Curso de Redes neuronales con Keras

Curso de Redes neuronales con Keras

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Descripción:

Este curso comienza con la introducción a algoritmos de aprendizaje supervisado, como la regresión lineal simple, el perceptrón multicapa clásico y las redes convolucionales profundas más sofisticadas. También explorará el procesamiento de imágenes con el reconocimiento de imágenes escritas a mano, la clasificación de imágenes en diferentes categorías y el reconocimiento avanzado de objetos con anotaciones de imágenes relacionadas. También se proporciona un ejemplo de identificación de puntos destacados para la detección de rostros. A continuación, se le presentará las Redes recurrentes, que están optimizadas para procesar datos de secuencia como texto, audio o series temporales. También, aprenderá sobre algoritmos de aprendizaje no supervisados, como los Autoencoders y las muy populares Generative Adversarial Networks (GAN). También explorará los usos no tradicionales de redes neuronales como Style Transfer. Finalmente, verás Refuerzo de Aprendizaje y su aplicación a AI, otra dirección popular de investigación y aplicación de redes neuronales..

Objetivo:

Entre otras cosas, al término el Curso de Redes neuronales con Keras, el alumno habrá aprendido a: Optimizar las funciones paso a paso en una gran red neuronal utilizando el algoritmo Backpropagation. Afinar una red neuronal para mejorar la calidad de los resultado. Usar aprendizaje profundo para procesamiento de imagen y audio. Usar redes de tensores neuronales recursivas (RNTN) para superar la incrustación de palabras estándar en casos especiales. Identificar problemas para los cuales las soluciones de red neuronal recurrente (RNN) son adecuadas. Explorar el proceso requerido para implementar Autoencoders. Desarrollar una red neuronal profunda usando el aprendizaje de refuerzo.

Temario:

Parte 1: CONFIGURACIÓN DEL ENTORNO KERAS

Instalación de Python | Instalación de Theano | Instalación de TensorFlow | Instalación de Keras | Keras sobre Docker | Keras sobre Amazon AWS | Keras sobre Microsoft Azure | Evaluación de la Arquitectura

Parte 2: FUNDAMENTOS DE REDES NEURONALES

Perceptron | Multilayer perceptron | Funciones de activación | Sigmoid | ReLU | Otras | Ejemplo real | Backpropagation

Parte 3: CONVNETS

Deep Convolutional Neural Networks | Ejemplo LeNet | Código LeNet en Keras | Reconocimiento de Imágenes CIFAR-10 | Very deep convolutional networks para reconocimiento de imágenes de gran escala

Parte 4: GENERATIVE ADVERSIAL NETWORKS Y WAVENET

Modelo GAN | Deep Convolutional GAN | Adversarial GANs para MNIST | Adversarial GANs para CIFAR | WaveNet

Parte 5: WORD EMBEDDINGS

word2vec | Glove | Embeddings entrenados previamente

Parte 6: RECURRENT NEURAL NETWORKS

Células Simple RNN | Topologías | Gradientes desvanecidos y explosivos | Long Short Term Memory | Gated recurrent Unit | RNN Bi-direccional | RNN Stateful | Otras variantes de RNN

Parte 7: OTROS MODELOS

Redes de Regresión | Aprendizaje no supervisado | Deep Networks | Personalización de Keras | Modelos Generativos

El plan de estudios está vacío.
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