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Nestor Nicolas Campos Rojas
En este proyecto de 1 hora, aprenderás a crear, entrenar y evaluar una red neuronal convolucional básica para predecir la categoría asociada a imágenes
Además, cómo trabajar con Pytorch de forma online, usando Google Colaboratory.
Register for this course and see more details by visiting:
OpenCourser.com/course/13aehi/disenando
What's inside
Syllabus
Diseñando tu primer modelo con Pytorch
Al final de este proyecto, tú entenderás cómo usar Pytorch con Numpy y cómo crear y entrenar una red neuronal básica.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for beginners with no prior experience in deep learning
Provides hands-on practice using Pytorch and Google Colaboratory
Can serve as a foundation for further exploration of deep learning
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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 Diseñando tu primer modelo con Pytorch with these
activities:
Sigue tutoriales guiados sobre Pytorch para reforzar tu comprensión de las CNN
Show steps
Los tutoriales guiados proporcionarán instrucciones paso a paso para implementar CNN utilizando Pytorch, lo que te permitirá reforzar tu comprensión práctica.
Browse courses on
PyTorch
Show steps
-
Busca tutoriales en línea o recursos de la comunidad que cubran la implementación de CNN en Pytorch
-
Sigue los tutoriales cuidadosamente, implementando los conceptos en tu propio código
-
Experimenta con diferentes parámetros y conjuntos de datos para obtener una comprensión más profunda de las CNN
Realiza ejercicios prácticos de redes neuronales convolucionales (CNN) utilizando Pytorch
Show steps
Los ejercicios prácticos te permitirán fortalecer tu comprensión de las redes neuronales convolucionales y cómo utilizar Pytorch para implementarlas.
Browse courses on
CNN
Show steps
-
Revisa los conceptos básicos de las CNN y Pytorch
-
Implementa una CNN básica para predecir la categoría de imágenes
-
Entrena y evalúa la CNN utilizando un conjunto de datos de imágenes
Crea una presentación sobre los principios de las redes neuronales convolucionales
Show steps
Crear una presentación te ayudará a organizar tus conocimientos sobre las CNN y a comunicar sus principios de manera efectiva.
Browse courses on
CNN
Show steps
-
Investiga los principios fundamentales de las CNN
-
Recopila ejemplos de aplicaciones del mundo real de las CNN
-
Diseña una presentación clara y concisa que cubra los principios clave de las CNN
-
Presenta tu presentación a tus compañeros o a un público objetivo
Show all three activities
Sigue tutoriales guiados sobre Pytorch para reforzar tu comprensión de las CNN
Show steps
Los tutoriales guiados proporcionarán instrucciones paso a paso para implementar CNN utilizando Pytorch, lo que te permitirá reforzar tu comprensión práctica.
Browse courses on
PyTorch
Show steps
Show steps
Show steps
- Busca tutoriales en línea o recursos de la comunidad que cubran la implementación de CNN en Pytorch
- Sigue los tutoriales cuidadosamente, implementando los conceptos en tu propio código
- Experimenta con diferentes parámetros y conjuntos de datos para obtener una comprensión más profunda de las CNN
Realiza ejercicios prácticos de redes neuronales convolucionales (CNN) utilizando Pytorch
Show steps
Los ejercicios prácticos te permitirán fortalecer tu comprensión de las redes neuronales convolucionales y cómo utilizar Pytorch para implementarlas.
Browse courses on
CNN
Show steps
Show steps
Show steps
- Revisa los conceptos básicos de las CNN y Pytorch
- Implementa una CNN básica para predecir la categoría de imágenes
- Entrena y evalúa la CNN utilizando un conjunto de datos de imágenes
Crea una presentación sobre los principios de las redes neuronales convolucionales
Show steps
Crear una presentación te ayudará a organizar tus conocimientos sobre las CNN y a comunicar sus principios de manera efectiva.
Browse courses on
CNN
Show steps
Show steps
Show steps
- Investiga los principios fundamentales de las CNN
- Recopila ejemplos de aplicaciones del mundo real de las CNN
- Diseña una presentación clara y concisa que cubra los principios clave de las CNN
- Presenta tu presentación a tus compañeros o a un público objetivo
Show all three activities
Career center
Learners who complete Diseñando tu primer modelo con Pytorch will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. They work with data scientists to identify the best models for solving specific problems. This course can help you develop the skills you need to become a Machine Learning Engineer by teaching you how to use Pytorch to create and train machine learning models. With these skills, you will be able to design and develop models that can solve real-world problems.
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data to identify trends and patterns. They use their findings to make recommendations to businesses on how to improve their operations. This course can help you develop the skills you need to become a Data Analyst by teaching you how to use Pytorch to create and train machine learning models. With these skills, you will be able to analyze data and identify trends that can help businesses make better decisions.
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, cleaning, and analyzing data to identify trends and patterns. They use their findings to make recommendations to businesses on how to improve their operations. This course can help you develop the skills you need to become a Data Scientist by teaching you how to use Pytorch to create and train machine learning models. With these skills, you will be able to analyze data and identify trends that can help businesses make better decisions.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts are responsible for developing and using mathematical models to solve business problems. They work with businesses to identify and solve problems such as how to optimize production schedules, how to allocate resources, and how to improve customer service. This course can help you develop the skills you need to become an Operations Research Analyst by teaching you how to use Pytorch to create and train machine learning models. With these skills, you will be able to develop models that can solve business problems.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and testing software applications. They work with teams of engineers to create software that meets the needs of users. This course can help you develop the skills you need to become a Software Engineer by teaching you how to use Pytorch to create and train machine learning models. With these skills, you will be able to develop software that can solve complex problems.
