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Este proyecto es un curso práctico y efectivo para aprender a generar redes neuronales convolucionales con Python y Keras. Aprenderás desde cero los fundamentos del Deep Learning y a como crear este tipo de redes.

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Este proyecto es un curso práctico y efectivo para aprender a generar redes neuronales convolucionales con Python y Keras. Aprenderás desde cero los fundamentos del Deep Learning y a como crear este tipo de redes.

Gracias a este curso aprenderás a programar tus propias redes convolucionales capaces de clasificar objetos de imágenes como el tipo de ropa o el número de la imagen.

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What's inside

Syllabus

Inteligencia Artificial en Power BI
En este curso se aprenderá a programar redes neuronales con Keras

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Explores Convolutional Neural Networks (CNNs), a fundamental concept in computer vision
Leverages the Python programming language and Keras, an open-source deep learning library, for practical implementation
Suitable for individuals seeking to enhance their understanding of deep learning and its applications in image recognition
Provides hands-on experience in building and training CNNs for image classification tasks

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Redes neuronales convolucionales con Keras with these activities:
Revisa los fundamentos del Deep Learning
Revisar los fundamentos del Deep Learning te proporcionará una base sólida para comprender las redes neuronales convolucionales y sus aplicaciones.
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  • Revisa los conceptos básicos del aprendizaje automático y las redes neuronales
  • Familiarízate con las diferentes arquitecturas de redes neuronales
  • Practica el entrenamiento y la evaluación de redes neuronales sencillas
Sigue tutoriales para implementar redes neuronales convolucionales
Seguir tutoriales te proporcionará instrucciones paso a paso y ejemplos prácticos para ayudarte a comprender e implementar redes convolucionales.
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  • Busca tutoriales de redes neuronales convolucionales con Python y Keras
  • Sigue los tutoriales paso a paso
  • Mejora los tutoriales con tus propios experimentos y modificaciones
Programa redes convolucionales en Python con Keras
Practicar la programación de redes convolucionales reforzará tu comprensión de los conceptos y te permitirá aplicarlos con mayor eficacia en tus proyectos.
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  • Crea un entorno de desarrollo con Python y Keras
  • Importa los datos de imágenes y etiquétalos
  • Diseña y programa una red convolucional
  • Entrena la red convolucional con los datos etiquetados
  • Evalúa el rendimiento de la red convolucional
Two other activities
Expand to see all activities and additional details
Show all five activities
Crea un proyecto de reconocimiento de imágenes con una red convolucional
Crear un proyecto te permitirá aplicar todo lo aprendido en el curso de forma práctica y te ayudará a comprender mejor las aplicaciones del mundo real de las redes convolucionales.
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  • Define el problema de reconocimiento de imágenes que quieres resolver
  • Recopila y etiqueta un conjunto de datos de imágenes
  • Diseña y entrena una red convolucional para el problema específico
  • Implementa y evalúa el rendimiento de la red convolucional
Participa en concursos o hackatones sobre redes neuronales convolucionales
Participar en concursos o hackatones te permitirá poner a prueba tus habilidades, aprender de otros y mantenerte al día con las últimas tendencias en el campo de las redes neuronales convolucionales.
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  • Busca concursos o hackatones sobre redes neuronales convolucionales
  • Forma un equipo o participa individualmente
  • Diseña y implementa una solución utilizando redes neuronales convolucionales
  • Envía tu solución y compite contra otros participantes
  • Analiza los resultados y aprende de la experiencia

