phpbnl18 About Me @joel__lord joellord
  3. 3. @joel__lord #phpbnl18 Our Agenda for today… • AI vs ML • Deep Learning & Neural Networks • Supervised vs unsupervised • Naïve Bayes Classifier • Genetic Algorithms
  4. 4. @joel__lord #phpbnl18
  5. 5. @joel__lord #phpbnl18 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 #phpbnl18 Artificial Intelligence “takes actions that maximize its chance of success at some goal”
  7. 7. @joel__lord #phpbnl18 Examples in real life
  8. 8. @joel__lord #phpbnl18 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 #phpbnl18
  10. 10. @joel__lord #phpbnl18
  11. 11. @joel__lord #phpbnl18
  12. 12. @joel__lord #phpbnl18
  13. 13. @joel__lord #phpbnl18
  14. 14. @joel__lord #phpbnl18 “Don’t be afraid of artificial intelligence, be afraid of humanity.”
  15. 15. @joel__lord #phpbnl18 Deep Learning & Big Data • Explosion of digital data • Can’t be processed with traditional methods anymore
  16. 16. @joel__lord #phpbnl18 Neural Networks • Breaking big problems in small layers
  17. 17. @joel__lord #phpbnl18 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 #phpbnl18 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 #phpbnl18 The Naïve Bayes Classifier
  20. 20. @joel__lord #phpbnl18 Bayes Theorem
  21. 21. @joel__lord #phpbnl18 Bayes Theorem where
  22. 22. @joel__lord #phpbnl18 Bayes Theorem •  
  23. 23. @joel__lord #phpbnl18 Bayes Theorem •  
  24. 24. @joel__lord #phpbnl18 Naive Bayes Classifier • Let’s look at a concrete example. • You never know what you’re gonna get
  25. 25. @joel__lord #phpbnl18 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 #phpbnl18 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 #phpbnl18 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 0.225 0.075
  28. 28. @joel__lord #phpbnl18 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 0.225 0.075  
  29. 29. @joel__lord #phpbnl18 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 #phpbnl18
  31. 31. @joel__lord #phpbnl18 Sentiment Analysis • Not Machine Learning • Uses classifiers and AFINN-165 (and emojis)
  32. 32. @joel__lord #phpbnl18 Sentiment Analysis • Javascript: • npm install sentiment • PHP: • composer require risan/sentiment- analysis
  33. 33. @joel__lord #phpbnl18 Genetic Algorithm • Awesome shit!
  34. 34. @joel__lord #phpbnl18 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 #phpbnl18 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 #phpbnl18 Genetic Algorithm Credit: AutoDesk https://autodeskresearch.com/projects/ Dreamcatcher
  37. 37. @joel__lord #phpbnl18 https://www.youtube.com/watch?v=pgaEE27nsQw
  38. 38. @joel__lord #phpbnl18 Genetic Algorithm in code //Declare Consts function randomInt(min, max) {…} function random(min, max) {…} function fitness(individual) {…} function sortByFitness(population) {…} function randomIndividual() {…} function randomPopulation(size) {…} 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. @joel__lord #phpbnl18 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
  40. 40. Presented By JOEL LORD PHP Benelux, January 26th 2018 @joel__lord joellord Thank you!
  41. 41. Presented By JOEL LORD PHP Benelux, January 26th 2018 @joel__lord joellord Questions?
  42. 42. Impact of parameters on Genetic Algorithms
  • DavideDellErba1

    Apr. 26, 2018
  • JelleSmeets

    Jan. 29, 2018
  • nikolaizujev

    Jan. 29, 2018

Slides from my talk at PHP Benelux 2018. Abstract: 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 about the basics of machine learning through various basic examples, without the need for a PhD or deep knowledge of assembly. 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 Javascript.

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