Chevron Left
Simple Recurrent Neural Network with Keras(으)로 돌아가기

Coursera Project Network의 Simple Recurrent Neural Network with Keras 학습자 리뷰 및 피드백

4.5
별점
113개의 평가
•
15개의 리뷰

강좌 소개

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....

최상위 리뷰

필터링 기준:

Simple Recurrent Neural Network with Keras의 15개 리뷰 중 1~15

교육 기관: Pravin S

•
2020년 5월 21일

Best Understanding of Recurrent Neural Network in simplest way.

교육 기관: SENTHIL K B

•
2020년 5월 12일

Excellent planning and guidance throughout

교육 기관: Gangone R

•
2020년 7월 4일

very useful course

교육 기관: Prakash S

•
2020년 5월 31일

Excellent tool

교육 기관: Kamlesh C

•
2020년 6월 21일

Thanks

교육 기관: Abel F Z C

•
2020년 7월 9일

good

교육 기관: Vajinepalli s s

•
2020년 6월 20일

nice

교육 기관: Ashwin P

•
2020년 5월 12일

good

교육 기관: Daniel S R

•
2020년 7월 13일

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

교육 기관: Mohammed B

•
2020년 6월 24일

good

교육 기관: Mónika J

•
2020년 5월 13일

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.

교육 기관: Salil M

•
2020년 5월 22일

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

교육 기관: M V

•
2020년 9월 25일

Awesome course, really learnt a lot !

교육 기관: Pramod H K

•
2020년 7월 26일

Very good and simple intro to RNN.

교육 기관: Dr R S

•
2020년 5월 10일

Will learn the PYTHON soon and get expert in this.

Coursera Footer

경력을 시작하거나 쌓기

인기 있는 주제 찾아보기

Popular courses and articles

온라인으로 학위 또는 자격증 취득

Coursera

커뮤니티

기타

어디에서나 학습
앱스토어에서 다운로드하기구글 플레이에서 받기
Placeholder