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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: 6,799
Рецензии: 1,057

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

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..

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!

Фильтр по:

976–1000 из 1,051 отзывов о курсе Convolutional Neural Networks in TensorFlow

автор: Moustafa S

26 июня 2020 г.

the same problems with the auto graders, it's exhausting sadly

автор: Pranav H

2 дек. 2019 г.

Coding exercises should be made compulsory and for the grade

автор: Eyal B

16 февр. 2020 г.

Didn't provide a real understanding for transfer learning

автор: MOUAFEK A

4 янв. 2020 г.

not much of insights or details, and it's too easy!

автор: Sandeep

22 апр. 2021 г.

The lab exercises for week 4 needs to be changed

автор: Alejo G

6 окт. 2019 г.

A lot of boilerplate code with few new concepts

автор: Mikołaj M

12 окт. 2020 г.

The course covers elementary techniques.

автор: Victor S

4 сент. 2020 г.

Useful course. Just a bit unstructured.

автор: Bojiang J

7 мар. 2020 г.

Content too easy and not engaging....

автор: Aymen M

20 мар. 2021 г.

The last assignment is malformed

автор: Navid H

15 сент. 2019 г.

I wish it had real assignments

автор: Samyak J

2 авг. 2020 г.

exercises are not very clear

автор: Paula S

6 апр. 2020 г.

course is a little too easy.

автор: Pallavi

12 мар. 2020 г.

It was not great and good

автор: Yuxuan C

12 апр. 2020 г.

A little bit too easy.

автор: Luiz C

11 июня 2019 г.

not challenging enough

автор: Victor M

19 мар. 2020 г.

Contenido superficial

автор: Igors K

26 окт. 2019 г.

I wish it used TF2.

автор: Masoud V

21 авг. 2019 г.

Useful but too easy

автор: Ruxue P

14 окт. 2020 г.

Too little content

автор: Gerard C I

20 нояб. 2019 г.

to much shallow

автор: Rob S

3 сент. 2020 г.

Good course

автор: Neshy

29 нояб. 2020 г.

too basic

автор: Mohammed I A T

21 сент. 2020 г.

just ok

автор: Thomas R

8 февр. 2021 г.

Materials were good for someone who has taken university courses on convolutional networks, but labs were extremely poorly done. Final lab of the course was missing sections for the data generator flow method calls, and augmentation wasn't even tested for. Marker could be improved and provided code can have better sections and maybe an explaining markdown at the top rather than going back and forth. I also noticed that accuracy changed from logs.get('acc') to logs.get('accuracy') which seems to be a tensorflow version issue. I feel overall like the course has been abandoned.