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Вернуться к Convolutional Neural Networks in TensorFlow

Отзывы учащихся о курсе Convolutional Neural Networks in TensorFlow от партнера deeplearning.ai

4.7
звезд
Оценки: 7,066
•
Рецензии: 1,102

О курсе

If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In Course 2 of the deeplearning.ai TensorFlow Specialization, you will learn advanced techniques to improve the computer vision model you built in Course 1. You will explore how to work with real-world images in different shapes and sizes, visualize the journey of an image through convolutions to understand how a computer “sees” information, plot loss and accuracy, and explore strategies to prevent overfitting, including augmentation and dropout. Finally, Course 2 will introduce you to transfer learning and how learned features can be extracted from models. The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization....

Лучшие рецензии

MS
12 нояб. 2020 г.

A really good course that builds up the knowledge over the concepts covered in Course 1. All the ideas are applicable in real world scenario and this is what makes the course that much more valuable!

RB
14 мар. 2020 г.

Nice experience taking this course. Precise and to the point introduction of topics and a really nice head start into practical aspects of Computer Vision and using the amazing tensorflow framework..

Фильтр по:

451–475 из 1,102 отзывов о курсе Convolutional Neural Networks in TensorFlow

автор: Pham C B

•
30 мая 2020 г.

good course for understand cnn in tensorflow

автор: Sabila H

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1 мая 2020 г.

Great, but the code exercise must be updated

автор: Matas U

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27 апр. 2020 г.

Amazing course, challenging and interesting.

автор: NITIN S T

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21 нояб. 2020 г.

Excellently instructed by Laurence Moroney.

автор: Abid H

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3 июля 2020 г.

Great experience learning this course.Bravo

автор: YASH N

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25 июня 2020 г.

Explained each and every details with ease.

автор: Charley L

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19 апр. 2020 г.

comprehensive concepts, easy to understand.

автор: Antti R

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2 нояб. 2019 г.

Very informative, nice pace, easy to follow

автор: Aashutosh K J

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19 окт. 2020 г.

Very good teacher that's all I have to say

автор: Mohammad A

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13 июня 2020 г.

assignments should contain some guidelines

автор: Radhakrishnan V

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11 июня 2020 г.

Learnt a lot in this course. Thanks a lot.

автор: Hanan S A

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3 янв. 2020 г.

code and slides are nice and crystal clear

автор: Leo

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25 июня 2019 г.

Great course for Computer Vision problems!

автор: Ameya D

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29 июня 2021 г.

Able to get good knowledge of Tensorflow.

автор: 张越兴

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24 июля 2020 г.

我太爱你了 吴恩达老师还有google这个老师 我在youyube上看过您的视频！

автор: Mats E

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8 июля 2019 г.

Very good high-level introduction course.

автор: Oleksandr M

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11 июня 2019 г.

It's very clear and useful! Thank you! :)

автор: Rich H

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27 июня 2020 г.

Fantastic! I learned so much!!! Love it!

автор: Madhumitha S

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14 июня 2020 г.

excellent explanations and lucid content

автор: Oscar S D

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5 мая 2020 г.

Excellent course with challenging tasks!

автор: thalla p

•
9 мар. 2020 г.

well planned, to dip into deep learning.

автор: winniefred m b

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16 мая 2021 г.

Loved the labs, they were very engaging

автор: Ogunkeye F

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18 июня 2020 г.

Very well detailed course. I enjoyed it

автор: S. M F

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14 апр. 2020 г.

I wish it had more weeks to learn more!

автор: Alejandro B S

•
9 апр. 2020 г.

good course, the instructe its the best

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