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Bewertung und Feedback des Lernenden für Sequences, Time Series and Prediction von deeplearning.ai

4.7
Sterne
4,453 Bewertungen
704 Bewertungen

Über den Kurs

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....

Top-Bewertungen

OR

3. Aug. 2019

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

6. Juni 2020

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.

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476 - 500 von 704 Bewertungen für Sequences, Time Series and Prediction

von Sabrina M U

17. Apr. 2021

great!

von dandy

15. Apr. 2020

xiexie

von 丰博 李

3. Nov. 2020

坚实的基础

von Hamora H

13. Juli 2020

Good!

von Rodrigo N

24. Sep. 2019

Show!

von 李英斌

18. Sep. 2019

nice!

von Brijesh G

5. Aug. 2021

good

von Suci A S

19. Juni 2021

good

von M. S

28. Mai 2021

good

von Indria A

19. Apr. 2021

cool

von ABHIJEET S

17. Apr. 2021

Nice

von Al F N P M

12. Apr. 2021

Nice

von Indah D S

10. Apr. 2021

cool

von Ahmad H N

5. Apr. 2021

good

von Shree H

14. Aug. 2020

best

von RAGHUVEER S D

25. Juli 2020

good

von Jurassic

6. Sep. 2019

good

von echo

31. Aug. 2019

good

von Roberto

22. Apr. 2021

ty

von Abdulaziz A J

9. Apr. 2020

:)

von 林韋銘

11. Sep. 2019

gj

von eashwar n

3. Juli 2021

von John K

27. Aug. 2020

Very good way to get familiar with Tensoflow - it's pluses as well as its minuses.

Good overview of applying tf.keras to this topic. Machine learning is clearly a practical discipline (i.e. theory alone will not get you there), so I appreciated the chance to write some code and read a decent amount of code.

Laurence Moroney is a good, upbeat instructor.

All the courses within the Tensorflow in Practice specialization on Coursera may be most beneficial after first taking Andrew Ng's course on AI (also Coursera), but if you know something about loss functions, gradient descent, and backpropagation (which can be learned quick-and-dirty online), then you should be fine to go ahead and take this specialization before Professor Ng's course.

My one persistent wish for all four of the courses in this specialization is that significantly more time be spent on understanding the shapes of tensors as they flow through the models. Invariably, the only areas that gave me real problems as I did the coding homework were those where my tensor shape did not match what the model needed to see. Documentation at Tensorflow.org was of little help with this topic. Looking at Stackoverflow, it is apparent that there are certain (unwritten?) facts about the order and count of dimensions for the tensors as they flow through, e.g. batch count is listed first, time step is second, frame is third, or something like that. What if I have twelve dimensions in my tensor? Do certain model layers require a minimum number of dimensions of input or output? etc. etc.

Finally, this specialization really teaches the tf.keras framework, not Tensorflow itself, which I do not think was explained in the course info, but maybe I missed it. Still - keras is probably a good way to enter the subject.

All in all, I do know a lot more than I did before, and have acquired new skills. Clearly, there's more to work on, which is good.

von Егор Е

24. Aug. 2019

I like very match the first and second week of the course, because it contains dense new theoretical and practical things. The idea of time series forecasting and preparing windowed dataset was explained very clear and was very usefull for all next lessons. Also the difference between statistic and neural network approaches was very helpful.

The 3 and 4 week I would prefer zip in one , because the experiments with RNN, LSTM and Conv is very familiar and actually I've done them together one by one. I would pleased to learn some explanation and examples why each type of architecture follow their result. How the results depend on dataset preparation. Particulary, I did not get what architecture work better with seasonality, autocorrelations, and noise.

von Xiang J

6. Okt. 2019

I think overall it is a good course, these are the things I learnt:

First-hand experience with tensorflow, but more focus on the basics of keras

Knows how to preprocess data for image, text, and times series to feed it into NN

Knows basic concepts of keras layers such as CNN, LSTM, RNN, Conv1D, DNN

Knows learning rate rough gauge techniques

Things to improve:

Fix the typos, such as window[:1], there are a few posted in the forum

Should introduce more basics of tensorflow instead of kerasShould

include more links/documentation for the side knowledge, such as paddingAdding

some layers seems magical, such as Conv1D before LSTM for time series, what is the logic behind?