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Conference Google: Construire des solutions plus intelligentes sans expertise en machine learning

Sans expertise en ML != Sans ML

Intro

Who are we ?

Laurent Picard

  • Developer advocate - Google Cloud
  • Ebook pioner

Any sufficiently advanced technology is indistiguishable from magic

  • Arthur C. Clarke

What is machine learning ?

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Why is machine learning now possible ?

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Three ways we can benefit from ML today

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Ne reiventez pas la roue !

Nouveau champ: auto-ML

  • on peut construire nos propres models sans expertise

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Machine learning API

Ready-to-use models

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Vision API

Computer vision before ML:

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Landmark detection:

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Capable de determiner ou a ete prise la photo (quel endroit)

La photo originale a ete modifiee (symetrie horizontale)

  • Toujours capable de determiner l'origine de la photo

Object detection:

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Face detection:

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Vue 3D de Gollum donc pas un vrai visage humain (mais marche quand meme !)

Text detection:

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Meme avec une legere rotation, on detecte toujours le texte

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Detecte egalement l'ecriture manuscrite (quelques erreurs)

Web entity detection and image matching:

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La photo ci-dessus de Tolkien est totalement inedite pour l'API utilisee, capable de reconnaitre Tolkien + determiner que l'origine est un journal espagnol

OSS Client libraries

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Librairies clientes en open-source sur GitHub dans plusieurs langages.

Video Intelligence API

Demo:

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OSS Client libraries

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Natural Language API

Analyze text with a simple request

Syntax analysis:

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Entity detection

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Content classification

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Sentiment analysis:

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Le ML se plante totalement sur la detection du sarcasme.

Translation API

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Google Translate par exemple!

On peut les ameliorer regulierement en fournissant de plus en plus d'exemples et de contre-exemples.

Speech-to-Text API

Convert text to speech in 120 languages with a simple request.

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Fonctionne en temps reel.

Ex: il y a quelques annees repeter a un bot en appelant une banque "Je veux un conseiller" en esperant qu'il comprenne.

Consequence sympa des reseaux neuronaux: aujourd'hui les speech-to-text API sont resistants aux bruits car ils apprennent a partir de vrais echantillon.

Speech timestamps:
Search for text within your audio

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OSS Client libraries

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Text-to-speech (TTS) API

Generate natural speech with a simple request

WaveNet natural voices, par Deepmind

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C'est le modele le plus avance de tous, qui reproduit le mieux une voix humaine.

Demo: "Quelle est la temperature a Paris ?" avec un accent anglais

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Tadaaaaaa

OSS Client libraries

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Tuto pour generer des voix

AutoML

Build your custom model with no expertise

Generic results with the Vision API

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Cloud AutoML

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Demo:

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Utilisation de ~250 images en moyennes

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"Juste" 3h de calculs.

Auto-generate a custom model from your data

Unified in AI Platform

Demo

Evaluation

Transfert learning

Hyperparameter tuning

Conlusion

How can I build smarter solutions ?

Liens utiles

→ Présentation
→ BD Google AI
→ ML codelabs

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