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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: 6,816
Рецензии: 1,059

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

JM
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.

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:

951–975 из 1,053 отзывов о курсе Convolutional Neural Networks in TensorFlow

автор: Jingwei L

30 авг. 2019 г.

The course is taught excellently. However, there are overfull file stream operations in Python that the course does not cover.

автор: Harri V

26 янв. 2021 г.

Week 4 final assignment was quite bad, because there was new Python/Numpy stuff which was not covered at all in the course.

автор: Marc-Antoine G

13 нояб. 2019 г.

Please make the "Ungraded assignment" Graded and add more comments/directive in them to make sure we understand each steps.

автор: Samuel K

2 нояб. 2019 г.

Clear explanations. Good sample codes. Too easy. Doesn't go deep enough in terms of theory. Exercises should be mandatory.

автор: Daniel D

26 мар. 2020 г.

Pros: the course teaches CNNs clearly and concisely.

Cons: the memory issues on the last assignment wasted a lot of time.

автор: David H

16 нояб. 2019 г.

Not solid enough and the exercise could be more organised. For example: some of the data downloading links didn't work.

автор: Shreenivas

22 дек. 2020 г.

Good content. The coding and assignments need significant improvement. There is no support whatsoever in assignments.

автор: Sailesh G

2 нояб. 2019 г.

Expected a lot more in this course from the Tensorflow specialization. Something that'd take us beyond tf.keras.

автор: Daniel Y

27 февр. 2021 г.

Disappointing. This is more like Python course. Deep Learning specialization CNN course teaches you x100 more.

автор: Mohammed F

6 июля 2019 г.

Could have dived more into the details and inner workings of Convolutional layers but overall awesome course.

автор: Alexey V

22 нояб. 2019 г.

complex ideas in very basic tasks that you can easily accomplish by copy-pasting from provided notebooks.

автор: Shubham A G

25 авг. 2019 г.

Lacks depth and complexity. The course is geared more towards complete newbies or high school graduates.

автор: Frank W

17 июля 2020 г.

The programming tasks are not very helpful. The main difficulty is that unknown methods should be used.

автор: Michael E

14 февр. 2020 г.

Would like to have seen some information on techniques such as batch normalization and residual layers.

автор: Apichart L

5 июля 2021 г.

Not well-design, it is very light information and knowledge to learn, unlike the #1 introduction.

автор: Nahum P

22 сент. 2020 г.

Final work had almost no connection to what you learned during the course.

Not enough hands on.

автор: Geoff G

17 авг. 2020 г.

The topics are explained very briefly.There is no in depth coverage of the mentioned topics

автор: Yu S C

26 июня 2021 г.

I dont think this course is very helpful in terms of price/how much you can learn.

автор: Sajal C

31 мар. 2020 г.

Course is good however there can be programming assignments for better practice.

автор: Javier I R

15 нояб. 2020 г.

Great course, but the last exam was miles away from the things presented on it.

автор: Ashish J

3 июля 2020 г.

this could have been better if object detection and segmentation was a part of

автор: Enyang W

6 нояб. 2019 г.

too easy! not much to learn actually. all the videos could be one lesson only

автор: vedansh s

13 сент. 2019 г.

The concepts are not as clear as in other courses.

Dissapointed a little bit

автор: Wellington B

5 авг. 2019 г.

need to watch Andrew Ng's course on deep learning before watching this one

автор: Alan K H S

25 июля 2021 г.

The final exercise its really unclear on instructions and tools to use