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Deep Learning con CNTK by Pablo Doval

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Plain Concepts
Plain Concepts

The document is an agenda for a conference on "Intelligent Hospitals: Discovering Digital Health". The agenda includes keynote speeches and sessions on topics like storing and sharing medical images in Azure, applying artificial intelligence to healthcare using Cognitive Services, computer vision, Power BI and machine learning, chatbots, and using virtual and augmented reality in hospitals.

Health & Medicine
1 of 65
21.06.2017 Hospitales Inteligentes: Descubriendo la Sanidad Digital Pablo Álvarez Doval Deep Learning con CNTK Data Team Lead
2 10:00 Keynote ¿Cuál es el futuro de la sanidad digital? 10:15 Almacena y comparte imágenes médicas con Azure 10:30 Beneficios de la Inteligencia artificial aplicada a la sanidad/ Cognitive Services 11:00 Visión artificial/ Cognitive Services 11:45 Break / Networking 12:15 PowerBI + Machine Learning 13:00 Asistentes virtuales/ Bots 13:15 Realidad Virtual y Aumentada en los hospitales 13:30 Experimenta las diferentes realidades con Hololens y Oculus Hospitales Inteligentes: Descubriendo la Sanidad Digital
3 Buenas y malas noticias…
I work with code and data, but don't tell my mom; she thinks I'm a piano player in a whorehouse. Data Pontifex @ Plain Concepts. Pablo Doval @plainconcepts 4 @PabloDoval DATA TEAM LEAD
¿QUÉ ES ESTO?
¿PARA QUE NOS SIRVE ESTO?
MACHINE LEARNING EN AZURE Será por opciones… Azure ML • Fácil y sencillo • Escalabilidad limitada R Server • Lenguaje conocido • Escalado a grandes datasets • Evaluaciones en tiempo real HDInsight • Mahout • SparkML • Escalabilidad casi ilimitada Cognitive Services • APIs listas para usar • Modelos pre- entrenados • No son necesarios conocimientos de ML CNTK • Modelos complejos • Entrenamiento personalizado • Entrenamiento multi server/multi GPU
¿POR QUÉ NO USAR COGNITIVE SERVICES?
Vision Speech Language Knowledge Search
Vision • Computer Vision • Content Moderator • Emotion API • Face API • … Speech Language Knowledge Search
Vision Speech • Bing Speech API • Custom Speech Service • Speaker Recognition Service Language Knowledge Search
Vision Speech Language • Spell Check API • LUIS • Linguistic Analysis • Translator API • … Knowledge Search
Vision Speech Language Knowledge • Entity Linking Service • Academic Knowledge Service • Q&A Maker • … Search
Vision Speech Language Knowledge Search • Autosuggest API • Image Search API • News Search API • …
¿POR QUÉ NO USAR COGNITIVE SERVICES? Vienen pre-entrenados
¿POR QUÉ NO USAR COGNITIVE SERVICES? Vienen pre-entrenados
¿POR QUÉ NO USAR COGNITIVE SERVICES? Vienen pre-entrenados
¿POR QUÉ NO USAR COGNITIVE SERVICES? Vienen pre-entrenados
Deep Learning con CNTK
CNTKNo me preguntéis por el acrónimo. De verdad. ¿Por qué? • Desarrollo más rápido de los modelos • Uso de múltiples CPUs y GPUs • Multi-plataforma • Es el producto que Microsoft usa internamente
• Es el acrónimo del algo. • Es un framework de Deep Learning, y es open-source, con contribuciones externas relevantes del MIT y Stanford, entre otros. • Permite expresar redes neuronales complejas de un modo relativamente sencillo, y se encarga de todos los pasos de su ciclo de vida: desde el entrenamiento hasta la evaluación. • Desarrollo en BrainScript, Python (.NET en camino) CNTK No insistáis. Sigo sin querer comentar lo del dichoso acrónimo.
