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

4.7
étoiles
4,248 évaluations
680 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

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.

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.

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651 - 675 sur 680 Avis pour Sequences, Time Series and Prediction

par Neshy

6 févr. 2021

too easy

par Masoud V

23 août 2019

Good

par Leonardo

21 déc. 2020

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.

par Joanne R

8 sept. 2019

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..

par Andrei I

13 févr. 2021

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.

par Praful G

22 mai 2021

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.

par Ebdulmomen A

26 sept. 2020

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 !

par Kaushal T

5 août 2019

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.

par Victor H

11 sept. 2019

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.

par Tomek D

29 févr. 2020

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

par Akiva K S

7 sept. 2021

Junk course. Andrew Ng is a great specialist but I'll never try courses from deeplearning.ai.

par Yevhen D

13 févr. 2021

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

par Sergey K

22 oct. 2020

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

par Sujin S

5 oct. 2019

Poor audio quality.. Cant even hear in full volume

par Gabor S

25 juin 2020

Very bad quizzes, no challenge whatsoever.

par Bojiang J

12 mars 2020

Too much repetition in the content.

par Anant G

28 nov. 2021

It is a surface-level introduction

par Ankit G

21 mai 2020

Could have been better

par Magdalena S

30 mars 2020

Too easy.

par Adam F

1 nov. 2021

This specialization is false advertising. It does NOT prepare you for the Tensorflow certification exam. It’s especially disappointing after taking the fantastic specialization by Andrew Ng, and makes this specialization feel like a cheap cash grab by Coursera and DeepLearning.ai. This series of courses fails to prepare you for three reasons:

1 – The certification exam is done on whatever is the current version of Tensorflow (v2.6 as of writing). You can’t expect a specialization like this to update every minor release, but much of the coding is still on the v1.X version!

2 – The certification exam requires you to work in the PyCharm IDE. The IDE doesn’t even get a mention in this specialization and it is all done through Google Colab.

3 – The material is covered at a very superficial level. I was hoping to walk out of it feeling confident in using Tensorflow on novel problems, but I’ve barely learned anything about Tensorflow that I didn’t already get from Andrew Ng’s specialization. There’re a few minutes of lectures (some weeks less than 10 minutes). The programming assignments are either pathetically easy, or lack any guidance on what to do (seriously sometimes there’s no instructions at all, you have to guess what to do by the variable names), or both.

Save your time and money and go elsewhere to learn Tensorflow.

par Oliverio J S J

21 oct. 2021

Like the previous course, this is not a four-week course but a collection of short videos that can be watched in one morning. The first week (actually the first hour) provides interesting insights about time series but the rest of the weeks (hours) are a quick review of code snippets. The pace set by a sequence of one-minute videos is excruciating and makes it difficult to follow the lectures. In addition, there are no practical assignments, just some trivial quizzes.

par Xiaotian Z

25 nov. 2020

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.

par Robert

2 avr. 2021

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.

par Brad N

21 sept. 2020

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.

par Yanghao W

17 févr. 2021

This is a quick introduction of using TensorFlow for prediction without any explanation for helping students understand the codes, the rationale, and the technical details we need to know for doing practice in daily work.