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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TensorFlow in Practice の専門講座deeplearning.aiこのコースについて
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共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
次における4の4コース
柔軟性のある期限
スケジュールに従って期限をリセットします。
中級レベル
You should take the first 3 courses of the TensorFlow Specialization and be comfortable coding in Python and understanding high school-level math.
約13時間で修了
英語
字幕：英語
学習内容
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
習得するスキル
ForecastingMachine LearningTensorflowTime Seriesprediction
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
次における4の4コース
柔軟性のある期限
スケジュールに従って期限をリセットします。
中級レベル
You should take the first 3 courses of the TensorFlow Specialization and be comfortable coding in Python and understanding high school-level math.
約13時間で修了
英語
字幕：英語
提供：
deeplearning.ai
Founded by Andrew Ng, deeplearning.ai is on a mission to make world-class AI education accessible to people around the globe so that we can all benefit from an AI-powered future.
シラバス - 本コースの学習内容
3時間で修了
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!
3時間で修了
10件のビデオ （合計33分）, 3 readings, 3 quizzes
10件のビデオ
Time series examples4 分
Machine learning applied to time series1 分
Common patterns in time series5 分
Introduction to time series4 分
Train, validation and test sets3 分
Metrics for evaluating performance2 分
Moving average and differencing2 分
Trailing versus centered windows1 分
Forecasting4 分
3件の学習用教材
Introduction to time series notebook10 分
Forecasting notebook10 分
Week 1 Wrap up10 分
1の練習問題
Week 1 Quiz
3時間で修了
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!
3時間で修了
10件のビデオ （合計27分）, 5 readings, 3 quizzes
10件のビデオ
Preparing features and labels4 分
Preparing features and labels3 分
Feeding windowed dataset into neural network2 分
Single layer neural network2 分
Machine learning on time windows37
Prediction2 分
More on single layer neural network2 分
Deep neural network training, tuning and prediction4 分
Deep neural network3 分
5件の学習用教材
Preparing features and labels notebook10 分
Sequence bias10 分
Single layer neural network notebook10 分
Deep neural network notebook10 分
Week 2 Wrap up10 分
1の練習問題
Week 2 Quiz
3時間で修了
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...
3時間で修了
10件のビデオ （合計20分）, 5 readings, 3 quizzes
10件のビデオ
Conceptual overview2 分
Shape of the inputs to the RNN2 分
Outputting a sequence1 分
Lambda layers1 分
Adjusting the learning rate dynamically2 分
RNN1 分
LSTM1 分
Coding LSTMs2 分
More on LSTM1 分
5件の学習用教材
More info on Huber loss10 分
RNN notebook10 分
Link to the LSTM lesson10 分
LSTM notebook10 分
Week 3 Wrap up10 分
1の練習問題
Week 3 Quiz
3時間で修了
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.
3時間で修了
11件のビデオ （合計24分）, 5 readings, 3 quizzes
11件のビデオ
Convolutions58
Bi-directional LSTMs3 分
LSTM1 分
Real data - sunspots3 分
Train and tune the model3 分
Prediction1 分
Sunspots1 分
Combining our tools for analysis3 分
Congratulations!38
Specialization wrap up - A conversation with Andrew Ng2 分
5件の学習用教材
Convolutional neural networks course10 分
More on batch sizing10 分
LSTM notebook10 分
Sunspots notebook10 分
Wrap up10 分
1の練習問題
Week 4 Quiz
レビュー
SEQUENCES, TIME SERIES AND PREDICTION からの人気レビュー
JHにより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.
ORにより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.
MIにより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.
SPにより2020年6月4日
I really love this course. It's really easy to understand and systematic. please create more course about time series forecasting. Thank you very much. I've learned a lot of things from this course.
TensorFlow in Practice専門講座について
Discover the tools software developers use to build scalable AI-powered algorithms in TensorFlow, a popular open-source machine learning framework.
In this four-course Specialization, you’ll explore exciting opportunities for AI applications. Begin by developing an understanding of how to build and train neural networks. Improve a network’s performance using convolutions as you train it to identify real-world images. You’ll teach machines to understand, analyze, and respond to human speech with natural language processing systems. Learn to process text, represent sentences as vectors, and input data to a neural network. You’ll even train an AI to create original poetry!
AI is already transforming industries across the world. After finishing this Specialization, you’ll be able to apply your new TensorFlow skills to a wide range of problems and projects.
Looking for more advanced TensorFlow content? Check out the new TensorFlow: Data and Deployment Specialization.
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What will I get if I subscribe to this Specialization?
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Is financial aid available?
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