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

4.6
Оценки: 84
Рецензии: 12

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...

Лучшие рецензии

автор: MD

Feb 03, 2019

Very good.The explanation of the RNN was very good but the tensor2tensor was very hard.

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Рецензии: 12

автор: Carlos Viejo

Feb 03, 2019

Excellent Sequence Models explanations and examples to learn from, I quite enjoyed all the fantastic tips and best practices recommended by Google, looking forward to the next course in the specialization.

автор: Mark Davey

Feb 03, 2019

Very good.The explanation of the RNN was very good but the tensor2tensor was very hard.

автор: ELINGUI Pascal Uriel

Jan 27, 2019

Great one!

автор: Arindam Ghoshal

Dec 20, 2018

No Doubt COURSERA is always best AND MNC like IBM,Google courses associated with coursera are MIND-BLOWING.

The Instructors are so great at Explanation Part that hardly anyone won't Understand All the Topics

I would love to thank all the INSTRUCTORS who created such a Awesome Content for us.

My Personal Ratings For All the Instructors: 100 / 100

автор: Raja Ranjith Garikapati

Dec 11, 2018

Good

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.