UN EJEMPLO 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 f(x) f(x) “X” “O”
9 pixels 9pixels 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 …… 81 valores ( 𝑥) Ʃ Ʃ Ʃ Sum (weights x pixels) = 𝑤 𝑥 ⋅ 𝑥 Ʃ Sum (weights x pixels) = 𝑤 𝑂 ⋅ 𝑥 Bias (𝑏) UN EJEMPLO
9 pixels 9pixels 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 …… 81 valores ( 𝑥) Ʃ Ʃ 0.78 0.32 SIGMOIDE
9 pixels 9pixels 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 …… 81 valores ( 𝑥) Ʃ Ʃ 0.75 0.25 SOFTMAX
9 pixels 9pixels 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0.75 0.25 1 0 FUNCION DE PÉRDIDA
9 pixels 9pixels 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0.75 0.25 1 0 FUNCION DE PÉRDIDA
…… …… …… …… . . …… …… …… …… . . x = vector de pixels de entrada y = vector de etiquetas (one-hot) z = times(x, W) + b cross_entropy_with_softmax(z, y) classification_error(z, y) …… …… …… …… . . …… …… …… …… . . x = vector de pixels de entrada ENTRENAMIENTO
• + s • + s • + Softmax W1 b1 W2 b2 Wout bout CrossEntropy h1 h2 P x y h1 = Sigmoid (W1 * x + b1) h2 = Sigmoid (W2 * h1 + b2) P = Softmax (Wout * h2 + bout) ce = CrossEntropy (y, P) ce ¿CÓMO SE DISEÑA ESTO EN CNTK?
Demo: Regresión Logística
Redes Neuronales
Redes Neuronales (Clasificador) Input Layer
Redes Neuronales (Clasificador) Input Layer Hidden Layers Output Layer
Forward Propagation Input Layer Hidden Layers Output Layer
Forward Propagation Input Layer Hidden Layers Output Layer
Forward Propagation Input Layer Hidden Layers Output Layer
Forward Propagation Input Layer Hidden Layers Output Layer
Pesos y Bias Input Layer Hidden Layers Output Layer
Entrenamiento Input Layer Hidden Layers Output Layer Coste = Valor Generado – Valor Real
Backpropagation Coste = Valor Generado – Valor Real Error = X Input Layer Hidden Layers Output Layer
Deep Learning
IMAGENET CHALLENGE
0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 FILTRADO Comparar no siempre es fácil…
-1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 1 -1 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 -1 1 1 1 1 1 1 1 1 1 1 -1 1 -1 1 1 1 -1 1 -1 1 1 -1 1 1 1 -1 1 1 Filtro 1 Filtro 2 Filtro 3 1.0 0.11 0.55 FILTRADO …así que buscamos características comunes
1 -1 -1 -1 1 -1 -1 -1 1 Filtro 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 FILTRADO
1 -1 -1 -1 1 -1 -1 -1 1⊗ -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 = 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 CONVOLUCIÓN
1 -1 -1 -1 1 -1 -1 -1 1⊗ -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 = 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ⊗ = 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -1 -1 1 -1 1 -1 1 -1 -1 CONVOLUCIÓN
1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 ⊗ = 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 1 -1 1 -1 1 -1 1 -1 1 0.33 -0.55 -0.11 -0.11 0.11 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.11 0.33 -0.77 1.00 -0.77 0.33 -0.11 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33 -1 -1 1 -1 1 -1 1 -1 -1
0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 POOLING
0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 POOLING
0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 1.00 0.33 0.55 0.33 0.33 1.00 0.33 0.55 0.55 0.33 1.00 0.11 0.33 0.55 0.11 0.77 POOLING
0.77 0.00 0.11 0.33 0.55 0.00 0.33 0.00 1.00 0.00 0.33 0.00 0.11 0.00 0.11 0.00 1.00 0.00 0.11 0.00 0.55 0.33 0.33 0.00 0.55 0.00 0.33 0.33 0.55 0.00 0.11 0.00 1.00 0.00 0.11 0.00 0.11 0.00 0.33 0.00 1.00 0.00 0.33 0.00 0.55 0.33 0.11 0.00 0.77 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 Rectified Linear Units (ReLU)
1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 1 -1 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 -1 1 Filtro 1 Filtro 2 Filtro 3 ¿De dónde han salido estos? ¿QUÉ FALTA AQUÍ?
