Bringing a machine learning model into the real world involves a lot more than just modeling. This Specialization will teach you how to navigate various deployment scenarios and use data more effectively to train your model.
Este curso faz parte do Programa de cursos integrados TensorFlow: Data and Deployment
Data Pipelines with TensorFlow Data Services
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Informações sobre o curso
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O que você vai aprender
Leverage built-in datasets with just a few lines of code
Use APIs to control how you split your data
Process all types of unstructured data
Habilidades que você terá
TensorflowMachine Learning
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
We recommend taking Course 1 of the TensorFlow in Practice Specialization first, or have a basic familiarity with building models in TensorFlow
Aprox. 13 horas para completar
Sugerido: 4 weeks of study, 4-5 hours/week
Inglês
Legendas: Inglês
Programa - O que você aprenderá com este curso
5 horas para concluir
Data Pipelines with TensorFlow Data Services
You'll learn about the types of data that you would normally come across when doing machine learning.
5 horas para concluir
14 vídeos (Total 27 mín.), 2 leituras, 2 testes
14 videos
Introduction1min
Popular datasets2min
Data pipelines58s
Extract, transform, load3min
Versioning datasets2min
Looking at the notebook1min
Introduction43s
Legacy API and Subsplits5min
Splits API (S3)2min
Introduction22s
Legacy API in code1min
Splits API (S3) in code1min
Week 1 wrap up43s
2 leituras
Downloading the Coding Examples and Exercises10min
Try out the notebook yourself10min
1 exercício prático
Week 1 Quiz
6 horas para concluir
Exporting your data into the training pipeline
This week you’re going to start looking at the code for using the data with input pipelines!
6 horas para concluir
21 vídeos (Total 44 mín.), 5 leituras, 2 testes
21 videos
Introduction22s
Input data1min
Basic mechanics2min
Numeric and bucketized columns2min
Vocabulary and hashed columns, feature crossing2min
Embedding columns2min
Introduction24s
Notebook walkthrough4min
Introduction19s
Numpy, Pandas and Images2min
CSV3min
Text and TFRecord1min
Generators1min
Introduction17s
Notebook walkthrough4min
Introduction1min
Numpy and Pandas2min
Images1min
CSV4min
Text2min
5 leituras
Link to the notebook10min
Link to the CNN course10min
Link to the notebook10min
CSV: colab10min
Link to the tokenization10min
1 exercício prático
Week 2 Quiz
4 horas para concluir
Performance
How you load your data into your model can have a huge impact on how efficiently the model trains. You'll learn how to handle your data input to avoid bottlenecks, race conditions and more!
4 horas para concluir
11 vídeos (Total 20 mín.)
11 videos
Introduction36s
ETL2min
What happens when you train a model2min
Introduction25s
Caching58s
Parallelism APIs2min
Autotuning2min
Parallelizing data extraction2min
Best practices for code improvements3min
A few words by Laurence34s
1 exercício prático
Week 3 Quiz
5 horas para concluir
Publishing your datasets
This week let’s learn about how you can share your data with the world in a way that’s easy for others to consume!
5 horas para concluir
11 vídeos (Total 24 mín.), 2 leituras, 2 testes
11 videos
Introduction44s
How to start using a dataset2min
Implementation4min
File access and possible problems in data3min
Publishing the dataset3min
Introduction18s
Going through the colab (1)2min
Going through the colab (2)2min
Closing words14s
A conversation with Andrew Ng1min
2 leituras
URLs10min
Link to the colab10min
1 exercício prático
Week 4 Quiz
Avaliações
Principais avaliações do DATA PIPELINES WITH TENSORFLOW DATA SERVICES
por GLMar 2, 2020
Laurence cares deeply about the students. Not only about what they learn, but that they actually enjoy and learn it. What a fantastic teacher.
por ADFeb 2, 2020
Excellent course both for Data Scientists and Machine Learning Engineers!
oferecido por
deeplearning.ai
deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders.
Sobre Programa de cursos integrados TensorFlow: Data and Deployment
Continue developing your skills in TensorFlow as you learn to navigate through a wide range of deployment scenarios and discover new ways to use data more effectively when training your machine learning models.
In this four-course Specialization, you’ll learn how to get your machine learning models into the hands of real people on all kinds of devices. Start by understanding how to train and run machine learning models in browsers and in mobile applications. Learn how to leverage built-in datasets with just a few lines of code, learn about data pipelines with TensorFlow data services, use APIs to control data splitting, process all types of unstructured data and retrain deployed models with user data while maintaining data privacy. Apply your knowledge in various deployment scenarios and get introduced to TensorFlow Serving, TensorFlow, Hub, TensorBoard, and more.
Industries all around the world are adopting Artificial Intelligence. This Specialization from Laurence Moroney and Andrew Ng will help you develop and deploy machine learning models across any device or platform faster and more accurately than ever.
This Specialization builds upon skills learned in the TensorFlow in Practice Specialization. We recommend learners complete that Specialization prior to enrolling in TensorFlow: Data and Deployment.
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
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.
O que recebo ao me inscrever nesta Especialização?
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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