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![How was it before?
from sklearn import svm
model = svm.SVC(gamma=0.001, C=100.)
from sklearn import datasets
digits = datasets.load_digits()
model.fit(digits.data[:-1], digits.target[:-1])
model.predict(digits.data[-1])](https://image.slidesharecdn.com/ttfx-160401074818/75/Machine-Learning-je-m-y-mets-demain-47-2048.jpg)
![How was it before?
from sklearn import svm
model = svm.SVC(gamma=0.001, C=100.)
from sklearn import datasets
digits = datasets.load_digits()
model.fit(digits.data[:-1], digits.target[:-1])
model.predict(digits.data[-1])
WAT?](https://image.slidesharecdn.com/ttfx-160401074818/75/Machine-Learning-je-m-y-mets-demain-48-2048.jpg)
![Scikit
from sklearn import svm
model = svm.SVC(gamma=0.001, C=100.)
from sklearn import datasets
digits = datasets.load_digits()
model.fit(digits.data[:-1], digits.target[:-1])
model.predict(digits.data[-1])](https://image.slidesharecdn.com/ttfx-160401074818/75/Machine-Learning-je-m-y-mets-demain-51-2048.jpg)
![Scikit
from sklearn import svm
model = svm.SVC(gamma=0.001, C=100.)
from sklearn import datasets
digits = datasets.load_digits()
model.fit(digits.data[:-1], digits.target[:-1])
model.predict(digits.data[-1])](https://image.slidesharecdn.com/ttfx-160401074818/75/Machine-Learning-je-m-y-mets-demain-52-2048.jpg)
![AutoML Scikit
import autosklearn
model = autosklearn.AutoSklearnClassifier()
from sklearn import datasets
digits = datasets.load_digits()
model.fit(digits.data[:-1], digits.target[:-1])
model.predict(digits.data[-1])](https://image.slidesharecdn.com/ttfx-160401074818/75/Machine-Learning-je-m-y-mets-demain-53-2048.jpg)
Uploaded byLouis Dorard
Machine Learning: je m'y mets demain!
The document discusses various aspects of machine learning (ML) including its applications, methods, and upcoming events related to ML, such as workshops and meetups. It highlights the use of ML in real estate and startup competitions, the functionality of machine learning APIs, and deep learning concepts for classification problems. Additionally, it promotes the accessibility of ML through cloud platforms and open-source tools.
![Vibe Coding vs. Spec-Driven Development [Free Meetup]](https://cdn.slidesharecdn.com/ss_thumbnails/vibecodingvsspecdrivendevelopment-251209105622-43f455e7-thumbnail.jpg?width=640&height=640&fit=bounds)
Machine Learning: je m'y mets demain!
- 1. Machine Learning: jem’y mets demain! @louisdorard #TTFX - March 31, 2016
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- 4. Amazon for DavidJones (@d_jones, see source)
- 5. Amazon for DavidJones (@d_jones, see source)
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- 18. • Startups pitch •AI asks questions live to each startup • AI assigns score • Startup with highest score wins 100000 € 18 AI Startup Battle at PAPIs.io
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- 21. How does itwork?
- 22. Data + MachineLearning
- 24. Bedrooms Bathrooms Surface(foot²) Year built Type Price ($) 3 1 860 1950 house 565,000 3 1 1012 1951 house 2 1.5 968 1976 townhouse 447,000 4 1315 1950 house 648,000 3 2 1599 1964 house 3 2 987 1951 townhouse 790,000 1 1 530 2007 condo 122,000 4 2 1574 1964 house 835,000 4 2001 house 855,000 3 2.5 1472 2005 house 4 3.5 1714 2005 townhouse 2 2 1113 1999 condo 1 769 1999 condo 315,000
- 25. Bedrooms Bathrooms Surface(foot²) Year built Type Price ($) 3 1 860 1950 house 565,000 3 1 1012 1951 house 2 1.5 968 1976 townhouse 447,000 4 1315 1950 house 648,000 3 2 1599 1964 house 3 2 987 1951 townhouse 790,000 1 1 530 2007 condo 122,000 4 2 1574 1964 house 835,000 4 2001 house 855,000 3 2.5 1472 2005 house 4 3.5 1714 2005 townhouse 2 2 1113 1999 condo 1 769 1999 condo 315,000
- 27. Bedrooms Bathrooms Surface(foot²) Year built Type Price ($) 3 1 860 1950 house 565,000 3 1 1012 1951 house 2 1.5 968 1976 townhouse 447,000 4 1315 1950 house 648,000 3 2 1599 1964 house 3 2 987 1951 townhouse 790,000 1 1 530 2007 condo 122,000 4 2 1574 1964 house 835,000 4 2001 house 855,000 3 2.5 1472 2005 house 4 3.5 1714 2005 townhouse 2 2 1113 1999 condo 1 769 1999 condo 315,000
- 28. ML is aset of AI techniques where “intelligence” is built from examples
- 30. 30 Use cases • Real-estate •Spam filtering • City bikes • Startup competition • Reduce churn • Optimize pricing • Anticipate demand property price email spam indicator location, context #bikes startup success indicator customer churn indicator product, price #sales product, store, date #sales Zillow Gmail V3 predict Preseries ChurnSpotter Amazon Blue Yonder RULES
- 31. Making Machine Learningaccessible with cloud platforms
