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Вернуться к Convolutional Neural Networks in TensorFlow

Отзывы учащихся о курсе Convolutional Neural Networks in TensorFlow от партнера

Оценки: 7,431

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 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 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


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..


11 сент. 2019 г.

great introductory stuff, great way to keep in touch with tensorflow's new tools, and the instructor is absolutely phenomenal. love the enthusiasm and the interactions with andrew are a joy to watch.

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451–475 из 1,158 отзывов о курсе Convolutional Neural Networks in TensorFlow

автор: Anwar S

21 сент. 2020 г.

very good contents in the right pace and direction

автор: Ed M

23 сент. 2019 г.

Amazing practical examples and great use of TF 2.0

автор: Бибик М В

29 янв. 2021 г.

very good course, best teacher, interesting tasks

автор: Anubhav

21 авг. 2020 г.

Great course to experience CNNs using Tensorflow.

автор: Jifan Z

19 авг. 2020 г.

Better than the first course. Hope to learn more.

автор: Hieu N

24 дек. 2019 г.

Another extraordinary course from

автор: Asad M

5 мар. 2021 г.

Loved the simple and to the point explanations !

автор: Kush S

24 мая 2019 г.

It is one of the best courses likewise course 1.

автор: Meet M V

28 мая 2021 г.

Learned a lot from this course, made it simpler

автор: Shaukat

28 июня 2020 г.

Very well paced course, learned a lot about CNN

автор: Hadj S Y

29 апр. 2020 г.

Great Course! So well structured and explained.

автор: Sk S

13 апр. 2020 г.

It was too much of a learning !!! Great Content

автор: Aditya J

23 мар. 2020 г.

one of the best course I have done on Coursera.

автор: Aritra R G

6 мар. 2020 г.

A great place to start with CNN and tensorflow.

автор: Philippe B

26 янв. 2020 г.

Très bon cours sur les réseaux convolutionnels.

автор: Michalis F

25 сент. 2019 г.

simple, to the point and good notebooks! thanks

автор: TEJAS P

8 июля 2020 г.

Extremely Practical Course ! Really Enriching!

автор: Washing L

16 сент. 2019 г.

Very easy to learn. Very practical and useful!

автор: Robin C

30 сент. 2021 г.

This programme has benefited me a great deal.

автор: nilo b m

13 сент. 2020 г.

Gostei muito do curso, aprendi muitas coisas.

автор: Bohdan K

27 авг. 2020 г.

The course is professionally made. Well done!

автор: Abhiroop A

9 мая 2020 г.

Its an amazing course. 10/10 would recommend

автор: Demaison F

5 мая 2020 г.

progressif et complet. Exercices intéressants

автор: Seeon s

6 янв. 2020 г.

very very useful and powerful toll i learn :)

автор: Bằng P C

30 мая 2020 г.

good course for understand cnn in tensorflow