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Retour à Image Understanding with TensorFlow on GCP

Avis et commentaires pour d'étudiants pour Image Understanding with TensorFlow on GCP par Google Cloud

471 évaluations
53 avis

À propos du cours

This is the third course of the Advanced Machine Learning on GCP specialization. In this course, We will take a look at different strategies for building an image classifier using convolutional neural networks. We'll improve the model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while trying to avoid overfitting our data. We will also look at practical issues that arise, for example, when you don’t have enough data and how to incorporate the latest research findings into our models. You will get hands-on practice building and optimizing your own image classification models on a variety of public datasets in the labs we’ll work on together. Prerequisites: Basic SQL, familiarity with Python and TensorFlow...

Meilleurs avis

28 oct. 2020

a real eye opener education, it gave me lots of answers to the questions i had in this area. it is just amazing that ML can differ between roses and tulips !

10 déc. 2019

Great course, great team, First week is as it was expected but second is week is outstanding. The Neural Architecture search(NAS) is outstanding. Super job.

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26 - 50 sur 51 Avis pour Image Understanding with TensorFlow on GCP

par Gregory R G J

30 avr. 2019

Thumbs Up!

par Kamlesh C

18 juin 2020

Thank you

par Anshumaan K P

22 nov. 2020



26 juin 2019


par Atichat P

4 oct. 2018


par Juanjia Y

19 avr. 2020

I have to manually logging in gcp everytime for each lab and that is painful. Labs could be made more user friendly. Also I was anticipating that there's some code demo in the 2nd week can show me how to import a pre-trained model for object detection but the class only shows how to use these model on gcp platform.

par Ed E

30 déc. 2019

Fairly basic course and would have liked more guidance on setting up jobs for processing including sample JSON requests etc. However was interesting but definitely not challenging.

par Guillermo M

22 août 2019

I would like to work with TPUs in one laboratory. Also, I would like to see how the pattern of image was formed throught the convolutional neural network in a lab.

par Carlos V

8 déc. 2018

The course provides an excellent overview of Image Understanding with TF and the utilization of all the capabilities of GCP to build productionable image systems.

par James H

24 févr. 2020

Some of the content could not be completed/needs to be updated. I ran into a few bugs/errors, but still wanted to learn the content/gain the certificate.

par Pratima S

5 mars 2020

This course is very helpful if any one working with image processing it solves many real time problem occurs during image collection and processing.

par Vishrut K

20 mai 2020

The course is good however for 2020 it seems out of date as it uses TensorFlow v1 which is way more complex as compared to the last east v2.

par Mirko J R

4 avr. 2019

You should improve the explanation of Transfer Learning from prebuilt models like ResNet. The conceptual side is not clear.

par Roopesh N

23 déc. 2018

Good Practical Experience with the concepts what that I learned . Good for recommending my friends.

par Abhishek S

22 nov. 2019

Great TPU Exploration.

Mr LEK Is very cool and his explanation about the topic is sound easy.

par Md A A M

14 juil. 2020

Everything is good except Lab of Transfer learning was missing.

par Armando F

18 mai 2019

Highly recommended

par Nikhileshkumar I

15 sept. 2019

greate course.

par Hemant D K

27 nov. 2018

Good material

par George V

12 avr. 2020


par Jeramia P

7 août 2019

Many things have changed in the labs and the instructions are no longer as clear and relevant as in other courses.

par German M

4 août 2020

Overall good effort. Some of the labs at week 2 should be adapted to the TF 2.X specs.


30 déc. 2019

Please improve this course content

par Danilo D

17 nov. 2019

Many labs have not been updated

par Kartik .

21 sept. 2019

code not explained correctly