Sequences, Time Series and Prediction

本課程是 DeepLearning.AI TensorFlow Developer 專業證書專項課程的一部分

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Laurence Moroney

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DeepLearning.AI TensorFlow Developer 專業證書deeplearning.ai

課程信息

134,567 次近期查看

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 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.

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第 4 門課程（共 4 門）

DeepLearning.AI TensorFlow Developer

中級

You should take the first 3 courses of the TensorFlow Specialization and be comfortable coding in Python and understanding high school-level math.

完成時間大約為13 小時

英語（English）

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您將學到的內容有

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

您將獲得的技能

ForecastingMachine LearningTensorflowTime Seriesprediction

可靈活調整截止日期

根據您的日程表重置截止日期。

可分享的證書

完成後獲得證書

100% 在線

立即開始，按照自己的計劃學習。

第 4 門課程（共 4 門）

DeepLearning.AI TensorFlow Developer

中級

You should take the first 3 courses of the TensorFlow Specialization and be comfortable coding in Python and understanding high school-level math.

完成時間大約為13 小時

英語（English）

講師

授課教師評分

4.72/5 （554 個評分）

Laurence Moroney

Instructor

Lead AI Advocate, Google

314,585 個學生

15 門課程

提供方

deeplearning.ai

DeepLearning.AI is an education technology company that develops a global community of AI talent.

教學大綱 - 您將從這門課程中學到什麼

內容評分97%（8,291 個評分）

週

週 1

完成時間為 3 小時

Sequences and Prediction

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!

完成時間為 3 小時

10 個視頻（總計 33 分鐘）, 3 個閱讀材料, 3 個測驗

10 個視頻

Introduction, A conversation with Andrew Ng3分鐘

Time series examples4分鐘

Machine learning applied to time series1分鐘

Common patterns in time series5分鐘

Introduction to time series4分鐘

Train, validation and test sets3分鐘

Metrics for evaluating performance2分鐘

Moving average and differencing2分鐘

Trailing versus centered windows1分鐘

Forecasting4分鐘

3 個閱讀材料

Introduction to time series notebook10分鐘

Forecasting notebook10分鐘

Week 1 Wrap up10分鐘

1 個練習

Week 1 Quiz

週

週 2

完成時間為 3 小時

Deep Neural Networks for Time Series

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!

完成時間為 3 小時

10 個視頻（總計 27 分鐘）, 5 個閱讀材料, 3 個測驗

10 個視頻

A conversation with Andrew Ng1分鐘

Preparing features and labels4分鐘

Preparing features and labels3分鐘

Feeding windowed dataset into neural network2分鐘

Single layer neural network2分鐘

Machine learning on time windows37

Prediction2分鐘

More on single layer neural network2分鐘

Deep neural network training, tuning and prediction4分鐘

Deep neural network3分鐘

5 個閱讀材料

Preparing features and labels notebook10分鐘

Sequence bias10分鐘

Single layer neural network notebook10分鐘

Deep neural network notebook10分鐘

Week 2 Wrap up10分鐘

1 個練習

Week 2 Quiz

週

週 3

完成時間為 3 小時

Recurrent Neural Networks for Time Series

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...

完成時間為 3 小時

10 個視頻（總計 20 分鐘）, 5 個閱讀材料, 3 個測驗

10 個視頻

Week 3 - A conversation with Andrew Ng3分鐘

Conceptual overview2分鐘

Shape of the inputs to the RNN2分鐘

Outputting a sequence1分鐘

Lambda layers1分鐘

Adjusting the learning rate dynamically2分鐘

RNN1分鐘

LSTM1分鐘

Coding LSTMs2分鐘

週 4

完成時間為 4 小時

Real-world time series data

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.

完成時間為 4 小時

11 個視頻（總計 24 分鐘）, 7 個閱讀材料, 3 個測驗

11 個視頻

Week 4 - A conversation with Andrew Ng1分鐘

Convolutions58

Bi-directional LSTMs3分鐘

LSTM1分鐘

Real data - sunspots3分鐘

Train and tune the model3分鐘

Prediction1分鐘

Sunspots1分鐘

Combining our tools for analysis3分鐘

Congratulations!38

Specialization wrap up - A conversation with Andrew Ng2分鐘

7 個閱讀材料

Convolutional neural networks course10分鐘

審閱

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654 條評論

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來自SEQUENCES, TIME SERIES AND PREDICTION的熱門評論

由 WE 提供2020年7月16日

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.

由 JH 提供2020年3月21日

Really like the focus on practical application and demonstrating the latest capability of TensorFlow. As mentioned in the course, it is a great compliment to Andrew Ng's Deep Learning Specialization.

由 MI 提供2020年6月6日

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.

由 OR 提供2019年8月3日

It was an amazing experience to learn from such great experts in the field and get a complete understanding of all the concepts involved and also get thorough understanding of the programming skills.

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關於 DeepLearning.AI TensorFlow Developer 專業證書

TensorFlow is one of the most in-demand and popular open-source deep learning frameworks available today. The DeepLearning.AI TensorFlow Developer Professional Certificate program teaches you applied machine learning skills with TensorFlow so you can build and train powerful models.

In this hands-on, four-course Professional Certificate program, you’ll learn the necessary tools to build scalable AI-powered applications with TensorFlow. After finishing this program, you’ll be able to apply your new TensorFlow skills to a wide range of problems and projects. This program can help you prepare for the Google TensorFlow Certificate exam and bring you one step closer to achieving the Google TensorFlow Certificate. Ready to deploy your models to the world? Learn how to go live with your models with the TensorFlow: Data and Deployment Specialization. Looking to customize and build powerful real-world models for complex scenarios? Check out the TensorFlow: Advanced Techniques Specialization.

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Sequences, Time Series and Prediction