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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....

MI

2020年6月6日

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

2020年3月21日

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.

篩選依據：

創建者 ABHIJEET S

•2021年4月17日

Nice

創建者 Al F N P M

•2021年4月12日

Nice

創建者 Indah D S

•2021年4月10日

cool

創建者 Ahmad H N

•2021年4月5日

good

創建者 Shree H

•2020年8月14日

best

創建者 RAGHUVEER S D

•2020年7月25日

good

創建者 Jurassic

•2019年9月6日

good

創建者 echo

•2019年8月31日

good

創建者 Roberto

•2021年4月22日

ty

創建者 Abdulaziz A J

•2020年4月9日

:)

創建者 Ming G

•2019年9月11日

gj

創建者 Eashwar N

•2021年7月3日

創建者 John K

•2020年8月27日

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.

創建者 Егор Е

•2019年8月24日

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.

創建者 Xiang J

•2019年10月6日

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?

創建者 José D

•2020年4月26日

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.

創建者 Francisco F

•2020年9月23日

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.

創建者 Muthiah A

•2020年1月9日

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

創建者 João A J d S

•2019年8月3日

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.

創建者 Edward T

•2020年8月8日

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!

創建者 Mihail Y

•2020年9月10日

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.

創建者 Shubham K

•2020年8月18日

Really nice introduction time series data analysis for regression and prediction. This course extends what you will learn in the rest of the specialisation (NN, Dense Layers, Convolutions, RNN, LSTM) to univariate time series data. I highly recommend this. Its very easy after you do rest of courses from specialisation. Good luck learning. And kudos to Laurence and Dr Andrew Ng for being a lovely instructors and making this accessible to all .

創建者 4SF18IS103 - S A

•2020年6月7日

I liked the flow of the course, working on synthetic data and then moving to real data. But I also think it would be better if I had already taken Andrew Ng's Deep Learning Course before approaching this Course. Plus since there weren't any Graded Programming exercise, it didn't feel like I would be confident in making my own model. So I'm going back and taking Deep learning course.

創建者 Tibor S

•2021年1月3日

Great course for a brief intorduction to time series predictions. One needs to integrate knowledge gained from somewhere else (i.e. the course is not comprehensive, but that is also not expected). What I was missing is clarification from authors of some of the important questions/comments in the forum. Several things from the course are left unexplained. Otherwise, I recommend it!

創建者 Gerard S

•2020年3月26日

First of all congratulations on the specialization. I felt that I have improved a lot my previous knowledge of Machine Learning and programming with Python and TS. One improving note:I felt that this course could go to third place in the specialization. You go deeper in CNN and LSTM which I missed in the previous one :)

Also, it would be great 2 examples of real-world scenarios