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Sequence Models for Time Series and Natural Language Processing, Google Cloud

Оценки: 46
Рецензии: 8

Об этом курсе

This course is an introduction to sequence models and their applications, including an overview of sequence model architectures and how to handle inputs of variable length. • Predict future values of a time-series • Classify free form text • Address time-series and text problems with recurrent neural networks • Choose between RNNs/LSTMs and simpler models • Train and reuse word embeddings in text problems You will get hands-on practice building and optimizing your own text classification and sequence models on a variety of public datasets in the labs we’ll work on together. Prerequisites: Basic SQL, familiarity with Python and TensorFlow...
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Рецензии: 8

автор: Raja Ranjith Garikapati

Dec 11, 2018


автор: Elias Papachristos

Dec 04, 2018

I really loved it!

автор: Hemant Devidas Kshirsagar

Dec 01, 2018

Very informative, very much useful to my ongoing work on NLP.

автор: Harold Lawrence Marzan Mercado

Nov 25, 2018

This was a very interesting course on NLP and Time Series. My only concern is that some notebooks worked for python 2 mode and not for python 3. Also, the tensor 2 tensor lab could not be completed in 2 hours, as some of the training may take more than 3 hours to complete.

Overall, good information, great technology and great teachers.

Thank you.

автор: Печатнов Юрий

Nov 22, 2018

First quiz is very bad

But totally the course is interesting and I like it :)

автор: Jun Wang

Nov 11, 2018

Excellent course for those who know RNN. Knowledge is refreshed and techniques are consolidated. More details about Google ecosystem is introduced.

автор: 林佳佑

Nov 02, 2018

this course is helpful for learning sequence data with tensor flow ,Thanks for this course

автор: Jason Cheung

Oct 19, 2018

Quite a challenging course so far.