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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!
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
Deep Neural Networks for Time Series
Week 2•5 hours to complete
Module details
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!
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 for Time Series
Week 3•5 hours to complete
Module details
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...
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.
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Learner reviews
4.7
5,162 reviews
5 stars
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4 stars
15.72%
3 stars
3.89%
2 stars
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1 star
1.22%
Showing 3 of 5162
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MM
4·
Reviewed on Dec 26, 2019
I'm so glad to take this course and build my knowledge regarding time-series data and modern approaches to create prognostic models. Thanks to Andrew Ng and L. Moroney to provide this course.
W
WE
4·
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.
F
FF
5·
Reviewed on Apr 15, 2022
This is a very suitable course for those of you who are new to machine learning, because after I took this course my interest in machine learning has increased. especially CNN computer vision.
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