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.
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Programa de cursos integrados TensorFlow in Practicedeeplearning.aiInformações sobre o curso
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Resultados de carreira do aprendiz
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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.
Curso 2 de 4 no
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.
Curso 2 de 4 no
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
oferecido 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 - O que você aprenderá com este curso
7 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!
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
7 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!
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
7 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!
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
7 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!
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
Avaliações
Principais avaliações do CONVOLUTIONAL NEURAL NETWORKS IN TENSORFLOW
por JM11 de Set de 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 RB14 de Mar de 2020
Nice experience taking this course. Precise and to the point introduction of topics and a really nice head start into practical aspects of Computer Vision and using the amazing tensorflow framework..
por KA13 de Abr de 2020
Nice course. Even though I have previously done some projects using CNN and multi-class classification still this course let me to have an insight to how these APIs work. Keep Up The Good Work!!!!!!
por SS8 de Dez de 2019
Very clear explanation on the concepts at the higher level and practical application of it is discussed, demonstrated and also the exercises are of the same way. You will just love learning this way
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.
Perguntas Frequentes – FAQ
When will I have access to the lectures and assignments?
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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.
What will I get if I subscribe to this Specialization?
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.
What is the refund policy?
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.
Is financial aid available?
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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