Chevron Left
Back to Sequences, Time Series and Prediction

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

4.7
stars
4,953 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 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

MI

Jun 6, 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.

JH

Mar 21, 2020

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.

Filter by:

551 - 575 of 781 Reviews for Sequences, Time Series and Prediction

By Brijesh G

Aug 5, 2021

good

By Suci A S

Jun 19, 2021

good

By M. S

May 28, 2021

good

By Indria A

Apr 19, 2021

cool

By ABHIJEET S

Apr 17, 2021

Nice

By alfatoni n

Apr 12, 2021

Nice

By Indah D S

Apr 10, 2021

cool

By Ahmad H N

Apr 5, 2021

good

By Shree H

Aug 14, 2020

best

By RAGHUVEER S D

Jul 25, 2020

good

By Jurassic

Sep 6, 2019

good

By echo

Aug 31, 2019

good

By Roberto

Apr 22, 2021

ty

By a

Apr 9, 2020

:)

By Ming G

Sep 11, 2019

gj

By eashwar n

Jul 3, 2021

By John K

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.

By Егор Е

Aug 24, 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.

By Xiang J

Oct 6, 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?

By José D

Apr 26, 2020

In this final course of the Tensor InPractice Specialization, all pieces come together to solve a real world example (Kaggles' sunspots) using Keras (TensorFlow's high-level API). This course focuses on Time sequence, using CNN & RNN. As explained in the videos, this specialization is an introduction to Deep Learning using Keras. There is no math in all of the courses. As a result, if you want to understand why and how it works under the hood, you want to do the "Deep Learning" Specialization. As I did The DeepLearning one before this one, this whole specialization was like an addition exercise.

By Francisco F

Sep 23, 2020

I wish this course was taught with real world data, which only happens in week 4. Rather, the course utilizes synthetic data, which is not as great in providing perspective as real data and real problems. Also, volume is really low, don't know why. Other than that, as always, great course. Great specialization! A pretty good intro to Tensorflow for the ones who haven't used it before and a nice recap of the basics for the ones like me who have been using and have missed some core concepts here and there.

By Muthiah A

Jan 9, 2020

I enjoyed the thoughtful exercises and measured experienced guidance of Laurence (who has been doing this for years now in big stage). It’s a bite sized introduction to Tensorflow aspects for busy professionals and while you can “game” the quizzes and earn completion, really the onus is on learner to spend time on reading materials and videos and great colab exercises. Google Colab notebooks are single outstanding reason this whole specialization is compelling to me.

Thanks everyone @ Coursera

By João A J d S

Aug 3, 2019

I think I might say this for every course of this specialisation:

Great content all around!

It has some great colab examples explaining how to put these models into action on TensorFlow, which I'm know I'm going to revisit time and again.

There's only one thing that I think it might not be quite so good: the evaluation of the course. There isn't one, apart from the quizes. A bit more evaluation steps, as per in Andrew's Deep Learning Specialisation, would require more commitment from students.

By Edward T

Aug 8, 2020

Its a shame that the assignments were not graded. What's the incentive to struggle and dive deep when the notebook is just a repetition of the lecture notebooks and the assignment is ungraded? This course would greatly benefit in making those assignments graded and bringing in multivariate, multistep forecasting into the mix! Overall, though, I did enjoy it and I learned a thing or two about modelling and signal processing. Need to continue on this journey!

By Mihail Y

Sep 10, 2020

Very interesting course. Thanks to Laurance Moroney for the clear and concise way of presenting complex concepts. The only reason I am giving 4 star to the course is that I was really expecting to get more about the information on forecasting using multiple series as features and forecasting multiple series at once. I think it will be interesting to add to the course more on this, as it is still tricky for me to manage these tasks in Tensorflow.