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.
本課程是 TensorFlow in Practice 專項課程 專項課程的一部分
Sequences, Time Series and Prediction
提供方
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
100% 在線
第 4 門課程（共 4 門）
可靈活調整截止日期
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8 小時
英語（English）
教學大綱 - 您將從這門課程中學到什麼
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!
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!
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...
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.
來自Sequences, Time Series and Prediction的熱門評論
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.
Great course! The notebooks were a great help for understanding the material. I only wish there were auto-graded notebooks in addition to the quizzes like in some of the other courses by Andrew Ng.
講師
Laurence Moroney
AI Advocate關於 deeplearning.ai
關於 TensorFlow in Practice 專項課程
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