Ejemplo: AlexNet
Ejemplo: AlexNet
AlexNet en CNTK (Python)
PRODUCCIONALIZACIÓN ¿Cómo nos llevamos esto a producción?
• AutoEncoders • Recurrent Neural Networks (RNN) • Long Short Term Memory (LSNN) ALGUNAS IDEAS No solo de CNN vive el Data Scientist
¡Cacharread! Cognitive toolkit @ https://cntk.ai Tutoriales @ https://cntk.ai/pythondocs/tutorials.html Notebooks @ https://notebooks.azure.com/cntk/libraries/tutorials Entended el backpropagation, ¡en serio!  CALL TO ACTION
¡GRACIAS! www.plainconcepts.com @plainconcepts
www.plainconcepts.com MADRID Paseo de la Castellana 163, 10º 28046 Madrid. España T. (+34) 91 5346 836 BILBAO Calle Ledesma 10-bis 3º 48001 Bilbao. España T. (+34) 94 6073 371 BARCELONA Carrer Compte d’Urgell 240 4º 1A 08036 Barcelona. España T. (+34) 93 7978 566 SEVILLA Avenida de la innovación s/n Edificio Renta Sevilla, 3º A 41020 Sevilla. España DUBAI Dubai Internet City. Building 1 73030 Dubai. EAU T. (+971) 4 551 6653 LONDON Impact Hub Kings Cross 24B York Way, N1 9AB London. UK SEATTLE 1511, Third Ave Seattle WA 98101. USA T. (+1) 206 708 1285

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Deep Learning con CNTK by Pablo Doval

  • 1. 21.06.2017 Hospitales Inteligentes: Descubriendo la Sanidad Digital Pablo Álvarez Doval Deep Learning con CNTK Data Team Lead
  • 2. 2 10:00 Keynote ¿Cuál es el futuro de la sanidad digital? 10:15 Almacena y comparte imágenes médicas con Azure 10:30 Beneficios de la Inteligencia artificial aplicada a la sanidad/ Cognitive Services 11:00 Visión artificial/ Cognitive Services 11:45 Break / Networking 12:15 PowerBI + Machine Learning 13:00 Asistentes virtuales/ Bots 13:15 Realidad Virtual y Aumentada en los hospitales 13:30 Experimenta las diferentes realidades con Hololens y Oculus Hospitales Inteligentes: Descubriendo la Sanidad Digital
  • 3. 3 Buenas y malas noticias…
  • 4. I work with code and data, but don't tell my mom; she thinks I'm a piano player in a whorehouse. Data Pontifex @ Plain Concepts. Pablo Doval @plainconcepts 4 @PabloDoval DATA TEAM LEAD
  • 6. ¿PARA QUE NOS SIRVE ESTO?
  • 7.
  • 8.
  • 9.
  • 10. MACHINE LEARNING EN AZURE Será por opciones… Azure ML • Fácil y sencillo • Escalabilidad limitada R Server • Lenguaje conocido • Escalado a grandes datasets • Evaluaciones en tiempo real HDInsight • Mahout • SparkML • Escalabilidad casi ilimitada Cognitive Services • APIs listas para usar • Modelos pre- entrenados • No son necesarios conocimientos de ML CNTK • Modelos complejos • Entrenamiento personalizado • Entrenamiento multi server/multi GPU
  • 11. ¿POR QUÉ NO USAR COGNITIVE SERVICES?
