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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: 6,988
Рецензии: 1,088

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.

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:

1026–1050 из 1,088 отзывов о курсе Convolutional Neural Networks in TensorFlow

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.

автор: Li P Z

19 янв. 2020 г.

If you have taken Andrew's courses in ML or deep learning, you will be disappointed. The amount of content in the videos and exercises is shrunk down by 75% per week. I think a much better job could have been done of structuring the course, and creating meaningful exercises. The instructor does an OK job of showing you how to use TF, but he doesn't always explain things very clearly, and doesn't always have an accurate understanding of how ML or deep learning works.

автор: 黃文喜

7 июня 2020 г.

Content is really useful, but the assignment is really really bad and not user friendly(actually it drives me crazy). For example, instruction is not clear, parameter is outdated(still use 'acc' for accuracy?), assignment cannot be graded not because of modeling. These inconvenience obscure of the importance of learning CNN in TF. For this reason I don't think this course worth more than 3 stars.

автор: Rishi R

26 июля 2020 г.

This course could have covered many more topics in detail, like visualizing individual layers, performing style transfer, saving and loading models, etc. All these were skipped and weeks were wasted on a simple extension of a small concept (image augmentation and multi-class learning) which anyone who glanced at the Keras API could have learnt. I am disappointed at this course frankly.

автор: Tran N M T

5 июля 2020 г.

Really a bad course. Most of the materials can be found online for free on TensorFlow official documentations. Many practices are outdated. Problems with the coding assignment are a nightmare. There is no supervisor to answer many common questions. The code grader checks for very particular things and instructions were not clear at all. In general, this is a pretty bad course.

автор: Ian P

18 февр. 2021 г.

The first and fourth graded assignments were not very well posed. The grader in the 4th graded assignment kept running out of memory. The instructors do not get back to people in the forums. There was not much actual new material: most of the 4 weeks of material could have been covered in a single week. This has been the most discouraging coursera course i have taken.

автор: Ayush M

8 дек. 2020 г.

Course Material not detailed enough and expected more from it. It does not contain enough variety in exercises and lacks a lot of concepts.

Anyone with good learning (and "overfitting") can complete 1 course in a day.

Final assignment lacked a lot of use case description and it did not even tell us anything about the data or recommended parameters for training.

автор: Cristián A P I

8 мар. 2021 г.

There was a lot of repetition with respect to the previous course, making this one feel a bit like filler. The exercises were poorly designed: they used functionality that was not taught during the course, were badly explained and often had weird passing conditions, to the point that I felt I was fighting with the grader instead of trying to improve my code.

автор: Daniel N

13 авг. 2020 г.

Far to simple. Significant concepts were glossed over and the exercises were mainly copy and past from the examples. Lessons that covered a "week" took < 1 hour with a couple minor points learned. Don't recommend if you want to really know how CNNs work.

автор: Dhruva G

21 авг. 2019 г.

The content could have been covered in 15 min. Moreover, I thought you guys will teach tensorflow low level API and estimators etc. atleast in course 2. Also, what happened to the graded assignments ? I finished this course in 40 min.

автор: Stephen M

4 мая 2020 г.

The course simply does not cover much information. The whole course could be compacted into a decent one hour lecture. Andrew Ng has some great courses on machine learning but I don't believe this to be one of them.

автор: Nils-Jörn

15 июля 2021 г.

I was wasting my time with small coding obstacles for setting up the data (missing hints ?!) instead of getting teached on how to implement models in various ways and how to use regularization for real...

автор: Kalana I

5 июня 2021 г.

The course material is good and the lectures are great but rating it low to bring attention to the assignments which were old and incomplete. They really need to be updated. Specially week 4.

автор: Klemen V

7 янв. 2021 г.

In my opinion there is to little background explanation. There were errors in Programming Assignment

in week 3 and 4. I had to look in forum discussion so I could complete the exercise

автор: Dmitry T

28 дек. 2020 г.

weeks from 1-3 were perfect.

But the programming assignment on week 4 needs to be fixed. Please add hints and examples, otherwise it is just a headache

автор: Jose R

26 июля 2020 г.

No enough time spend in the actual code which limits the learning on the understanding of the concepts with implementation. Doubt how useful this is

автор: Muhammad R R M

12 апр. 2021 г.

Last exercise is so bad. It didn't even discuss about flow function, why it's need 26 last layer? isn't it should be only 3?

автор: Dmitriy S

12 нояб. 2020 г.

Absolutely awful grader. You spend most of the time figuring out errors and intentions of the grader writer.