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Вернуться к Simple Recurrent Neural Network with Keras

Отзывы учащихся о курсе Simple Recurrent Neural Network with Keras от партнера Coursera Project Network

4.5
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Оценки: 113
•
Рецензии: 15

О курсе

In this hands-on project, you will use Keras with TensorFlow as its backend to create a recurrent neural network model and train it to learn to perform addition of simple equations given in string format. You will learn to create synthetic data for this problem as well. By the end of this 2-hour long project, you will have created, trained, and evaluated a sequence to sequence RNN model in Keras. Computers are already pretty good at math, so this may seem like a trivial problem, but it’s not! We will give the model string data rather than numeric data to work with. This means that the model needs to infer the meaning of various characters from a sequence of text input and then learn addition from the given data. 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 Tensorflow pre-installed. Please note that you will need some experience in Python programming, and a theoretical understanding of Neural Networks to be able to finish this project successfully. 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....

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1–15 из 15 отзывов о курсе Simple Recurrent Neural Network with Keras

автор: Pravin S

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21 мая 2020 г.

Best Understanding of Recurrent Neural Network in simplest way.

автор: SENTHIL K B

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12 мая 2020 г.

Excellent planning and guidance throughout

автор: Gangone R

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4 июля 2020 г.

very useful course

автор: Prakash S

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31 мая 2020 г.

Excellent tool

автор: Kamlesh C

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21 июня 2020 г.

Thanks

автор: Abel F Z C

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9 июля 2020 г.

good

автор: Vajinepalli s s

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20 июня 2020 г.

nice

автор: Ashwin P

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12 мая 2020 г.

good

автор: Daniel S R

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13 июля 2020 г.

Good guided course. I would add a quite more deep details in the model architecture to understand better how are the inputs and the outputs of each layer in the RNN model

автор: Mohammed B

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24 июня 2020 г.

good

автор: Mónika J

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13 мая 2020 г.

I think that the explanation on the code is not enough for beginners and that it mostly depends on the student's background and effort wether they understand it or not.

автор: Salil M

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22 мая 2020 г.

The knowledge about RNN was average, it was mainly focusing on data processing for RNN use, can be improved by using RNN more rigorously

автор: M V

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25 сент. 2020 г.

Awesome course, really learnt a lot !

автор: Pramod H K

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26 июля 2020 г.

Very good and simple intro to RNN.

автор: Dr R S

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10 мая 2020 г.

Will learn the PYTHON soon and get expert in this.

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