  • 13. Vision • Computer Vision • Content Moderator • Emotion API • Face API • … Speech Language Knowledge Search
  • 14. Vision Speech • Bing Speech API • Custom Speech Service • Speaker Recognition Service Language Knowledge Search
  • 15. Vision Speech Language • Spell Check API • LUIS • Linguistic Analysis • Translator API • … Knowledge Search
  • 16. Vision Speech Language Knowledge • Entity Linking Service • Academic Knowledge Service • Q&A Maker • … Search
  • 17. Vision Speech Language Knowledge Search • Autosuggest API • Image Search API • News Search API • …
  • 18. ¿POR QUÉ NO USAR COGNITIVE SERVICES? Vienen pre-entrenados
  • 19. ¿POR QUÉ NO USAR COGNITIVE SERVICES? Vienen pre-entrenados
  • 20. ¿POR QUÉ NO USAR COGNITIVE SERVICES? Vienen pre-entrenados
  • 21. ¿POR QUÉ NO USAR COGNITIVE SERVICES? Vienen pre-entrenados
  • 23. CNTKNo me preguntéis por el acrónimo. De verdad. ¿Por qué? • Desarrollo más rápido de los modelos • Uso de múltiples CPUs y GPUs • Multi-plataforma • Es el producto que Microsoft usa internamente
  • 24. • Es el acrónimo del algo. • Es un framework de Deep Learning, y es open-source, con contribuciones externas relevantes del MIT y Stanford, entre otros. • Permite expresar redes neuronales complejas de un modo relativamente sencillo, y se encarga de todos los pasos de su ciclo de vida: desde el entrenamiento hasta la evaluación. • Desarrollo en BrainScript, Python (.NET en camino) CNTK No insistáis. Sigo sin querer comentar lo del dichoso acrónimo.
  • 25. UN EJEMPLO 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 f(x) f(x) “X” “O”
  • 26. 9 pixels 9pixels 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 …… 81 valores ( 𝑥) Ʃ Ʃ Ʃ Sum (weights x pixels) = 𝑤 𝑥 ⋅ 𝑥 Ʃ Sum (weights x pixels) = 𝑤 𝑂 ⋅ 𝑥 Bias (𝑏) UN EJEMPLO
  • 27. 9 pixels 9pixels 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 …… 81 valores ( 𝑥) Ʃ Ʃ 0.78 0.32 SIGMOIDE
  • 28. 9 pixels 9pixels 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 …… 81 valores ( 𝑥) Ʃ Ʃ 0.75 0.25 SOFTMAX
  • 29. 9 pixels 9pixels 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0.75 0.25 1 0 FUNCION DE PÉRDIDA
  • 30. 9 pixels 9pixels 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0.75 0.25 1 0 FUNCION DE PÉRDIDA
  • 31. …… …… …… …… . . …… …… …… …… . . x = vector de pixels de entrada y = vector de etiquetas (one-hot) z = times(x, W) + b cross_entropy_with_softmax(z, y) classification_error(z, y) …… …… …… …… . . …… …… …… …… . . x = vector de pixels de entrada ENTRENAMIENTO
  • 32. • + s • + s • + Softmax W1 b1 W2 b2 Wout bout CrossEntropy h1 h2 P x y h1 = Sigmoid (W1 * x + b1) h2 = Sigmoid (W2 * h1 + b2) P = Softmax (Wout * h2 + bout) ce = CrossEntropy (y, P) ce ¿CÓMO SE DISEÑA ESTO EN CNTK?
