Informações sobre o curso

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Resultados de carreira do aprendiz

13%

consegui um benefício significativo de carreira com este curso

10%

recebi um aumento ou promoção
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário

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

Aprox. 26 horas para completar
Inglês
Legendas: Inglês, Russo, Japonês

O que você vai aprender

  • Handle real-world image data

  • Plot loss and accuracy

  • Explore strategies to prevent overfitting, including augmentation and dropout

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

Habilidades que você terá

Inductive TransferAugmentationDropoutsMachine LearningTensorflow

Resultados de carreira do aprendiz

13%

consegui um benefício significativo de carreira com este curso

10%

recebi um aumento ou promoção
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário

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

Aprox. 26 horas para completar
Inglês
Legendas: Inglês, Russo, Japonês

Instrutores

oferecido por

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deeplearning.ai

Programa - O que você aprenderá com este curso

Classificação do conteúdoThumbs Up97%(5,496 classificações)Info
Semana
1

Semana 1

7 horas para concluir

Exploring a Larger Dataset

7 horas para concluir
8 vídeos (Total 18 mín.), 5 leituras, 3 testes
8 videos
A conversation with Andrew Ng1min
Training with the cats vs. dogs dataset2min
Working through the notebook4min
Fixing through cropping49s
Visualizing the effect of the convolutions1min
Looking at accuracy and loss1min
Week 1 Wrap up33s
5 leituras
Before you Begin: TensorFlow 2.0 and this Course10min
The cats vs dogs dataset10min
Looking at the notebook10min
What you'll see next10min
What have we seen so far?10min
1 exercício prático
Week 1 Quiz30min
Semana
2

Semana 2

7 horas para concluir

Augmentation: A technique to avoid overfitting

7 horas para concluir
7 vídeos (Total 14 mín.), 6 leituras, 3 testes
7 videos
Introducing augmentation2min
Coding augmentation with ImageDataGenerator3min
Demonstrating overfitting in cats vs. dogs1min
Adding augmentation to cats vs. dogs1min
Exploring augmentation with horses vs. humans1min
Week 2 Wrap up37s
6 leituras
Image Augmentation10min
Start Coding...10min
Looking at the notebook10min
The impact of augmentation on Cats vs. Dogs10min
Try it for yourself!10min
What have we seen so far?10min
1 exercício prático
Week 2 Quiz30min
Semana
3

Semana 3

7 horas para concluir

Transfer Learning

7 horas para concluir
7 vídeos (Total 14 mín.), 5 leituras, 3 testes
7 videos
Understanding transfer learning: the concepts2min
Coding transfer learning from the inception mode1min
Coding your own model with transferred features2min
Exploring dropouts1min
Exploring Transfer Learning with Inception1min
Week 3 Wrap up36s
5 leituras
Start coding!10min
Adding your DNN10min
Using dropouts!10min
Applying Transfer Learning to Cats v Dogs10min
What have we seen so far?10min
1 exercício prático
Week 3 Quiz30min
Semana
4

Semana 4

7 horas para concluir

Multiclass Classifications

7 horas para concluir
6 vídeos (Total 12 mín.), 5 leituras, 3 testes
6 videos
Moving from binary to multi-class classification44s
Explore multi-class with Rock Paper Scissors dataset2min
Train a classifier with Rock Paper Scissors1min
Test the Rock Paper Scissors classifier2min
A conversation with Andrew Ng1min
5 leituras
Introducing the Rock-Paper-Scissors dataset10min
Check out the code!10min
Try testing the classifier10min
What have we seen so far?10min
Wrap up10min
1 exercício prático
Week 4 Quiz30min

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Sobre Programa de cursos integrados 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....
TensorFlow in Practice

Perguntas Frequentes – FAQ

  • Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

    • The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.

    • The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

  • Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.

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