4.6
87 ratings
12 reviews

#### 100% online

Start instantly and learn at your own schedule.

#### Approx. 11 hours to complete

Suggested: 17 hours/week...

#### English

Subtitles: English

#### 100% online

Start instantly and learn at your own schedule.

#### Approx. 11 hours to complete

Suggested: 17 hours/week...

#### English

Subtitles: English

### Syllabus - What you will learn from this course

Week
1
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....
14 videos (Total 41 min), 1 reading, 4 quizzes
14 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 sequences20s
Lab solution: using linear models for sequences7m
Modeling sequences with DNNs2m
Lab intro: using DNNs for sequences19s
Lab solution: using DNNs for sequences2m
Modeling sequences with CNNs3m
Lab intro: using CNNs for sequences19s
Lab solution: using CNNs for sequences3m
The variable-length problem4m
How to send course feedback10m
1 practice exercise
Working with Sequences
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....
4 videos (Total 15 min), 1 quiz
4 videos
How RNNs represent the past4m
The limits of what RNNs can represent5m
1 practice exercise
Recurrent Neural Networks
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....
14 videos (Total 62 min), 4 quizzes
14 videos
LSTMs and GRUs6m
RNNs in TensorFlow2m
Lab Intro: Time series prediction: end-to-end (rnn)45s
Lab Solution: Time series prediction: end-to-end (rnn)10m
Deep RNNs1m
Lab Intro: Time series prediction: end-to-end (rnn2)26s
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
1 practice exercise
Dealing with Longer Sequences
Week
2
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. ...
8 videos (Total 35 min), 2 quizzes
8 videos
Text Classification6m
Selecting a Model2m
Lab Intro: Text Classification47s
Lab Solution: Text Classification11m
Python vs Native TensorFlow4m
Demo: Text Classification with Native TensorFlow7m
Summary1m
1 practice exercise
Text Classification
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....
6 videos (Total 28 min), 2 quizzes
6 videos
Modern methods of making word embeddings8m
Introducing TensorFlow Hub1m
Lab Intro: Evaluating a pre-trained embedding from TensorFlow Hub24s
Lab Solution: TensorFlow Hub9m
Using TensorFlow Hub within an estimator1m
1 practice exercise
Reusable Embeddings
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....
10 videos (Total 84 min), 3 quizzes
10 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 Dialogflow54s
Lab Solution: Dialogflow13m
1 practice exercise
Encoder-Decoder Models
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. ...
1 video (Total 4 min), 1 reading
1 video
4.6
12 Reviews

### Top Reviews

By MDFeb 3rd 2019

Very good.The explanation of the RNN was very good but the tensor2tensor was very hard.

## Instructor

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....

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....