El curso "Deep Learning" ofrece una introducciĂłn práctica a las redes neuronales artificiales y su aplicaciĂłn en diversas áreas como el reconocimiento de dĂgitos manuscritos, detecciĂłn de cáncer y generaciĂłn de texto. Utilizando TensorFlow y Keras, los participantes aprenderán a construir y entrenar modelos de redes neuronales, incluyendo CNN y RNN, a travĂ©s de ejemplos y ejercicios prácticos.
Deep Learning: Del Concepto a la Práctica
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Deep Learning: Del Concepto a la Práctica
This course is part of Ciencia de Datos e IA : De los Fundamentos a la Práctica Specialization
Instructors: Eduardo RodrĂguez del Angel
Included with 
Gain insight into a topic and learn the fundamentals.
Beginner level
No prior experience required
88 hours to complete
3 weeks at 29 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Describir las técnicas de Deep Learning utilizando TensorFlow y Keras para resolver problemas de reconocimiento de patrones y generación de texto.
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Recently updated!
March 2025
Assessments
27 assignments
Taught in Spanish
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This course is part of the Ciencia de Datos e IA : De los Fundamentos a la Práctica Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course
En esta secciĂłn, los estudiantes serán introducidos al mundo del Deep Learning, comenzando con una explicaciĂłn de las redes neuronales artificiales. Se explorarán los componentes y la estructura de las NN, y se realizará un primer proyecto práctico de programaciĂłn para reconocer dĂgitos manuscritos.
What's included
13 videos1 reading11 assignments
Esta secciĂłn se centra en el uso de TensorFlow y Keras para la construcciĂłn y entrenamiento de redes neuronales. Los estudiantes aprenderán a implementar modelos para la detecciĂłn de dĂgitos manuscritos y el diagnĂłstico de cáncer de mama, utilizando estas poderosas herramientas de Deep Learning
What's included
10 videos2 readings6 assignments
Explorarás las redes neuronales convolucionales (CNN), una técnica avanzada para el procesamiento de imágenes. Se desarrollarán proyectos prácticos para el reconocimiento de términos manuscritos y el lenguaje de señas, aprendiendo a guardar, cargar y compartir los modelos de redes neuronales.
What's included
14 videos1 reading8 assignments
Esta secciĂłn se dedica a las redes neuronales recurrentes (RNN) y las Long Short-Term Memory (LSTM), utilizadas principalmente para la generaciĂłn de texto. Los contenidos de está secciĂłn te permitirán comprender cĂłmo crear modelos que generen texto de manera coherente, practicando con ejemplos y ejercicios especĂficos.
What's included
8 videos3 readings2 assignments
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