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Learner Reviews & Feedback for Sequences, Time Series and Prediction by DeepLearning.AI

4.7
stars
5,036 ratings

About the Course

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 Developer 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 reviews

FF

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This is a very suitable course for those of you who are new to machine learning, because after I took this course my interest in machine learning has increased. especially CNN computer vision.

JH

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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.

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551 - 575 of 789 Reviews for Sequences, Time Series and Prediction

By Thach V M

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Sep 2, 2022

good!

By 丰博 李

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Nov 3, 2020

坚实的基础

By Hamora H

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Jul 13, 2020

Good!

By Rodrigo N

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Sep 24, 2019

Show!

By 李英斌

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Sep 18, 2019

nice！

By Anggi P S

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Apr 14, 2024

good

By Dini P U

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Nov 4, 2023

good

By Egi R T

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Jun 29, 2022

good

By Brijesh G

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Aug 5, 2021

good

By Suci A S

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Jun 19, 2021

good

By M. S

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May 28, 2021

good

By Indria A

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Apr 19, 2021

cool

By ABHIJEET S

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Apr 17, 2021

Nice

By alfatoni n

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Apr 12, 2021

Nice

By Indah D S

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Apr 10, 2021

cool

By Ahmad H N

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Apr 5, 2021

good

By Shree H

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Aug 14, 2020

best

By RAGHUVEER S D

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Jul 25, 2020

good

By Jurassic

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Sep 6, 2019

good

By echo

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Aug 31, 2019

good

By Roberto

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Apr 22, 2021

ty

By a

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Apr 9, 2020

:)

By Ming G

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Sep 11, 2019

gj

By eashwar n

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Jul 3, 2021

By John K

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Aug 27, 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.