About this Course
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Flexible deadlines

Flexible deadlines

Reset deadlines in accordance to your schedule.
Advanced Level

Advanced Level

Hours to complete

Approx. 11 hours to complete

Suggested: 14 hours/week...
Available languages

English

Subtitles: English
100% online

100% online

Start instantly and learn at your own schedule.
Flexible deadlines

Flexible deadlines

Reset deadlines in accordance to your schedule.
Advanced Level

Advanced Level

Hours to complete

Approx. 11 hours to complete

Suggested: 14 hours/week...
Available languages

English

Subtitles: English

Syllabus - What you will learn from this course

Week
1
Hours to complete
4 hours to complete

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 videos (Total 41 min), 1 reading, 4 quizzes
Video14 videos
Getting Started with Google Cloud Platform and Qwiklabs3m
Sequence data and models5m
From sequences to inputs2m
Modeling sequences with linear models2m
Lab intro: using linear models for sequencesm
Lab solution: using linear models for sequences7m
Modeling sequences with DNNs2m
Lab intro: using DNNs for sequencesm
Lab solution: using DNNs for sequences2m
Modeling sequences with CNNs3m
Lab intro: using CNNs for sequencesm
Lab solution: using CNNs for sequences3m
The variable-length problem4m
Reading1 reading
How to send course feedback10m
Quiz1 practice exercise
Working with Sequences0
Hours to complete
15 minutes to complete

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 videos (Total 15 min), 1 quiz
Video4 videos
How RNNs represent the past4m
The limits of what RNNs can represent5m
The vanishing gradient problem1m
Quiz1 practice exercise
Recurrent Neural Networks0
Hours to complete
4 hours to complete

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 videos (Total 62 min), 4 quizzes
Video14 videos
LSTMs and GRUs6m
RNNs in TensorFlow2m
Lab Intro: Time series prediction: end-to-end (rnn)m
Lab Solution: Time series prediction: end-to-end (rnn)10m
Deep RNNs1m
Lab Intro: Time series prediction: end-to-end (rnn2)m
Lab Solution: Time series prediction: end-to-end (rnn2)6m
Improving our Loss Function2m
Demo: Time series prediction: end-to-end (rnnN)3m
Working with Real Data10m
Lab Intro: Time Series Prediction - Temperature from Weather Data1m
Lab Solution: Time Series Prediction - Temperature from Weather Data11m
Summary1m
Quiz1 practice exercise
Dealing with Longer Sequences0
Week
2
Hours to complete
2 hours to complete

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 videos (Total 35 min), 2 quizzes
Video8 videos
Text Classification6m
Selecting a Model2m
Lab Intro: Text Classificationm
Lab Solution: Text Classification11m
Python vs Native TensorFlow4m
Demo: Text Classification with Native TensorFlow7m
Summary1m
Quiz1 practice exercise
Text Classification0
Hours to complete
1 hour to complete

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 videos (Total 28 min), 2 quizzes
Video6 videos
Modern methods of making word embeddings8m
Introducing TensorFlow Hub1m
Lab Intro: Evaluating a pre-trained embedding from TensorFlow Hubm
Lab Solution: TensorFlow Hub9m
Using TensorFlow Hub within an estimator1m
Quiz1 practice exercise
Reusable Embeddings0
Hours to complete
3 hours to complete

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 videos (Total 84 min), 3 quizzes
Video10 videos
Attention Networks4m
Training Encoder-Decoder Models with TensorFlow6m
Introducing Tensor2Tensor11m
Lab Intro: Cloud poetry: Training custom text models on Cloud ML Engine1m
Lab Solution: Cloud poetry: Training custom text models on Cloud ML Engine25m
AutoML Translation4m
Dialogflow6m
Lab Intro: Introducing Dialogflowm
Lab Solution: Dialogflow13m
Quiz1 practice exercise
Encoder-Decoder Models0
Hours to complete
14 minutes to complete

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 video (Total 4 min), 1 reading
Video1 video
Reading1 reading
Additional Reading10m

About 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....

About the Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization

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

Frequently Asked Questions

  • 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, when available.

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