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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: 6,833
Рецензии: 1,061

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:

926–950 из 1,057 отзывов о курсе Convolutional Neural Networks in TensorFlow

автор: Salih K

9 нояб. 2020 г.

The course itself is really good; however, homework problems at the end of the chapters are very unorganized. There is almost no guide at all. You may end up spending hours while trying to figure out why grader is having problems or your model's accuracy is very low.

автор: Varun C

10 июля 2020 г.

Giving it 3 stars because of the last week's assignment. There is little to no information about the dataset and the learner is just expected to know how to deal with the data. No information on how many classes to expect as output and other necessary information.

автор: Ambroise L

29 дек. 2019 г.

What could improve it: Not enough depth in the practicals if you have already done Andrew Ng's course on Conv nets. No graded practical exercise.

What was good: Clear examples, Good setup to experiment with the algorithms & Speak explains concepts very clearly,

автор: Ignacio R L

28 мар. 2020 г.

Good course, but the notebooks need a deep review to fix the problems related to balance between the requirements of the exercise and the resources available also a better explanation of the exercise aims would be a nice to have to avoid misunderstandings

автор: Michael R

18 сент. 2019 г.

Actually a great course. Only not getting more stars due to the issue encountered with the last exercise where there is an issue in loading the data files. The workbook keeps on crashing and there is no solution provided to resolve that.

автор: Matías B

28 мая 2020 г.

The material is good, but there is not much thereof.

The duration of the assignmentsis greatly exaggerated, since most of the lengths for the readings and exercises are wrong.

The course can easily be done in 25% of the official time.

автор: Dirk H

7 нояб. 2019 г.

If you have taken the first course of the specialization this class was repetitive at some points. I also did not like that there have not been graded coding problems. I still got some practice and learned some new techniques.

автор: Wenyu Y

20 мар. 2020 г.

The materials about implmentation of transfer learning is helpfu, but again, I think the whole content of the first two courses could be compressed into one week. There're really not too much new things.

автор: Sumit c

18 мая 2020 г.

some clear instructions should be given for students. In exercise of week 4, there was no specific instruction about using .flow instead of .flow_from_directory, for labels we had to use to_catagorical.

автор: Amir S

24 мая 2020 г.

Course assignments need a good overhaul. The two environments to practice the assignments (Jupyter workbooks and Google Colab) are not consistent, one throws an error while the other one works fine.

автор: Nermeen M M

13 дек. 2019 г.

Very good course but please consider reordering the videos and reading especially in week 3. It is better to discuss the code in the video before moving to the notebook not the opposite.

Thank you

автор: Ashok N

26 июня 2020 г.

Course content was super nice.

But exercise organization is very annoying. not at all satisfied with the exercises. sometimes not loading and sometimes is really annoying . very disappointed

автор: Renjith B

15 июля 2019 г.

Good content for classification tasks. But didn't cover anything related to object recognition, localisation and semantic segmentation which are the challenging computer vision tasks.

автор: Luis S

9 февр. 2021 г.

The essential of convolutional neural networks is covered by this course although there ais unnecessary code in the examples and a lack of explanations especially in the assignments.

автор: Yuvraj G

11 апр. 2020 г.

Too basic course. If its a practical course, then there should be exposure to more functionality of keras and not just the basic one which can be done from a blog/documentation.

автор: Ted T

2 янв. 2021 г.

Lawrence's lectures were good, but exercises were disconnected from course material. Having to do exercises in Google Colab and then redo in Jupyter notebook was inefficient.

автор: Andrei I

13 февр. 2021 г.

Too easy. One can finish all exercises without learning much. The quality of explanations is poor. The whole course is but a short walk through Laurence's Jupiter notebooks.

автор: Andrea B

1 июня 2020 г.

the topic is interesting, and the course is quite hands-on, but the treatment of the subject is extremely basic. Videos are too short and somehow superficial and incomplete

автор: Michele M C

18 февр. 2021 г.

cnn implementation theory should be covered better, giving more reason why the code is written this way, furthermore the last homework of the course was bad designed

автор: Seif M M

20 сент. 2020 г.

very good course, but think it needs to go deeper in the functions and tools in tensorflow for conv netwroks, i have the feeling that the course is somehow shallow.

автор: Adnan

8 июня 2020 г.

It was a great course, but in my opinion, it could have been even better if it involved more concepts & APIs to explore apart from the most in-use TensorFlow APIs.

автор: Ethan V

17 авг. 2019 г.

Solid content, but it feels like it's not *very* much on top of the first course in this specialization. I think these two courses could be combined into one.

автор: Madhav A

16 окт. 2019 г.

The course is good for beginners as it is very basic. It needs more advance topics like Detection using TensorFlow. Have a lot of scope for improvement.

автор: Moeen T

19 февр. 2021 г.

There wasn't enough useful content. There were also many problems with the programming assignments, specially in the last week's assignment.

автор: Alejandro B G

3 сент. 2019 г.

Google colab system for tasks is pretty bad, no control on the tasks plus it erases and u can't prove you did the work unless you save it