Financial Analyst
Financial Analyst
Financial Analysts are responsible for analyzing financial data to make recommendations to investors. They use their knowledge of finance and economics to develop models that can predict the performance of stocks, bonds, and other financial instruments. This course can help you develop the skills you need to become a Financial Analyst by teaching you how to use Pytorch to create and train machine learning models. With these skills, you will be able to develop models that can analyze financial data and make recommendations to investors.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts are responsible for developing and using mathematical models to analyze financial data. They use their findings to make recommendations to investors on how to invest their money. This course can help you develop the skills you need to become a Quantitative Analyst by teaching you how to use Pytorch to create and train machine learning models. With these skills, you will be able to develop models that can analyze financial data and make recommendations to investors.
Business Analyst
Business Analyst
Business Analysts are responsible for analyzing business needs and developing solutions to meet those needs. They work with businesses to identify and solve problems such as how to improve customer service, how to increase sales, and how to reduce costs. This course can help you develop the skills you need to become a Business Analyst by teaching you how to use Pytorch to create and train machine learning models. With these skills, you will be able to develop models that can analyze business data and make recommendations to businesses.
Compliance Officer
Compliance Officer
Compliance Officers are responsible for ensuring that businesses comply with laws and regulations. They work with businesses to identify and mitigate risks such as legal risks, regulatory risks, and ethical risks. This course can help you develop the skills you need to become a Compliance Officer by teaching you how to use Pytorch to create and train machine learning models. With these skills, you will be able to develop models that can identify and mitigate compliance risks.
Product Manager
Product Manager
Product Managers are responsible for developing and managing products. They work with teams of engineers, designers, and marketers to create products that meet the needs of users. This course can help you develop the skills you need to become a Product Manager by teaching you how to use Pytorch to create and train machine learning models. With these skills, you will be able to develop products that are tailored to the needs of users.
Actuary
Actuary
Actuaries are responsible for assessing and managing risk. They use their knowledge of mathematics and statistics to develop models that can predict the likelihood of future events. This course can help you develop the skills you need to become an Actuary by teaching you how to use Pytorch to create and train machine learning models. With these skills, you will be able to develop models that can assess and manage risk.
Risk Manager
Risk Manager
Risk Managers are responsible for identifying and managing risks. They work with businesses to identify and mitigate risks such as financial risks, operational risks, and reputational risks. This course can help you develop the skills you need to become a Risk Manager by teaching you how to use Pytorch to create and train machine learning models. With these skills, you will be able to develop models that can identify and manage risks.
Auditor
Auditor
Auditors are responsible for examining and evaluating financial records to ensure that they are accurate and complete. They work with businesses to identify and mitigate risks such as financial risks, operational risks, and reputational risks. This course can help you develop the skills you need to become an Auditor by teaching you how to use Pytorch to create and train machine learning models. With these skills, you will be able to develop models that can examine and evaluate financial records.
Forensic Accountant
Forensic Accountant
Forensic Accountants are responsible for investigating financial crimes such as fraud, embezzlement, and money laundering. They work with law enforcement and businesses to identify and mitigate financial crimes. This course can help you develop the skills you need to become a Forensic Accountant by teaching you how to use Pytorch to create and train machine learning models. With these skills, you will be able to develop models that can identify and investigate financial crimes.
Project Manager
Project Manager
Project Managers are responsible for planning, executing, and closing projects. They work with teams of people to achieve project goals. This course can help you develop the skills you need to become a Project Manager by teaching you how to use Pytorch to create and train machine learning models. With these skills, you will be able to develop models that can help you plan, execute, and close projects.
For more career information including salaries, visit:
OpenCourser.com/course/13aehi/disenando
Reading list
We've selected seven 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
Diseñando tu primer modelo con Pytorch.
Este libro ofrece una guía práctica para el aprendizaje profundo con PyTorch, cubriendo una amplia gama de temas, desde modelos básicos hasta redes neuronales avanzadas.
Esta guía práctica proporciona una introducción completa al uso de PyTorch para el aprendizaje profundo. Cubre los conceptos básicos, como tensores, redes neuronales y optimización, y proporciona ejemplos prácticos para ayudarte a comenzar.
Proporciona una introducción práctica al aprendizaje profundo con Python. Es útil para ampliar la comprensión de los conceptos de redes neuronales y su aplicación en el aprendizaje automático.
Este libro proporciona una introducción práctica a PyTorch, cubriendo los conceptos básicos del aprendizaje profundo y el uso de PyTorch para crear y entrenar modelos de aprendizaje profundo.
Este libro proporciona una introducción general al aprendizaje profundo, que no se centra específicamente en PyTorch. Sin embargo, ofrece una valiosa base conceptual para los estudiantes que luego desean explorar PyTorch.
Save
Ofrece una cobertura más completa de los algoritmos de visión por computadora, incluyendo redes neuronales convolucionales. Es útil como referencia y para ampliar la comprensión de las técnicas avanzadas de visión por computadora.
Save
Es un libro de texto completo que cubre una amplia gama de temas de aprendizaje profundo. Es útil como referencia y para obtener una comprensión más completa del campo.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/13aehi/disenando
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Effort
1 hora
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Nestor Nicolas Campos Rojas
Language
Español
Good to know
Suitable for beginners with no prior experience in deep learning
Provides hands-on practice using Pytorch and Google Colaboratory
Can serve as a foundation for further exploration of deep learning
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