En este post les compartiré una serie de ejemplos para aprender a programar redes neuronales. Puede parecer una tarea abrumadora al principio, pero revisando los diversos modelos funcionales y un poco de abstracción, comprenderán el funcionamiento con mayor detalle.
- Red Neuronal Simple de un Perceptrón para Clasificación Binaria
- Red Neuronal Feedforward con TensorFlow.
- Red Neuronal Convolucional (CNN) para Clasificación de Imágenes
- Red Neuronal Recurrente (RNN) para Predicción de Series Temporales
- Red Neuronal Convolucional (CNN) para Reconocimiento de Dígitos.
- Red Neuronal Recurrente (RNN) con TensorFlow para Procesamiento de Lenguaje Natural.
- Transferencia de Aprendizaje con TensorFlow: Fine-Tuning de un Modelo Pre-entrenado.
- Red Neuronal LSTM para Generación de Texto
- Red Neuronal Generativa Adversarial (GAN)
- Red Neuronal Siamesa en TensorFlow.
- Detección de Objetos con TensorFlow y TensorFlow Object Detection API.
Con estos ejemplos sencillos, observaremos como estas redes aprenden y se adaptan, lo cual es muy diferente a la programación tradicional, donde debemos especificar reglas y algoritmos de manera explícita. En una red neuronal simple, los parámetros esenciales incluirán pesos y sesgos, funciones de activación, funciones de pérdida y ajustes. El costo de procesamiento variará según el tamaño, la complejidad de la red, el tipo de función, y la cantidad de datos principalmente. Para fines de comprensión, resultará más accesible trabajar con parámetros simples y posteriormente podrán realizar ajustes, usar funciones de activación mas eficientes, usar datos de entrenamiento más grandes, todo ello para obtener mayor precisión en los resultados.
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