Career center

Learners who complete Redes neuronales convolucionales con Keras will develop knowledge and skills that may be useful to these careers:
Data Scientist
Data Scientists use Deep Learning and Python to build models that help businesses make better decisions and predictions; they analyze and interpret large amounts of data to identify patterns and trends, which enables them to make recommendations for marketing campaigns, product development, or financial decisions, among others. This course on Convolutional Neural Networks with Python and Keras would help build the foundation of Deep Learning necessary for success in this career. By learning how to create Convolutional Neural Networks, learners may become more competitive in this career field.
Deep Learning Engineer
Deep Learning Engineers work on developing and maintaining Deep Learning models for various applications, such as image and speech recognition, natural language processing, and robotics. This course on Convolutional Neural Networks with Python and Keras may be useful for these engineers as it teaches the fundamentals of Deep Learning, as well as how to create Convolutional Neural Networks.
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning systems. They work to create and deploy algorithms that can learn from data and make predictions, often using Python and Deep Learning. This course on Convolutional Neural Networks with Python and Keras may be useful for Machine Learning Engineers who want to learn more about Convolutional Neural Networks and how to create their own.
Software Engineer
Software Engineers with expertise in Deep Learning may be involved in developing and maintaining software applications that use Convolutional Neural Networks. This course on Convolutional Neural Networks with Python and Keras may be useful for such engineers as it helps build a foundation in Deep Learning, as well as teaching how to create Convolutional Neural Networks.
Data Analyst
Data Analysts use various techniques to analyze and interpret data; Deep Learning is one such technique. This course on Convolutional Neural Networks with Python and Keras may be useful for Data Analysts who want to expand their skill set and learn more about Convolutional Neural Networks and how to create their own.
Artificial Intelligence Engineer
Artificial Intelligence Engineers with a specialization in Deep Learning may work on projects involving Convolutional Neural Networks. This course on Convolutional Neural Networks with Python and Keras may be useful for such engineers as it helps build a foundation in Deep Learning, as well as teaching how to create Convolutional Neural Networks.
Computer Vision Engineer
Computer Vision Engineers work on developing and maintaining computer vision systems, which often involve Convolutional Neural Networks. This course on Convolutional Neural Networks with Python and Keras may be useful for these engineers as it helps build a foundation in Deep Learning, as well as teaching how to create Convolutional Neural Networks.
Research Scientist
Research Scientists who focus on Artificial Intelligence and Machine Learning may find this course on Convolutional Neural Networks with Python and Keras useful, as it helps build a foundation in Deep Learning, as well as teaching how to create Convolutional Neural Networks.
Product Manager
Product Managers who work on products that involve Artificial Intelligence and Machine Learning may find this course on Convolutional Neural Networks with Python and Keras useful, as it helps build a foundation in Deep Learning, as well as teaching how to create Convolutional Neural Networks, which may help them better understand the technical aspects of the products they are managing.
Business Analyst
Business Analysts who work in the Artificial Intelligence and Machine Learning industry may find this course on Convolutional Neural Networks with Python and Keras useful, as it helps build a foundation in Deep Learning, as well as teaching how to create Convolutional Neural Networks, which may help them better understand the technical aspects of the projects they are working on.
Technical Writer
Technical Writers who specialize in Artificial Intelligence and Machine Learning may find this course on Convolutional Neural Networks with Python and Keras useful, as it helps build a foundation in Deep Learning, as well as teaching how to create Convolutional Neural Networks, which may help them better understand the technical aspects of the products they are writing about.
Sales Engineer
Sales Engineers who work in the Artificial Intelligence and Machine Learning industry may find this course on Convolutional Neural Networks with Python and Keras useful, as it helps build a foundation in Deep Learning, as well as teaching how to create Convolutional Neural Networks, which may help them better understand the technical aspects of the products they are selling.
Data Engineer
Data Engineers who work on projects involving Artificial Intelligence and Machine Learning may find this course on Convolutional Neural Networks with Python and Keras useful, as it helps build a foundation in Deep Learning, as well as teaching how to create Convolutional Neural Networks.
Quantitative Analyst
Quantitative Analysts who work on projects involving Artificial Intelligence and Machine Learning may find this course on Convolutional Neural Networks with Python and Keras useful, as it helps build a foundation in Deep Learning, as well as teaching how to create Convolutional Neural Networks.
IT Consultant
IT Consultants who work with clients in the Artificial Intelligence and Machine Learning industry may find this course on Convolutional Neural Networks with Python and Keras useful, as it helps build a foundation in Deep Learning, as well as teaching how to create Convolutional Neural Networks, which may help them better understand the technical needs of their clients.

Reading list

We've selected 12 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Redes neuronales convolucionales con Keras.
Este libro es un texto completo sobre el aprendizaje profundo. Cubre todos los aspectos del aprendizaje profundo, desde los fundamentos hasta los temas más avanzados.
Este libro proporciona una introducción completa al aprendizaje profundo con Python y Keras. Cubre los fundamentos del aprendizaje profundo, así como temas avanzados como redes neuronales convolucionales y redes neuronales recurrentes.
Este libro es un texto completo sobre reconocimiento de patrones y aprendizaje automático. Cubre todos los aspectos del reconocimiento de patrones y el aprendizaje automático, desde los fundamentos hasta los temas más avanzados.
Este libro proporciona una introducción al aprendizaje automático desde una perspectiva bayesiana y de optimización. Cubre los fundamentos del aprendizaje automático, así como temas más avanzados como redes neuronales convolucionales y redes neuronales recurrentes.
Este libro proporciona una introducción práctica al aprendizaje automático con TensorFlow. Cubre una amplia gama de temas, desde los fundamentos del aprendizaje automático hasta temas avanzados como el aprendizaje profundo.
Este libro proporciona una guía práctica para el aprendizaje automático con Python. Cubre una amplia gama de temas, desde la preparación de datos hasta la implementación de modelos de aprendizaje automático.
Este libro proporciona una guía práctica para el aprendizaje automático. Cubre una amplia gama de temas, desde la preparación de datos hasta la implementación de modelos de aprendizaje automático.
Este libro proporciona una introducción práctica al aprendizaje automático para hackers. Cubre una amplia gama de temas, desde la preparación de datos hasta la implementación de modelos de aprendizaje automático.
Este libro proporciona una guía práctica para el aprendizaje automático con Scikit-Learn, Keras y TensorFlow. Cubre una amplia gama de temas, desde la preparación de datos hasta la implementación de modelos de aprendizaje automático.
Este libro proporciona una introducción al aprendizaje automático para principiantes. Cubre los fundamentos del aprendizaje automático, así como temas más avanzados como redes neuronales convolucionales y redes neuronales recurrentes.

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