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

119,621 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24 小時

英語（English）

字幕：阿拉伯語（Arabic）, 法語（French）, （歐洲人講的）葡萄牙語, 意大利語, 越南語, 德語（German）, 俄語（Russian）, 英語（English）, 西班牙語（Spanish）, 日語

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

Forecasting
Machine Learning
Tensorflow
Time Series
prediction

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24 小時

英語（English）

授課教師

授課教師評分

4.72/5 （636 個評分）

Laurence Moroney

Instructor

Lead AI Advocate, Google

366,549 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.

授課大綱 - 您將從這門課程中學到什麼

內容評分97%（9,521 個評分）

週

週 1

完成時間為 6 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 6 小時

10 個視頻（總計 32 分鐘）, 6 個閱讀材料, 4 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 windows50

Forecasting3分鐘

6 個閱讀材料

Where to find the notebooks for this course5分鐘

Introduction to time series notebook (Lab 1)30分鐘

Forecasting notebook (Lab 2)30分鐘

Week 1 Wrap up2分鐘

Assignment Troubleshooting Tips5分鐘

(Optional) Common Coursera Labs Operations2分鐘

1 個練習

Week 1 Quiz30分鐘

週

週 2

完成時間為 6 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 6 小時

10 個視頻（總計 25 分鐘）, 4 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 labels (screencast)2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 network1分鐘

Deep neural network training, tuning and prediction4分鐘

Deep neural network2分鐘

4 個閱讀材料

Preparing features and labels notebook (Lab 1)30分鐘

Single layer neural network notebook (Lab 2)30分鐘

Deep neural network notebook (Lab 3)30分鐘

Week 2 Wrap up1分鐘

1 個練習

Week 2 Quiz30分鐘

週

週 3

完成時間為 5 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 5 小時

8 個視頻（總計 17 分鐘）, 5 個閱讀材料, 3 個測驗

8 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

LSTM1分鐘

Coding LSTMs2分鐘

5 個閱讀材料

RNN notebook (Lab 1)30分鐘

More info on Huber loss10分鐘

Link to the LSTM lesson10分鐘

LSTM notebook (Lab 2)30分鐘

Week 3 Wrap up1分鐘

1 個練習

Week 3 Quiz30分鐘

週

週 4

完成時間為 7 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 7 小時

11 個視頻（總計 23 分鐘）, 9 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

Convolutions with LSTM1分鐘

Real data - sunspots3分鐘

Train and tune the model2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

9 個閱讀材料

Convolutional neural networks course10分鐘

審閱

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來自SEQUENCES, TIME SERIES AND PREDICTION的熱門評論

由 MM 提供2019年12月26日

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.

由 SP 提供2020年3月26日

Few hands on programming assignments could be better for experience as was the case with starting two courses. Overall good course and the structure was well laid. Thanks for building it up

由 VH 提供2020年6月18日

Coming from a background of knowing Deep Learning and theory of Time Series, this course was extremely helpful in understanding the practical aspects. I would recommend you take a course as well

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.

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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