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Avis et commentaires pour d'étudiants pour Sequences, Time Series and Prediction par deeplearning.ai

4.7
étoiles
4,479 évaluations
711 avis

À propos du cours

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....

Meilleurs avis

MI

6 juin 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.

OR

3 août 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.

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626 - 650 sur 711 Avis pour Sequences, Time Series and Prediction

par Ashwani Y

24 avr. 2020

it was good

par Vikas C

24 déc. 2019

Good course

par Yu-Chen L

26 juin 2020

Okay

par Joanna S

21 juin 2020

I am a software engineer with a good base knowledge of machine learning and neural networks, and I took this course to get more specific knowledge about time series and TensorFlow to help with a project using stock market data. The content of this course is very shallow. I don't feel like I learned much reusable knowledge because much of the course is basically walking through code in Jupyter notebooks. If I wanted to just learn to copy someone else's code, I can do that on my own (for free) reading blog posts or tutorials. Also, quiz questions that ask about function names or names of libraries do not show any understanding of concepts and really just felt like filler because they needed 10 questions but hadn't taught any concepts to ask actual questions about.

I'm giving this 3 stars instead of 1 because maybe the audience is supposed to be students with less knowledge of machine learning or programming, or maybe it just doesn't match my learning style.

par Vincenzo T

15 nov. 2020

The course in general is good and introduces you to the uses of tensorflow keras API with different cases, but i can't give 5 stars because i think it still lacks on fundamental teaching about tensorflow.

I mean that during the course some tensorflow tools appear out of nothing, mainwhile i think would make a lot of sense to dedicate at least one course's module to introduce tensorflow library itself.

Just an example: during the last week we make an extensive use of tensorflow "Dataset" class to load the data into neural networks, and this tool appears out of nothing, but it seems very important and useful stuff that i think would deserve some introduction and explaining before you use it.

par Jiawei X

11 janv. 2020

This course is great for introduction. BUT it is still lacking very important background information of the Tensorflow Dataset and how to master it.

It makes sense not to go into too deep on the NN model and their theories but when it comes to practical usage of any machine learning packages, data pipelines play very significant role (count towards 60% - 70% of the codes).

In the course we briefly talk about Dataset and use only a few APIs without explaining why and the logic behind them. And tutorials from tensorflow's officials still lacking useful guidelines when dealing with dataset of multiple dimensions.

par Yemi A

16 août 2019

I found the start of the specialism was very well explained; and as a result now I really understand CNNs (as it is was explained much better than the other courses I’m doing on Udemy and LinkedIn Learning). However I would suggest that Andrew and Laurence revisit the latter part of the course from a learner point of view, looking at the pain points along their journey through Sequences and Predictions. Overall, the structure of the whole specialism can be improved, and I find it not as good as my previous course (Andrew’s Standford University Machine Learning Course which was brilliant)

par Egemen Y K

4 juin 2020

Though the course is very educational, the prediction is done at the right way. One can not use the windows of validation data to test it. The testing accuracy should be measured via point by point prediction which predicts the future value based on the predictions. At that way, the hardness of the problem makes sense, otherwise anyone could use the linear regression models rather than LSTMs. Please review the content again since it requires lots of stuff that is not covered like multivariate analysis, sequence prediction as well as point b ypoint prediction.

par Ethan V

6 sept. 2019

This is a good introduction to the API of keras, but that's not what I would expect from a "Tensorflow In Practice" Specialization. This is really an "Introduction to Keras" specialization, and really theory light one as well. As a graduate of the Deep Learning specialization, I expect this to be a way to apply that theory to large datasets and to novel architectures requiring some leverage of the lower level tensorflow APIs. Although I thought this course was well made, I feel it was not ambitious enough for it's name.

