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Отзывы учащихся о курсе Sequences, Time Series and Prediction от партнера

Оценки: 4,310
Рецензии: 687

О курсе

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 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....

Лучшие рецензии

21 мар. 2020 г.

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.

3 авг. 2019 г.

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.

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526–550 из 687 отзывов о курсе Sequences, Time Series and Prediction

автор: mehryar m

27 дек. 2019 г.

I'm so glad to take this course and build my knowledge regarding time-series data and modern approaches to create prognostic models. Thanks to Andrew Ng and L. Moroney to provide this course.


27 мар. 2020 г.

Few hands on programming assignments could be better for experience as was the case with starting two courses. Overall good course and the structure was well laid. Thanks for building it up

автор: William G

16 авг. 2019 г.

Though I feel some aspects of this course did not delve deep enough into the explanations of some functions, the course helped me understand how to use models for time series problems.

автор: winniefred m b

23 мая 2021 г.

taking this course was undoubtedly a better idea than endless scans over tensorflow documentation and other books. I am glad I got to do this course, wish I had taken this up earlier

автор: Hyungmin S

19 июля 2020 г.

I wish there were more detail explanation about hyper-parameter tuning when we define NN Models.

other than that, this course was great and gave me lot of insights. Thank you.

автор: Yongqing X

26 сент. 2020 г.

I'd like to learn more about algorithmic principle(Although some Andrew‘s class link is attached. )why not explain the principle combined with the real example

автор: CM

19 авг. 2019 г.

Wish there were graded programming exercises. The quizzes has questions not relevant to the goal of the lesson ex What is the seasonality of sunspots.

автор: Saikat M

16 мая 2020 г.

New techniques were learnt regarding how to create a time-series signal and how they can be manipulated for forcasting and feeding to DNN networks.

автор: Parth A

11 авг. 2019 г.

A good intro course to time series prediction. Would have loved some more data analysis and other time series methods like ARIMA and seasonal ARIMA

автор: Ruben Y Q

6 мая 2020 г.

course is good but it dont get deeper on using things like multivariate time series, in addition the course practice materials where kind of lax

автор: Jessie S

12 авг. 2019 г.

A little bit too simple cuz it only covers univariate time series practice. Would be better if there's more multivariate time series exercise.

автор: Dan R

13 мая 2020 г.

I was really waiting to predict 100 data that was similar to sequence, that being said; this was a good introduction to time series analysis.

автор: Kartik P

5 окт. 2020 г.

its a nice course but instead of using synthetic data, it would have been better if we use real-time datasets for our practice and learning.

автор: AMAN G

26 мая 2020 г.

The programming exercise should not have been optional. But overall, this was an amazing course. A thumbs up from my side. Thanks a lot.

автор: Ruben A M

9 сент. 2020 г.

A lot of typos and I felt that making the Graded Exercises instead of Non Graded is a much better experience for us the students.

автор: Erik J J D B

19 июля 2020 г.

Good and easy to follow course to learn tensorflow. You need a background on Machine Learning to fully benefit from this course.

автор: DING T K

12 февр. 2021 г.

It is nice in introduction to the use of TF in time series. But a bit difficult in the code which without detail explanation.

автор: Duncan B

29 апр. 2021 г.

I think this is a helpful introduction. It would have been helpful to delve into some multivariate sequence data examples.

автор: Guillermo B

2 апр. 2021 г.

It would be great if the projects at the end of each week were graded. They were easily solved using that week lesson.

автор: Thomas B

11 апр. 2020 г.

This is the best course of the specialization. You learn about applying DNN, RNN, LSTM and CNN to time series data.

автор: Itamar d P R F

20 июля 2020 г.

Great Course, enlights you were you should head. Although, it missed some further explanation of the steps used.

автор: Lorenzo N

20 янв. 2022 г.

G​reat course! It would have been nice to teach something about multivariate time series forecasting as well

автор: Nikhil K

8 июля 2020 г.

Have to complete Andrew Ng's Deeplearning as this is very basic course on top of that for time series data.

автор: Advay M

17 нояб. 2020 г.

add more videos to clear deep understanding of time series and more graded practical assignments too...