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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: 6,214
Рецензии: 963

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:

701–725 из 956 отзывов о курсе Convolutional Neural Networks in TensorFlow

автор: Subham S

23 дек. 2019 г.

The course content was quite good and overall understandable but the exercises and quizzes were quite easy, they could have been more challenging

автор: Pray S

23 апр. 2020 г.

The hand sign assignment need more explanation about using flow object from ImageDataGenerator since I just know only flow_from_directory object

автор: DING T K

14 февр. 2021 г.

Learn a lot for CNN in this course, but require advance knowledge in Python & Numpy to understand the code as it was not explain in the course.

автор: 屈佑平

28 нояб. 2020 г.

Exercise_4_Multi_class_classifier_Question-FINAL has problem if you entirely follow the tips, you can find the correct code in the forums.

автор: Gianluca T

17 сент. 2020 г.

Very nice and interesting videos, cool concepts, amazing datasets. Exercises lack sometimes clear objectives, or provide unclear feedbacks

автор: Rodolfo V d A

10 июля 2020 г.

I guess one thing was not studied, the method .flow() which get the images generated by keras with the dataset labeled for the final test.

автор: Shubham S

6 дек. 2020 г.

Lectures videos are amazing but the only problem in programming assignments. Programming assignments should have been properly designed..

автор: Abhiram

12 мар. 2020 г.

More detail videos or links,examples for important techniques like dropouts and for like multi class classification which may be optional

автор: Madhu

13 дек. 2020 г.

Content was great. It was insightful. However I felt, in few assignments, the instructions were misleading and took some to figure out.

автор: Ghifari A F

14 мая 2020 г.

The course is very useful for practical purposes. But this course didn't cover some advanced topics such as object detection and GAN.

автор: Nikos R

19 окт. 2020 г.

Very good course to get you started on convolutional neural networks. Week two had a small problem with the programming assignment.

автор: Brian ( B

23 окт. 2019 г.

very practical courses on implementation of CNN in tensorflow. Suggest student also take the deeplearning series with this series.

автор: Revant T

20 мая 2020 г.

Programming assignments could have been better. The programming assignments at the end of each week were not challenging enough.

автор: Hang N

26 февр. 2020 г.

This course offers more executable functions than actually helping you understand (in-depth) how neural network really works.

автор: Przemysław D

27 сент. 2019 г.

Instructors are really good, but in my opinion, this course should contain Object Detection and Object Segmentation topis.

автор: Balaji K

7 авг. 2020 г.

Short, crisp and to-the-point discussions... Complex practice exercises designed as easy ones for learners. Well done !

автор: Bhabani D

5 янв. 2020 г.

Great introductory course to learn the application of TensorFlow with Keras in the field of Convolutional Neural Network.

автор: Ricardo F

31 мар. 2020 г.

Excelent course! The examples could be more elaborated, but this is a minor issue. Congratulations, and thanks a lot!

автор: Benjamin S

12 окт. 2020 г.

A lot of learning material to digest but at the the end of the course you really have the feeling you've progressed.

автор: Wiput T

29 июля 2020 г.

It the great course and good explainataion. Tensorflow 2.0 is quite good for quick development tool for AIT project

автор: Josian Q

1 мая 2020 г.

Overall it was really good. Some of the code needed tweaking and the final test was not so easy to get right.

автор: Jaap d V

11 мар. 2020 г.

Thank for a great course. Please make the code samples it work for TF2.X the TF1.X has run out of shelf life

автор: Kevin H

12 апр. 2020 г.

End assignment uses skills not practiced in the course itself. Otherwise very smooth and well presented.

автор: Carlos D R

14 февр. 2020 г.

Es una gran continuación al curso anterior. No se puede hacer sin haber hecho previamente el otro curso

автор: Arpit G

7 янв. 2021 г.

Good and clear explanation of the basic concepts . But assignements quality can be certainly improved.