par Miguel L

27 mai 2020

I would leave 5 stars for the instructor. But the support you get from the forum sin minimal. There are tons of recurrent, important posts and threads unanswered...some of them even have months old. I may have posted or upvoted ten different questions and maybe received answers for three...and from fellow students who may or not may be right. That could really seem like a good place to start looking at some improvements. Not to mention the constant workarounds you have to do to successfully submit assignments.

par Justin F

28 déc. 2020

I echo some of the comments of others. The code needs to be more commented with explanations. There were details in the code that were not mentioned in the lectures or explained. When someone does not understand a particular line, then it is difficult to understand the rest of the code. The Deep Learning Specialization was much more complicated than this specialization, but I understood it better because it covered more of the details with clarity. Much of the code in this course had no comments at all.

par Ed E

9 déc. 2020

Too much focus on creating synthetic data and arbitrary code. Unlike the first three courses this was hard to follow with significant gaps in the material not explained.

Although I passed I am still unsure of what I have learnt on this course.

My advice would have been to use a real dataset from the start and build on this and eliminate all the helper functions that just really proved a distraction. This would also be a great motivator if the dataset was interesting.

par Pablo A

24 sept. 2020

Just like Course 3, Course 4 was a let down. The content is interesting but I think unlike Courses 1 and 2 it is presented in a way that is kind of plain and not really all that engaging. I also think the assignments should still be required as this adds incentive to really work hard at it. I learned a decent amount, but Courses 3 and 4 of this specialization were a disappointment.

par Yarik M R

23 févr. 2021

The materials are outdated and they are not as described as the first 2 courses (the effort and quality to make the curse is not the same as the others). The notebook from the first courses are very well documented and the ones from the last two are just code. Other than that the curse is great and well explained

par Chip J

21 mars 2020

Much preferred the Andrew Ng courses where we spent time coding specific sections of various neural nets. This ourse was practical, I guess, focusing on the mechanics of prepping data, but I don't feel it helped my understanding of the various machine learning techniques at all.

par Mushfiqur R

14 mai 2020

Some of the topics could have been described in details. There was always some kind of rush going on. By the way, I have come across several datasets and those labs introduced me to various neural network and their application using Tensorflow and Keras. Thanks to Laurence.

par Kevin H

12 juil. 2020

Graded, non-optional assignments should really be added to this, and the rest of the courses.

It would help ensure the understanding of the tools in question. Providing the answers as Colabs is nice and helpful, but does not drive you to actually try things out.

par Haoyu R

27 nov. 2019

In last week, the course gets really worse. The code are not well explained. And no tutor is there for answering the questions. For example, suddenly change the model from sequences2vector to sequences2sequences without any notification. What a shame.

par Alejandro B G

4 sept. 2019

Teacher is not anywhere close to Andrew, plus the grading tools are non-existent. It goes too heavy on preprocessing when we want to learn tensorflow, you could've spent all that time in teaching Tensorflow appart from Keras.

par Eugene Y

22 oct. 2020

Barely scratched the surface of the topic. For this particular course (alongside NLP too), I constantly had to look for more sources of information as certain aspects of the code implementation was not thoroughly explained.

par Hector B

12 juin 2020

Compared to the first two courses in the specialization, the last two lack a lot of practical coding homework, and there is really where the concepts are fixed. The course should have more graded coding excercises.

par Rajesh R

20 juin 2021

Good course but could have been better. Many obvious issues with time series forecasting / prediction were not discussed, such as the inability of the model to predict some of the peaks and troughs better.

par Asad M

23 févr. 2020

It's a relatively shallow course. They don't really dive down to the details or even don't cover whole a lot when it comes to examples, exercises or assignments. So, This is very much for the beginners.

par Igors K

23 nov. 2019

The week 2 quiz is super bad (even pro NN people didn't couldn't answer some questions cos they're badly worded).

I honestly am not a fan of getting the notebook after the video that explains it.

par peropop

4 févr. 2020

Nice course. Despite it's a practical one, you should consider to get just some deeper in the theory embedding the models you presented, to make the audience understand better what's going on.