Acerca de este Curso
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100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

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Nivel intermedio

Course 1 of the TensorFlow Specialization, Python coding, and high-school level math are required. ML/DL experience is helpful but not required.

Aprox. 7 horas para completar

Sugerido: 4 weeks of study, 4-5 hours/week...

Inglés (English)

Subtítulos: Inglés (English)

Qué aprenderás

  • Check

    Handle real-world image data

  • Check

    Plot loss and accuracy

  • Check

    Explore strategies to prevent overfitting, including augmentation and dropout

  • Check

    Learn transfer learning and how learned features can be extracted from models

Habilidades que obtendrás

Inductive TransferAugmentationDropoutsMachine LearningTensorflow

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel intermedio

Course 1 of the TensorFlow Specialization, Python coding, and high-school level math are required. ML/DL experience is helpful but not required.

Aprox. 7 horas para completar

Sugerido: 4 weeks of study, 4-5 hours/week...

Inglés (English)

Subtítulos: Inglés (English)

Los estudiantes que toman este Course son

  • Machine Learning Engineers
  • Data Scientists
  • Chief Technology Officers (CTOs)
  • Data Engineers
  • Scientists

Programa - Qué aprenderás en este curso

Semana
1
4 horas para completar

Exploring a Larger Dataset

8 videos (Total 18 minutos), 5 lecturas, 3 cuestionarios
8 videos
A conversation with Andrew Ng1m
Training with the cats vs. dogs dataset2m
Working through the notebook4m
Fixing through cropping49s
Visualizing the effect of the convolutions1m
Looking at accuracy and loss1m
Week 1 Wrap up33s
5 lecturas
Before you Begin: TensorFlow 2.0 and this Course10m
The cats vs dogs dataset10m
Looking at the notebook10m
What you'll see next10m
What have we seen so far?10m
1 ejercicio de práctica
Week 1 Quiz30m
Semana
2
4 horas para completar

Augmentation: A technique to avoid overfitting

7 videos (Total 14 minutos), 6 lecturas, 3 cuestionarios
7 videos
Introducing augmentation2m
Coding augmentation with ImageDataGenerator3m
Demonstrating overfitting in cats vs. dogs1m
Adding augmentation to cats vs. dogs1m
Exploring augmentation with horses vs. humans1m
Week 2 Wrap up37s
6 lecturas
Image Augmentation10m
Start Coding...10m
Looking at the notebook10m
The impact of augmentation on Cats vs. Dogs10m
Try it for yourself!10m
What have we seen so far?10m
1 ejercicio de práctica
Week 2 Quiz30m
Semana
3
4 horas para completar

Transfer Learning

7 videos (Total 14 minutos), 5 lecturas, 3 cuestionarios
7 videos
Understanding transfer learning: the concepts2m
Coding transfer learning from the inception mode1m
Coding your own model with transferred features2m
Exploring dropouts1m
Exploring Transfer Learning with Inception1m
Week 3 Wrap up36s
5 lecturas
Start coding!10m
Adding your DNN10m
Using dropouts!10m
Applying Transfer Learning to Cats v Dogs10m
What have we seen so far?10m
1 ejercicio de práctica
Week 3 Quiz30m
Semana
4
4 horas para completar

Multiclass Classifications

6 videos (Total 12 minutos), 5 lecturas, 3 cuestionarios
6 videos
Moving from binary to multi-class classification44s
Explore multi-class with Rock Paper Scissors dataset2m
Train a classifier with Rock Paper Scissors1m
Test the Rock Paper Scissors classifier2m
A conversation with Andrew Ng1m
5 lecturas
Introducing the Rock-Paper-Scissors dataset10m
Check out the code!10m
Try testing the classifier10m
What have we seen so far?10m
Wrap up10m
1 ejercicio de práctica
Week 4 Quiz30m
4.7
193 revisionesChevron Right

15%

consiguió un beneficio tangible en su carrera profesional gracias a este curso

11%

consiguió un aumento de sueldo o ascenso

Principales revisiones sobre Convolutional Neural Networks in TensorFlow

por JMSep 12th 2019

great introductory stuff, great way to keep in touch with tensorflow's new tools, and the instructor is absolutely phenomenal. love the enthusiasm and the interactions with andrew are a joy to watch.

por PSSep 14th 2019

An excellent course by Laurence Moroney on explaining how ConvNets are prepared using Tensorflow. A really good strategy to have the programming exercises on Google Colab to speed up the processing.

Instructor

Avatar

Laurence Moroney

AI Advocate
Google Brain

Acerca de deeplearning.ai

deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders....

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. Courses 1-3 are available now, with Course 4 launching in July....
TensorFlow in Practice

Preguntas Frecuentes

  • Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.

  • 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.

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