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Retour à Image Understanding with TensorFlow on GCP

Avis et commentaires pour l'étudiant pour Image Understanding with TensorFlow on GCP par Google Cloud

4.6
297 notes
•
35 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

PR

Jul 24, 2019

Amazing course! Definitely recommend the course for learning Google's way to handle images! ;)

BS

Jan 23, 2019

It was One of the great course having labs which was really fun

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26 - 34 sur 34 Examens pour Image Understanding with TensorFlow on GCP

par Hemant D K

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Nov 28, 2018

Good material

par Mirko J R

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Apr 04, 2019

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

par Armando F

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May 18, 2019

Highly recommended

par José G M

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Aug 22, 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 Nikhileshkumar I

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Sep 15, 2019

greate course.

par Abhishek H S

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Nov 22, 2019

Great TPU Exploration.

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

par Jeramia P

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Aug 07, 2019

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

par Kartik .

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Sep 21, 2019

code not explained correctly

par Danilo D

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Nov 17, 2019

Many labs have not been updated

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