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Avis et commentaires pour d'étudiants pour Anomaly Detection in Time Series Data with Keras par Coursera Project Network

192 évaluations
43 avis

À propos du cours

In this hands-on introduction to anomaly detection in time series data with Keras, you and I will build an anomaly detection model using deep learning. Specifically, we will be designing and training an LSTM autoencoder using the Keras API with Tensorflow 2 as the backend to detect anomalies (sudden price changes) in the S&P 500 index. We will also create interactive charts and plots using Plotly Python and Seaborn for data visualization and display our results in Jupyter notebooks. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and Keras pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions....

Meilleurs avis

8 juin 2020

It is good. step by step so I can understand. but unfortunately there are no subtitle.

18 mai 2020

I love how well he explained everything and made it simple to follow

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26 - 42 sur 42 Avis pour Anomaly Detection in Time Series Data with Keras

par Reinhold L

2 mai 2020

Useful example

par Josafat E

31 juil. 2020

The greatest

par Santiago G

21 sept. 2020


par V. V J

15 sept. 2020


par k j

1 juil. 2020


par sarithanakkala

25 juin 2020


par p s

21 juin 2020


par Rifat R

7 juin 2020


par Ashwin P

21 mai 2020


par George X H

23 juin 2020

A little bit of more explanations on the autoencoders on what each components and each line of code does will help. Also a little bit of summary on what the results means for S&P data would be better too. For example, anomalies that we detected does not just mean sudden jumps in S&P closing price levels, it means the changes that are not predicted by our neural network. So if there's a big jump on index prices, if it's predicted by our RNN it wouldn't count as an anomaly.

par Amitesh S

24 juil. 2020

The project was useful. Rhyme's interface needs testing and an upgrade.

par Keith N

12 oct. 2020

should spend time on explaining LSTM and Sequence model.

par Md A R

9 juin 2020

Good.. Need more explanation of code...

par Ben M

9 juil. 2020

Not enough description of the theory or the methods used. Found myself just writing the code out rather than understanding what I was doing which is not as useful as it could be. Make the session 1 hour 30 mins and spend the extra 30 mins letting learners know what they are actually doing rather than just following along

par Harsh K

6 août 2020

The instructor should explain more about the project. Specifically explaining some basic concepts first and should mention the use of projects in real life. Like how should you know, when to use this project!

par Simon S R

4 sept. 2020

hardly any explanation for the given model is provided. There are plenty of other tutorials on the web that go into more details.

par Emiel V

2 juil. 2020

The course is too short and specific to get an in depth understanding of anomaly detection with autoencoders.