  • 36. Redes Neuronales (Clasificador) Input Layer Hidden Layers Output Layer
  • 37. Forward Propagation Input Layer Hidden Layers Output Layer
  • 38. Forward Propagation Input Layer Hidden Layers Output Layer
  • 39. Forward Propagation Input Layer Hidden Layers Output Layer
  • 40. Forward Propagation Input Layer Hidden Layers Output Layer
  • 41. Pesos y Bias Input Layer Hidden Layers Output Layer
  • 42. Entrenamiento Input Layer Hidden Layers Output Layer Coste = Valor Generado – Valor Real
  • 43. Backpropagation Coste = Valor Generado – Valor Real Error = X Input Layer Hidden Layers Output Layer
  • 46. 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 FILTRADO Comparar no siempre es fácil…
  • 47. -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 1 -1 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 -1 1 1 1 1 1 1 1 1 1 1 -1 1 -1 1 1 1 -1 1 -1 1 1 -1 1 1 1 -1 1 1 Filtro 1 Filtro 2 Filtro 3 1.0 0.11 0.55 FILTRADO …así que buscamos características comunes
  • 48. 1 -1 -1 -1 1 -1 -1 -1 1 Filtro 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 FILTRADO
  • 49. 1 -1 -1 -1 1 -1 -1 -1 1⊗ -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 = 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 CONVOLUCIÓN
  • 50. 1 -1 -1 -1 1 -1 -1 -1 1⊗ -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 = 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ⊗ = 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -1 -1 1 -1 1 -1 1 -1 -1 CONVOLUCIÓN
  • 51. 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 -1 -1 -1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 ⊗ = 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 1 -1 1 -1 1 -1 1 -1 1 0.33 -0.55 -0.11 -0.11 0.11 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.11 0.33 -0.77 1.00 -0.77 0.33 -0.11 0.11 -0.55 0.55 -0.77 0.55 -0.55 0.11 -0.55 0.55 -0.55 0.33 -0.55 0.55 -0.55 0.33 -0.55 0.11 -0.11 0.11 -0.55 0.33 -1 -1 1 -1 1 -1 1 -1 -1
  • 52. 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 POOLING
  • 53. 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 POOLING
  • 54. 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 1.00 0.33 0.55 0.33 0.33 1.00 0.33 0.55 0.55 0.33 1.00 0.11 0.33 0.55 0.11 0.77 POOLING
  • 55. 0.77 0.00 0.11 0.33 0.55 0.00 0.33 0.00 1.00 0.00 0.33 0.00 0.11 0.00 0.11 0.00 1.00 0.00 0.11 0.00 0.55 0.33 0.33 0.00 0.55 0.00 0.33 0.33 0.55 0.00 0.11 0.00 1.00 0.00 0.11 0.00 0.11 0.00 0.33 0.00 1.00 0.00 0.33 0.00 0.55 0.33 0.11 0.00 0.77 0.77 -0.11 0.11 0.33 0.55 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.11 -0.11 0.11 -0.11 1.00 -0.33 0.11 -0.11 0.55 0.33 0.33 -0.33 0.55 -0.33 0.33 0.33 0.55 -0.11 0.11 -0.33 1.00 -0.11 0.11 -0.11 0.11 -0.11 0.33 -0.11 1.00 -0.11 0.33 -0.11 0.55 0.33 0.11 -0.11 0.77 Rectified Linear Units (ReLU)
  • 56. 1 -1 -1 -1 1 -1 -1 -1 1 -1 -1 1 -1 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 -1 1 Filtro 1 Filtro 2 Filtro 3 ¿De dónde han salido estos? ¿QUÉ FALTA AQUÍ?
  • 59.
  • 60. AlexNet en CNTK (Python)
  • 62. • AutoEncoders • Recurrent Neural Networks (RNN) • Long Short Term Memory (LSNN) ALGUNAS IDEAS No solo de CNN vive el Data Scientist
  • 63. ¡Cacharread! Cognitive toolkit @ https://cntk.ai Tutoriales @ https://cntk.ai/pythondocs/tutorials.html Notebooks @ https://notebooks.azure.com/cntk/libraries/tutorials Entended el backpropagation, ¡en serio!  CALL TO ACTION
  • 65. www.plainconcepts.com MADRID Paseo de la Castellana 163, 10º 28046 Madrid. España T. (+34) 91 5346 836 BILBAO Calle Ledesma 10-bis 3º 48001 Bilbao. España T. (+34) 94 6073 371 BARCELONA Carrer Compte d’Urgell 240 4º 1A 08036 Barcelona. España T. (+34) 93 7978 566 SEVILLA Avenida de la innovación s/n Edificio Renta Sevilla, 3º A 41020 Sevilla. España DUBAI Dubai Internet City. Building 1 73030 Dubai. EAU T. (+971) 4 551 6653 LONDON Impact Hub Kings Cross 24B York Way, N1 9AB London. UK SEATTLE 1511, Third Ave Seattle WA 98101. USA T. (+1) 206 708 1285

Editor's Notes

  1. Todos los nodos se activan con la misma función de activación. ¿Por qué entonces tienen valores diferentes? Se debe a que la entrada de la función de activación esta modificada por el peso y el bias.