Informações sobre o curso
This course is an introduction to sequence models and their applications, including an overview of sequence model architectures and how to handle inputs of variable length. • Predict future values of a time-series • Classify free form text • Address time-series and text problems with recurrent neural networks • Choose between RNNs/LSTMs and simpler models • Train and reuse word embeddings in text problems You will get hands-on practice building and optimizing your own text classification and sequence models on a variety of public datasets in the labs we’ll work on together. Prerequisites: Basic SQL, familiarity with Python and TensorFlow...
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Advanced Level

Nível avançado

Clock

Sugerido: 7 hours/week

Aprox. 8 horas restantes
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English

Legendas: English
Globe

cursos 100% online

Comece imediatamente e aprenda em seu próprio cronograma.
Calendar

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.
Advanced Level

Nível avançado

Clock

Sugerido: 7 hours/week

Aprox. 8 horas restantes
Comment Dots

English

Legendas: English

Programa - O que você aprenderá com este curso

1

Seção
Clock
4 horas para concluir

Working with Sequences

In this module, you’ll learn what a sequence is, see how you can prepare sequence data for modeling, and be introduced to some classical approaches to sequence modeling and practice applying them....
Reading
14 vídeos (Total de 41 min), 1 leitura, 4 testes
Video14 videos
Getting Started with Google Cloud Platform and Qwiklabs3min
Sequence data and models5min
From sequences to inputs2min
Modeling sequences with linear models2min
Lab intro: using linear models for sequencesmin
Lab solution: using linear models for sequences7min
Modeling sequences with DNNs2min
Lab intro: using DNNs for sequencesmin
Lab solution: using DNNs for sequences2min
Modeling sequences with CNNs3min
Lab intro: using CNNs for sequencesmin
Lab solution: using CNNs for sequences3min
The variable-length problem4min
Reading1 leituras
How to send course feedback10min
Quiz1 exercício prático
Working with Sequencesmin
Clock
15 minutos para concluir

Recurrent Neural Networks

In this module, we introduce recurrent neural nets, explain how they address the variable-length sequence problem, explain how our traditional optimization procedure applies to RNNs, and review the limits of what RNNs can and can’t represent....
Reading
4 vídeos (Total de 15 min), 1 teste
Video4 videos
How RNNs represent the past4min
The limits of what RNNs can represent5min
The vanishing gradient problem1min
Quiz1 exercício prático
Recurrent Neural Networksmin
Clock
4 horas para concluir

Dealing with Longer Sequences

In this module we dive deeper into RNNs. We’ll talk about LSTMs, Deep RNNs, working with real world data, and more....
Reading
14 vídeos (Total de 62 min), 4 testes
Video14 videos
LSTMs and GRUs6min
RNNs in TensorFlow2min
Lab Intro: Time series prediction: end-to-end (rnn)min
Lab Solution: Time series prediction: end-to-end (rnn)10min
Deep RNNs1min
Lab Intro: Time series prediction: end-to-end (rnn2)min
Lab Solution: Time series prediction: end-to-end (rnn2)6min
Improving our Loss Function2min
Demo: Time series prediction: end-to-end (rnnN)3min
Working with Real Data10min
Lab Intro: Time Series Prediction - Temperature from Weather Data1min
Lab Solution: Time Series Prediction - Temperature from Weather Data11min
Summary1min
Quiz1 exercício prático
Dealing with Longer Sequencesmin

2

Seção
Clock
2 horas para concluir

Text Classification

In this module we look at different ways of working with text and how to create your own text classification models. ...
Reading
8 vídeos (Total de 35 min), 2 testes
Video8 videos
Text Classification6min
Selecting a Model2min
Lab Intro: Text Classificationmin
Lab Solution: Text Classification11min
Python vs Native TensorFlow4min
Demo: Text Classification with Native TensorFlow7min
Summary1min
Quiz1 exercício prático
Text Classificationmin
Clock
1 hora para concluir

Reusable Embeddings

Labeled data for our classification models is expensive and precious. Here we will address how we can reuse pre-trained embeddings to make our models with TensorFlow Hub....
Reading
6 vídeos (Total de 28 min), 2 testes
Video6 videos
Modern methods of making word embeddings8min
Introducing TensorFlow Hub1min
Lab Intro: Evaluating a pre-trained embedding from TensorFlow Hubmin
Lab Solution: TensorFlow Hub9min
Using TensorFlow Hub within an estimator1min
Quiz1 exercício prático
Reusable Embeddingsmin
Clock
3 horas para concluir

Encoder-Decoder Models

In this module, we focus on a sequence-to-sequence model called the encoder-decoder network to solve tasks, such as Machine Translation, Text Summarization and Question Answering....
Reading
10 vídeos (Total de 84 min), 3 testes
Video10 videos
Attention Networks4min
Training Encoder-Decoder Models with TensorFlow6min
Introducing Tensor2Tensor11min
Lab Intro: Cloud poetry: Training custom text models on Cloud ML Engine1min
Lab Solution: Cloud poetry: Training custom text models on Cloud ML Engine25min
AutoML Translation4min
Dialogflow6min
Lab Intro: Introducing Dialogflowmin
Lab Solution: Dialogflow13min
Quiz1 exercício prático
Encoder-Decoder Modelsmin
Clock
14 minutos para concluir

Summary

In this final module, we review what you have learned so far about sequence modeling for time-series and natural language data. ...
Reading
1 vídeo (Total de 4 min), 1 leitura
Video1 vídeos
Reading1 leituras
Additional Reading10min

Sobre Google Cloud

We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success....

Sobre o Programa de cursos integrados Advanced Machine Learning with TensorFlow on Google Cloud Platform

This 5-course specialization focuses on advanced machine learning topics using Google Cloud Platform where you will get hands-on experience optimizing, deploying, and scaling production ML models of various types in hands-on labs. This specialization picks up where “Machine Learning on GCP” left off and teaches you how to build scalable, accurate, and production-ready models for structured data, image data, time-series, and natural language text. It ends with a course on building recommendation systems. Topics introduced in earlier courses are referenced in later courses, so it is recommended that you take the courses in exactly this order....
Advanced Machine Learning with TensorFlow on Google Cloud Platform

Perguntas Frequentes – FAQ

  • Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

  • If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

  • Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.

  • If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.

  • This course is one of a few offered on Coursera that are currently available only to learners who have paid or received financial aid. If you’d like to take this course, but can’t afford the course fee, we encourage you to submit a financial aid application.

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