Chevron Left
Вернуться к Convolutional Neural Networks in TensorFlow

Отзывы учащихся о курсе Convolutional Neural Networks in TensorFlow от партнера

Оценки: 7,651

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


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!


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:

901–925 из 1,179 отзывов о курсе Convolutional Neural Networks in TensorFlow

автор: Antoine A

2 янв. 2021 г.

Good course overall, but the exercise of the end of week 4 was really badly conceived/explained

автор: Ana C

25 янв. 2020 г.

Very interesting course. I've learned a lot. I would like to have a little more advanced topics

автор: Tudor M

11 мар. 2021 г.

Some assignments are quite vague and can take up a lot of time to findout what the problem is.

автор: Yuliya M

6 янв. 2020 г.

It's a wonderful course! Pity that the data for the fourth exercise are not available anymore.

автор: Dr. H H W

5 сент. 2019 г.

Great insight for the practical aspect of TensorFlow, add value on top of Andrew's DL courses.

автор: Shivakeshavan

7 авг. 2020 г.

Course was great but instructions for the coding exercises could be a little more explicit.

автор: Andy L

23 мар. 2020 г.

A lot of repetition. Stack traces, and error messages in the notebooks are less than clear

автор: Luis M

14 сент. 2021 г.

For the most part it is easy but the project really makes you put effort and mind into it.

автор: chintamani m

27 мая 2021 г.

Enjoyed this course thoroughly by getting loads of useful tips and training from Laurence

автор: Fadhel A

25 янв. 2020 г.

I hope it will be upgraded with graded weekly test (case study and building some models).

автор: Marcos V G J

25 сент. 2019 г.

Good content, but lacks exercises that forces us to code ourselves to solve the problemas

автор: CARLOS R H

14 янв. 2021 г.

Incredible course. Only comment is that the last exercise wasn't explained very clearly.

автор: Shaun K

10 мар. 2020 г.

Found course a little lighter compared to other deeplearning courses. But great overall.

автор: Lei W

30 окт. 2019 г.

will be nice to have non-third party programming exercises that are graded by Coursera

автор: Donal B

18 окт. 2019 г.

Excellent course. Would have liked graded coding assignments like in the first course.

автор: Heinz D

21 нояб. 2021 г.

Good lecturer, interesting content. But do not expect to cover the subject in depth.

автор: Huan G

6 июня 2021 г.

last assignment involve other scope such as numpy, hence more hints would be helpful

автор: Eduardo Z

24 июня 2020 г.

Great! Had some difficulties with the last excercise. But the rest was OK. Thanks!

автор: Philip L

14 нояб. 2019 г.

More exercises should be available for students to practice and test their skills.

автор: Eran Y

20 июня 2020 г.

Some references to relevant code in tests would be nice, don't send me searching.

автор: Gregor F

7 янв. 2020 г.

Straight to point with a lot of useful techniques! Interesting examples as well.


26 авг. 2020 г.

Nice course to learn about the basics about how to implement CNN's with keras.

автор: Sebastián M A

6 дек. 2019 г.

No le pongo las 5 estrellas por la falta de ejercicios prácticos calificables.

автор: Arun P R

18 апр. 2020 г.

Need to more specific about the instructions given for Programming Assignment

автор: Damon W

8 окт. 2019 г.

Good practical course. A bit heavy on visual images, but very informative.