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.
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DeepLearning.AI TensorFlow Developer 專業證書deeplearning.ai課程信息
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第 4 門課程（共 4 門）
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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）
字幕：阿拉伯語（Arabic）, 法語（French）, （歐洲人講的）葡萄牙語, 中文（簡體）, 意大利語, 越南語, 韓語, 德語（German）, 俄語（Russian）, 土耳其語（Turkish）, 英語（English）, 西班牙語（Spanish）
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）
可靈活調整截止日期
根據您的日程表重置截止日期。
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）
字幕：阿拉伯語（Arabic）, 法語（French）, （歐洲人講的）葡萄牙語, 中文（簡體）, 意大利語, 越南語, 韓語, 德語（German）, 俄語（Russian）, 土耳其語（Turkish）, 英語（English）, 西班牙語（Spanish）
提供方
deeplearning.ai
Founded by Andrew Ng, 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
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
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
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
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
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
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
More on LSTM1分鐘
5 個閱讀材料
More info on Huber loss10分鐘
RNN notebook10分鐘
Link to the LSTM lesson10分鐘
LSTM notebook10分鐘
Week 3 Wrap up10分鐘
1 個練習
Week 3 Quiz
完成時間為 3 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 3 小時
11 個視頻 （總計 24 分鐘）, 5 個閱讀材料, 3 個測驗
11 個視頻
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
5 個閱讀材料
Convolutional neural networks course10分鐘
More on batch sizing10分鐘
LSTM notebook10分鐘
Sunspots notebook10分鐘
Wrap up10分鐘
1 個練習
Week 4 Quiz
審閱
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 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.
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.
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.
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