Informações sobre o curso
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Comece imediatamente e aprenda em seu próprio cronograma.

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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. 7 horas para completar

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

Inglês

Legendas: Inglês

O que você vai aprender

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    Handle real-world image data

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    Plot loss and accuracy

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    Explore strategies to prevent overfitting, including augmentation and dropout

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

Habilidades que você terá

Inductive TransferAugmentationDropoutsMachine LearningTensorflow

100% online

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. 7 horas para completar

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

Inglês

Legendas: Inglês

Programa - O que você aprenderá com este curso

Semana
1
4 horas para concluir

Exploring a Larger Dataset

In the first course in this specialization, you had an introduction to TensorFlow, and how, with its high level APIs you could do basic image classification, an you learned a little bit about Convolutional Neural Networks (ConvNets). In this course you'll go deeper into using ConvNets will real-world data, and learn about techniques that you can use to improve your ConvNet performance, particularly when doing image classification! In Week 1, this week, you'll get started by looking at a much larger dataset than you've been using thus far: The Cats and Dogs dataset which had been a Kaggle Challenge in image classification!

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8 vídeos ((Total 18 mín.)), 5 leituras, 3 testes
8 videos
Working through the notebook4min
Fixing through cropping49s
Visualizing the effect of the convolutions1min
Looking at accuracy and loss1min
Week 1 Outro33s
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
4 horas para concluir

Augmentation: A technique to avoid overfitting

You've heard the term overfitting a number of times to this point. Overfitting is simply the concept of being over specialized in training -- namely that your model is very good at classifying what it is trained for, but not so good at classifying things that it hasn't seen. In order to generalize your model more effectively, you will of course need a greater breadth of samples to train it on. That's not always possible, but a nice potential shortcut to this is Image Augmentation, where you tweak the training set to potentially increase the diversity of subjects it covers. You'll learn all about that this week!

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7 vídeos ((Total 14 mín.)), 6 leituras, 3 testes
7 videos
Demonstrating overfitting in cats vs. dogs1min
Adding augmentation to cats vs. dogs1min
Exploring augmentation with horses vs. humans1min
Week 2 Outro37s
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
4 horas para concluir

Transfer Learning

Building models for yourself is great, and can be very powerful. But, as you've seen, you can be limited by the data you have on hand. Not everybody has access to massive datasets or the compute power that's needed to train them effectively. Transfer learning can help solve this -- where people with models trained on large datasets train them, so that you can either use them directly, or, you can use the features that they have learned and apply them to your scenario. This is Transfer learning, and you'll look into that this week!

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7 vídeos ((Total 14 mín.)), 5 leituras, 3 testes
7 videos
Coding your own model with transferred features2min
Exploring dropouts1min
Exploring Transfer Learning with Inception1min
Week 3 Outro36s
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
4 horas para concluir

Multiclass Classifications

You've come a long way, Congratulations! One more thing to do before we move off of ConvNets to the next module, and that's to go beyond binary classification. Each of the examples you've done so far involved classifying one thing or another -- horse or human, cat or dog. When moving beyond binary into Categorical classification there are some coding considerations you need to take into account. You'll look at them this week!

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6 vídeos ((Total 12 mín.)), 5 leituras, 3 testes
6 videos
Train a classifier with Rock Paper Scissors1min
Test the Rock Paper Scissors classifier2min
Outro, 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
Outro10min
1 exercício prático
Week 4 Quiz30min
4.8
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11%

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Principais avaliações do Convolutional Neural Networks in TensorFlow

por MHMay 24th 2019

A very comprehensive and easy to learn course on Tensor Flow. I am really impressed by the Instructor ability to teach difficult concept with ease. I will look forward another course of this series.

por CMMay 1st 2019

A patient and coherent introduction. At the end, you have good working code you can use elsewhere. Remarkably, the primary lecturer, Laurence Moroney, responds fairly quickly to posts in the forum.

Instrutores

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Laurence Moroney

AI Advocate
Google Brain

Sobre deeplearning.ai

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

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

Perguntas Frequentes – FAQ

  • Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.

  • Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.

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