- 33. HTML / CSS/ JavaScript
- 34. HTML / CSS/ JavaScript
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- 38. The two phasesof ML • TRAIN a model • PREDICT with a model 38 Machine Learning APIs
- 39. The two methodsof ML Application Programming Interfaces (here in Python) • model = create_model(‘training.csv’) • predicted_output, confidence = create_prediction(model, new_input) 39 Machine Learning APIs
- 40. The two methodsof ML Application Programming Interfaces (here in Python) • model = create_model(‘training.csv’) • predicted_output, confidence = create_prediction(model, new_input) 40 Machine Learning APIs
- 41. Example request toBigML API $ curl https://bigml.io/dev/model?$BIGML_AUTH -X POST -H "content-type: application/json" -d '{"dataset": "dataset/50ca447b3b56356ae0000029"}'
- 43. • Classification problem •Features: • Text of email • Sender in address book? • How often do I reply? • How quickly do I reply? • Demo 43 Priority detection
- 45. • VM withJupyter notebooks (Python & Bash) • API wrappers preinstalled: BigML & Google Pred • Notebook for easy setup of credentials • Scikit-learn and Pandas preinstalled • Open source VM provisioning script & notebooks • Search public Snaps on terminal.com:“machine learning” 45 Getting started
- 46. Making Machine Learningeasier
- 47. How was itbefore? from sklearn import svm model = svm.SVC(gamma=0.001, C=100.) from sklearn import datasets digits = datasets.load_digits() model.fit(digits.data[:-1], digits.target[:-1]) model.predict(digits.data[-1])
- 48. How was itbefore? from sklearn import svm model = svm.SVC(gamma=0.001, C=100.) from sklearn import datasets digits = datasets.load_digits() model.fit(digits.data[:-1], digits.target[:-1]) model.predict(digits.data[-1]) WAT?
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- 50. • Spearmint:“Bayesian optimization”fortuning parameters → Whetlab → Twitter • Auto-sklearn:“automated machine learning toolkit and drop- in replacement for a scikit-learn estimator” 50 Open Source AutoML libraries
- 51. Scikit from sklearn importsvm model = svm.SVC(gamma=0.001, C=100.) from sklearn import datasets digits = datasets.load_digits() model.fit(digits.data[:-1], digits.target[:-1]) model.predict(digits.data[-1])
- 52. Scikit from sklearn importsvm model = svm.SVC(gamma=0.001, C=100.) from sklearn import datasets digits = datasets.load_digits() model.fit(digits.data[:-1], digits.target[:-1]) model.predict(digits.data[-1])
- 53. AutoML Scikit import autosklearn model= autosklearn.AutoSklearnClassifier() from sklearn import datasets digits = datasets.load_digits() model.fit(digits.data[:-1], digits.target[:-1]) model.predict(digits.data[-1])
- 54. • Algorithm selection…AutoML • Scaling… Azure ML or Yhat (Greg at PAPIs Connect) • “Automating ML workflows: a report from the trenches”— Jose A. Ortega Ruiz 54 Automatization
- 55. Making Deep Learningaccessible
- 56. • Classification problem •Input is an image = pixel values 56 Image categorization pixel1 pixel2 pixel3 animal? 102 0 255 Yes 35 41 209 No … … … …
- 57. • Neural network: •Layers • Neurons of one layer connected to neurons of next layer • Each neuron receives signals from previous layer and sends new signal to next layer • New signal based on linear combination of signals received • “Deep”-> more than 3 layers 57 Deep Learning
- 58. 58 Deep Learning foranimal detection
- 59. 59 Deep Learning foranimal detection pixel1 pixel2 pixel3 cat dog 1st layer value=(102, 0, 255) Last layer value=(0.1, 0.7, 0.4) Output value=(0.8, 0.3) => there’s probably a cat!
- 60. 60 Deep Learning foranimal detection pixel1 pixel2 pixel3 cat dog 1st layer value=(4, 166, 23) Last layer value=(0.1, 0.7, 0.4) Output value=(0.1, 0.2) => probably no animal here
- 61. pixel1 pixel2 pixel3 cat dog 1st layer value=(102, 0,255) Output value=(0.8, 0.3) => there’s probably a cat! Last layer value=(0.1, 0.7, 0.4)
- 62. 62 Deep Learning foranimal detection pixel1 pixel2 pixel3 animal? 102 0 255 Yes 35 41 209 No … … … … • Replace images with“smart”representation given by last layer neuron1 neuron2 neuron3 animal? 0.1 0.2 0.5 Yes 0.8 0.3 0.8 No … … … …
- 63. • Prochain meetup: •Développer une application prédictive (Hors-série débutants) • Mardi 12 Avril à 19h - Le Node • Workshop: • Operational Machine Learning with open source and cloud platforms • Samedi 23 Avril - sera annoncé sur le Meetup! 63 Prochains événements ML à Bordeaux
- 64. Machine Learning: jem’y mets le 12 et le 23 Avril! meetup.com/Bordeaux-Machine-Learning-Meetup/
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