If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.
Este curso forma parte de Programa Especializado - TensorFlow in Practice
ofrecido por
Programa Especializado - TensorFlow in Practicedeeplearning.aiAcerca de este Curso
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Resultados profesionales del estudiante
40%
comenzó una nueva carrera después de completar estos cursos
43%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
12%
consiguió un aumento de sueldo o ascenso
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 1 de 4 en
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel intermedio
Experience in Python coding and high school-level math is required. Prior machine learning or deep learning knowledge is helpful but not required.
Aprox. 30 horas para completar
Inglés (English)
Subtítulos: Inglés (English), Español (Spanish), Ruso (Russian), Japonés
Qué aprenderás
Learn best practices for using TensorFlow, a popular open-source machine learning framework
Build a basic neural network in TensorFlow
Train a neural network for a computer vision application
Understand how to use convolutions to improve your neural network
Habilidades que obtendrás
Computer VisionTensorflowMachine Learning
Resultados profesionales del estudiante
40%
comenzó una nueva carrera después de completar estos cursos
43%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
12%
consiguió un aumento de sueldo o ascenso
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 1 de 4 en
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel intermedio
Experience in Python coding and high school-level math is required. Prior machine learning or deep learning knowledge is helpful but not required.
Aprox. 30 horas para completar
Inglés (English)
Subtítulos: Inglés (English), Español (Spanish), Ruso (Russian), Japonés
ofrecido por
deeplearning.ai
Founded by Andrew Ng, deeplearning.ai is on a mission to make world-class AI education accessible to people around the globe so that we can all benefit from an AI-powered future.
Programa - Qué aprenderás en este curso
6 horas para completar
A New Programming Paradigm
Welcome to this course on going from Basics to Mastery of TensorFlow. We're excited you're here! In week 1 you'll get a soft introduction to what Machine Learning and Deep Learning are, and how they offer you a new programming paradigm, giving you a new set of tools to open previously unexplored scenarios. All you need to know is some very basic programming skills, and you'll pick the rest up as you go along.
6 horas para completar
4 videos (Total 16 minutos), 5 lecturas, 3 cuestionarios
4 videos
A primer in machine learning3m
The ‘Hello World’ of neural networks5m
Working through ‘Hello World’ in TensorFlow and Python3m
5 lecturas
Before you begin: TensorFlow 2.0 and this course10m
From rules to data10m
Try it for yourself10m
Introduction to Google Colaboratory10m
Week 1 Resources10m
1 ejercicio de práctica
Week 1 Quiz
7 horas para completar
Introduction to Computer Vision
Welcome to week 2 of the course! In week 1 you learned all about how Machine Learning and Deep Learning is a new programming paradigm. This week you’re going to take that to the next level by beginning to solve problems of computer vision with just a few lines of code!
7 horas para completar
7 videos (Total 15 minutos), 6 lecturas, 3 cuestionarios
7 videos
An Introduction to computer vision2m
Writing code to load training data2m
Coding a Computer Vision Neural Network2m
Walk through a Notebook for computer vision3m
Using Callbacks to control training1m
Walk through a notebook with Callbacks1m
6 lecturas
Exploring how to use data10m
The structure of Fashion MNIST data10m
See how it's done10m
Get hands-on with computer vision1h
See how to implement Callbacks10m
Week 2 Resources10m
1 ejercicio de práctica
Week 2 Quiz
8 horas para completar
Enhancing Vision with Convolutional Neural Networks
Welcome to week 3! In week 2 you saw a basic Neural Network for Computer Vision. It did the job nicely, but it was a little naive in its approach. This week we’ll see how to make it better, as discussed by Laurence and Andrew here.
8 horas para completar
6 videos (Total 19 minutos), 6 lecturas, 3 cuestionarios
6 videos
What are convolutions and pooling?2m
Implementing convolutional layers1m
Implementing pooling layers4m
Improving the Fashion classifier with convolutions4m
Walking through convolutions3m
6 lecturas
Coding convolutions and pooling layers10m
Learn more about convolutions10m
Getting hands-on, your first ConvNet10m
Try it for yourself1h
Experiment with filters and pools1h
Week 3 Resources10m
1 ejercicio de práctica
Week 3 Quiz
9 horas para completar
Using Real-world Images
Last week you saw how to improve the results from your deep neural network using convolutions. It was a good start, but the data you used was very basic. What happens when your images are larger, or if the features aren’t always in the same place? Andrew and Laurence discuss this to prepare you for what you’ll learn this week: handling complex images!
