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Comentários e feedback de alunos de Siamese Network with Triplet Loss in Keras da instituição Coursera Project Network

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Sobre o curso

In this 2-hour long project-based course, you will learn how to implement a Triplet Loss function, create a Siamese Network, and train the network with the Triplet Loss function. With this training process, the network will learn to produce Embedding of different classes from a given dataset in a way that Embedding of examples from different classes will start to move away from each other in the vector space. 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 (e.g. Python, Jupyter, and Tensorflow) pre-installed. Prerequisites: In order to be successful in this project, you should be familiar with Python, Keras, Neural Networks. 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....

Melhores avaliações

AG

16 de jun de 2020

I like the way we got involved into practice by setting goals which are a bit challenging yet we want to achieve successfully.

NB

2 de ago de 2020

worth enrolling!! checkout in detail about this project even after completion

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1 — 19 de 19 Avaliações para o Siamese Network with Triplet Loss in Keras

por Isra P

12 de abr de 2020

por Joerg A

27 de mai de 2020

por Abhishek P G

17 de jun de 2020

por Luis A G L

22 de set de 2020

por Nittala V B

3 de ago de 2020

por Fabian L

14 de jun de 2020

por Angshuman S

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por XAVIER S M

2 de jun de 2020

por Doss D

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por Sourav D

31 de mai de 2020

por Santiago G

5 de nov de 2020

por sarithanakkala

24 de jun de 2020

por Qasim K

4 de dez de 2021

por Siddhesh S

20 de abr de 2020

por Sri C

4 de dez de 2020

por Simon S R

4 de set de 2020

por Jorge G

25 de fev de 2021

por Yannik U

16 de mar de 2022

por Molin D

8 de ago de 2020