Sequences, Time Series and Prediction

This course is part of DeepLearning.AI TensorFlow Developer Professional Certificate

Instructor: Laurence Moroney

150,822 already enrolled

4 modules

Gain insight into a topic and learn the fundamentals.

5,157 reviews

Intermediate level

Recommended experience

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

97%

Most learners liked this course

4 modules

Gain insight into a topic and learn the fundamentals.

5,157 reviews

Intermediate level

Recommended experience

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

97%

Most learners liked this course

What you'll learn

Solve time series and forecasting problems in TensorFlow
Prepare data for time series learning using best practices
Explore how RNNs and ConvNets can be used for predictions
Build a sunspot prediction model using real-world data

Skills you'll gain

Tools you'll learn

Tensorflow

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

4 assignments

Taught in English

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This course is part of the DeepLearning.AI TensorFlow Developer Professional Certificate

When you enroll in this course, you'll also be enrolled in this Professional Certificate.

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There are 4 modules in this course

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 Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.

In this fourth course, you will learn how to build time series models in TensorFlow. You’ll first implement best practices to prepare time series data. You’ll also explore how RNNs and 1D ConvNets can be used for prediction. Finally, you’ll apply everything you’ve learned throughout the Specialization to build a sunspot prediction model using real-world data! The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new DeepLearning.AI TensorFlow Developer Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.

Hi Learners and welcome to this course on sequences and prediction! In this course we'll take a look at some of the unique considerations involved when handling sequential time series data -- where values change over time, like the temperature on a particular day, or the number of visitors to your web site. We'll discuss various methodologies for predicting future values in these time series, building on what you've learned in previous courses!

What's included

10 videos7 readings1 assignment1 programming assignment2 ungraded labs

10 videos Total 32 minutes

Introduction: A conversation with Andrew Ng 3 minutes
Time series examples 4 minutes
Machine learning applied to time series 2 minutes
Common patterns in time series 5 minutes
Introduction to time series 4 minutes
Train, validation and test sets 3 minutes
Metrics for evaluating performance 2 minutes
Moving average and differencing 3 minutes
Trailing versus centered windows 1 minute
Forecasting 4 minutes

7 readings Total 18 minutes

Welcome to the course! 1 minute
About the notebooks in this course 5 minutes
Week 1 Wrap up 2 minutes
Join the DeepLearning.AI Forum to ask questions, get support, or share amazing ideas! 2 minutes
Lecture Notes Week 1 1 minute
Assignment Troubleshooting Tips 5 minutes
(Optional) Downloading your Notebook and Refreshing your Workspace 2 minutes

1 assignment Total 30 minutes

Week 1 Quiz 30 minutes

1 programming assignment Total 180 minutes

Working with generated time series 180 minutes

2 ungraded labs Total 60 minutes

Introduction to time series notebook (Lab 1) 30 minutes
Forecasting notebook (Lab 2) 30 minutes

Having explored time series and some of the common attributes of time series such as trend and seasonality, and then having used statistical methods for projection, let's now begin to teach neural networks to recognize and predict on time series!

What's included

10 videos2 readings1 assignment1 programming assignment3 ungraded labs

10 videos Total 25 minutes

A conversation with Andrew Ng 1 minute
Preparing features and labels 4 minutes
Preparing features and labels (screencast) 3 minutes
Feeding windowed dataset into neural network 2 minutes
Single layer neural network 3 minutes
Machine learning on time windows 1 minute
Prediction 2 minutes
More on single layer neural network 2 minutes
Deep neural network training, tuning and prediction 4 minutes
Deep neural network 3 minutes

2 readings Total 2 minutes

Week 2 Wrap up 1 minute
Lecture Notes Week 2 1 minute

1 assignment Total 30 minutes

Week 2 Quiz 30 minutes

1 programming assignment Total 180 minutes

Forecasting Using Neural Networks 180 minutes

3 ungraded labs Total 90 minutes

Preparing features and labels notebook (Lab 1) 30 minutes
Single layer neural network notebook (Lab 2) 30 minutes
Deep neural network notebook (Lab 3) 30 minutes

Recurrent Neural networks and Long Short Term Memory networks are really useful to classify and predict on sequential data. This week we'll explore using them with time series...

What's included

8 videos4 readings1 assignment1 programming assignment2 ungraded labs

8 videos Total 16 minutes

Week 3 - A conversation with Andrew Ng 3 minutes
Conceptual overview 3 minutes
Shape of the inputs to the RNN 2 minutes
Outputting a sequence 1 minute
Lambda layers 1 minute
Adjusting the learning rate dynamically 2 minutes
LSTM 2 minutes
Coding LSTMs 2 minutes

4 readings Total 22 minutes

More info on Huber loss 10 minutes
Link to the LSTM lesson 10 minutes
Week 3 Wrap up 1 minute
Lecture Notes Week 3 1 minute

1 assignment Total 30 minutes

Week 3 Quiz 30 minutes

1 programming assignment Total 180 minutes

Forecast using RNNs or LSTMs 180 minutes

2 ungraded labs Total 60 minutes

RNN notebook (Lab 1) 30 minutes
LSTM notebook (Lab 2) 30 minutes

On top of DNNs and RNNs, let's also add convolutions, and then put it all together using a real-world data series -- one which measures sunspot activity over hundreds of years, and see if we can predict using it.

What's included

11 videos9 readings1 assignment1 programming assignment2 ungraded labs

11 videos Total 22 minutes

Week 4 - A conversation with Andrew Ng 1 minute
Convolutions 1 minute
Bi-directional LSTMs 3 minutes
Convolutions with LSTM 1 minute
Real data - sunspots 3 minutes
Train and tune the model 3 minutes
Prediction 2 minutes
Sunspots 1 minute
Combining our tools for analysis 4 minutes
Congratulations! 1 minute
Specialization wrap up - A conversation with Andrew Ng 2 minutes

9 readings Total 42 minutes

Convolutional neural networks course 10 minutes
More on batch sizing 10 minutes
Lecture Notes Week 4 1 minute
[IMPORTANT] Reminder about end of access to Lab Notebooks 2 minutes
Wrap up 1 minute
References 10 minutes
Acknowledgments 1 minute
What next? 5 minutes
(Optional) Opportunity to Mentor Other Learners 2 minutes

1 assignment Total 30 minutes

Week 4 Quiz 30 minutes

1 programming assignment Total 180 minutes

Adding CNNs to improve forecasts 180 minutes

2 ungraded labs Total 75 minutes

Convolutions with LSTM notebook (Lab 1) 30 minutes
Sunspots notebooks (Lab 2 & Lab 3) 45 minutes

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Instructor

Instructor ratings

(787 ratings)

Laurence Moroney

DeepLearning.AI

22 Courses 595,289 learners

Offered by

DeepLearning.AI

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Reviewed on Jun 6, 2020

I really enjoyed this course, especially because it combines all different components (DNN, CONV-NET, and RNN) together in one application. I look forward to taking more courses from deeplearning.ai.

Reviewed on Jul 16, 2020

The course is fantastic. It was a bit short and with some hyperparameters tuning focus, it could have been great. Also, it seems that it is biased to show that LSTM is always superior to RNN networks.

Reviewed on May 14, 2020

The course is easy to follow and focuses on how to implement theoretical concepts using tensor flow. The way Lawrence approaches the sessions makes it really interesting and fun to learn!

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