9 horas para completar
9 videos (Total 27 minutos), 10 lecturas, 3 cuestionarios
9 videos
Understanding ImageGenerator4m
Defining a ConvNet to use complex images2m
Training the ConvNet with fit_generator2m
Walking through developing a ConvNet2m
Walking through training the ConvNet with fit_generator3m
Adding automatic validation to test accuracy4m
Exploring the impact of compressing images3m
A conversation with Andrew1m
10 lecturas
Explore an impactful, real-world solution10m
Designing the neural network10m
Train the ConvNet with ImageGenerator10m
Exploring the solution10m
Training the neural network10m
Experiment with the horse or human classifier1h
Get hands-on and use validation30m
Get Hands-on with compacted images30m
Week 4 Resources10m
Wrap up10m
1 ejercicio de práctica
Week 4 Quiz
Revisiones
Principales revisiones sobre INTRODUCTION TO TENSORFLOW FOR ARTIFICIAL INTELLIGENCE, MACHINE LEARNING, AND DEEP LEARNING
por AS8 de mar. de 2019
Good intro course, but google colab assignments need to be improved. And submitting a jupyter notebook was much more easier, why would I want to login to my google account to be a part of this course?
por JJ27 de abr. de 2019
Very well organized. Good speakers. Content is comprehensive for a Introductory Course. A little more explanation on Validation versus Testing and on some of the evaluation functions would be helpful.
por RD13 de ago. de 2019
Great course to get started with building Convolutional Neural Networks in Keras for building Image Classifiers. This is probably the best way to get beginners into Deep Learning for Computer Vision.
por MG26 de mar. de 2020
this course was helpful for me to learn more about CNN. it starts from basic concepts and improve that slowly and step by step. i truthfully recommend how what to start working with neural networks.
Acerca de Programa especializado: TensorFlow in Practice
Discover the tools software developers use to build scalable AI-powered algorithms in TensorFlow, a popular open-source machine learning framework.
In this four-course Specialization, you’ll explore exciting opportunities for AI applications. Begin by developing an understanding of how to build and train neural networks. Improve a network’s performance using convolutions as you train it to identify real-world images. You’ll teach machines to understand, analyze, and respond to human speech with natural language processing systems. Learn to process text, represent sentences as vectors, and input data to a neural network. You’ll even train an AI to create original poetry!
AI is already transforming industries across the world. After finishing this Specialization, you’ll be able to apply your new TensorFlow skills to a wide range of problems and projects.
Looking for more advanced TensorFlow content? Check out the new TensorFlow: Data and Deployment Specialization.
Preguntas Frecuentes
¿Cuándo podré acceder a las lecciones y tareas?
El acceso a las clases y las asignaciones depende del tipo de inscripción que tengas. Si tomas un curso en modo de oyente, verás la mayoría de los materiales del curso en forma gratuita. Para acceder a asignaciones calificadas y obtener un certificado, deberás comprar la experiencia de Certificado, ya sea durante o después de participar como oyente. Si no ves la opción de oyente:
- es posible que el curso no ofrezca la opción de participar como oyente. En cambio, puedes intentar con una Prueba gratis o postularte para recibir ayuda económica.
- Es posible que el curso ofrezca la opción 'Curso completo, sin certificado'. Esta opción te permite ver todos los materiales del curso, enviar las evaluaciones requeridas y obtener una calificación final. También significa que no podrás comprar una experiencia de Certificado.
¿Qué recibiré si me suscribo a este Programa especializado?
Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.
¿Cuál es la política de reembolsos?
Si estás suscrito, obtienes una prueba gratis de 7 días, que podrás cancelar cuando desees sin ningún tipo de penalidad. Una vez transcurrido ese tiempo, no realizamos reembolsos. No obstante, puedes cancelar tu suscripción cuando quieras. Consulta nuestra política completa de reembolsos.
¿Hay ayuda económica disponible?
Sí, Coursera ofrece ayuda económica a los estudiantes que no pueden pagar la tarifa. Solicítala haciendo clic en el enlace de Ayuda económica que está debajo del botón “Inscribirse” a la izquierda. Se te pedirá que completes una solicitud. Recibirás una notificación en caso de que se apruebe. Deberás completar este paso para cada uno de los cursos que forman parte del Programa especializado, incluido el proyecto final. Obtén más información.
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