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學生對 deeplearning.ai 提供的 Sequences, Time Series and Prediction 的評價和反饋

4.7
4,089 個評分
655 條評論

課程概述

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....

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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.

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.

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626 - Sequences, Time Series and Prediction 的 650 個評論(共 656 個)

創建者 Naim H

2019年11月2日

where is the assignment

創建者 Ksh N S

2020年9月15日

audio volume very less

創建者 Gerardo S

2019年10月1日

a little bit to light

創建者 Artem K

2020年9月17日

Plz more practice :)

創建者 Kai J J

2020年8月22日

A little to easy.

創建者 Neshy

2021年2月6日

too easy

創建者 Masoud V

2019年8月23日

Good

創建者 Leonardo

2020年12月21日

I have done the initial Deep learning courses of Andrew, and they were very thorough and well explained. I was expecting the same quality, however, it was not so. Explanations were generally good, but the examples and the details around the architecture of the models were barely discussed or considered, besides pointing me to the next course (which I have done). I was a bit disappointed TBH, for an "applied" course I do not think this provides enough material to begin applying this knowledge into real life problems.

創建者 Joanne R

2019年9月8日

Really poor quality, sadly. The notebooks are full of errors, the quizzes are mostly coding questions instead of being about deeper understanding of the notions studied, and I don't think the videos are clear enough about what decisions are most important when building this type of model and how to make those decisions. Love the topic, but very disappointed, and don't think this is worth what I'm paying..

創建者 Andrei I

2021年2月13日

The course is merely a walk-through some Jupiter notebooks of Laurence. There are no proper slides with explanation of what's going on. I also don't see much activity from the course creators on the discussion forums. It is incredibly easy to complete the course without forming any deep understanding.

The weekly programming exercises are not even automatically checked for accuracy.

創建者 Praful G

2021年5月22日

If you already have good knowledge of Neural Networks like CNN, RNN, LSTM, etc. then only opt for this one. Because they keep referring to previous courses in the specialisation for these. Also, they are only writing the code but never cleared about, what they are writing and why.

創建者 Ebdulmomen A

2020年9月26日

quiz's are pathetic! throughout the whole course the instructor talks about the advantages of RNN and LSTM and CNNs for time series prediction while not being able to prove this not even for one in the entire course, what a disappointment !

創建者 Kaushal T

2019年8月5日

The course was not as detailed or in a flow like I expected from a deeplearning.ai course and the editing was also very bad, one thing was shown and something else was spoken.

創建者 Victor H

2019年9月11日

A bit too high-level with lacking explanation on intuition. E.g. Conv1D was added to LSTM layers which helped reduce loss value, but did not go into the explanation of why.

創建者 Tomek D

2020年2月29日

Course is very quick and does not cover the topics in sufficient depth - explanations and discussion are all very brief.

創建者 Yevhen D

2021年2月13日

This course will be good only for very beginners. It's not deep and challenging enough.

創建者 Sergey K

2020年10月22日

To make it better you have to develop more challenging and GRADED! exercises

創建者 Sujin S

2019年10月5日

Poor audio quality.. Cant even hear in full volume

創建者 Gabor S

2020年6月25日

Very bad quizzes, no challenge whatsoever.

創建者 Bojiang J

2020年3月12日

Too much repetition in the content.

創建者 Ankit G

2020年5月21日

Could have been better

創建者 Magdalena S

2020年3月30日

Too easy.

創建者 Xiaotian Z

2020年11月25日

I do hope that the deeplearning.ai team could spend more time polishing the materials instead of just throwing the Tensorflow docs/sample codes and going through them superficially. Please also change the instructor as I really doubt his professionalism/experiences in ML practices despite his titles. Please, please don't ruin your brand, deeplearning.ai. I wish to see more in-depth courses like the ones taught by Andrew.

創建者 Robert

2021年4月2日

Maybe I had wrong expectations from this course. But to me it felt like the material in this course was extremely superficial. I was hoping to learn something, but it turned out to be a very basic overview of the material. Everything boiled down to "compile + fit" without the explanation of nuances associated with time-series settings.

創建者 Brad N

2020年9月21日

The last two parts of this 'specialization' were pretty much useless. Here's some code, let's look at the code three times, let's take a kindergarten quiz, let's look at the same code again, here's the answer you can copy if you bother doing the exercise.