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Отзывы учащихся о курсе Convolutional Neural Networks in TensorFlow от партнера

Оценки: 7,642

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 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 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


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.


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..

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776–800 из 1,178 отзывов о курсе Convolutional Neural Networks in TensorFlow


28 июня 2022 г.


автор: M N k

10 авг. 2021 г.


автор: Ken T

8 авг. 2021 г.


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19 июня 2021 г.


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25 апр. 2021 г.


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26 мар. 2021 г.


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19 мар. 2021 г.


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17 янв. 2021 г.


автор: Johnnie W

22 сент. 2020 г.



25 июля 2020 г.


автор: Rifat R

7 июня 2020 г.


автор: PANG M Q

29 мая 2020 г.


автор: Amit K

13 мая 2020 г.


автор: Nho N

17 мар. 2020 г.


автор: zhenzhen w

18 нояб. 2019 г.


автор: Jurassic

6 сент. 2019 г.


автор: Ming G

20 авг. 2019 г.


автор: Islam U

24 янв. 2021 г.

The course definitely teaches interesting techniques (Dropout, Transfer Learning) and tools (use of ImageDataGenerator). What i think would be an improment point is further tips on how to actually achieve a state of art (or really high quality) models. For example for full Cats and Dogs dataset from Kaggle, there was an optional ungraded work that asked to achieve over 99.9% accuracy on both training/validated datasets. It would be great if some tips on how to achieve this would be given. Maybe some discussion of network architectures that can achieve this, as this subject is not always covered, while it plays probably a dominant role whether you make it or break it. Otherwise, i liked the course and thanks for wonderfull explanations.

P.s. week 4 final graded task is structured suboptimally, so maybe it can be reviewed, as many people struggling with many sorts of errors.

автор: Todd R

2 апр. 2022 г.

I was glad they finally showed up how to do a classification involving multiple objects instead of just recognizing horses or humans, cats or dogs. Coursera is one of the few places to be exposed to the solution of such a recognition problem. I thought the final assignment required heavy python knowledge, but that wasn't explained in the course outline. Remember that the assignments should not take more than 1 hour to execute the program. I had a program that took four hours to read in 20000 plus csv data points. I wasn't doing the assignment correctly. The discussion forum helped me.

автор: Xiaolong L

20 февр. 2021 г.

In general a very good course. But it seems that the instructor could have put more work into the weekly projects. For example, the weekly project for the 3rd week is almost the same as that of week 2. Also, one of the week boasted that the project involves training on the full dogs vs cats dataset but it is actually still just a subset. I was able to run on the full data set by downloading and loading them manually. I can see that the platform has concerns on the computational resources usage, but it should at least be accurate on in the project descriptions.

автор: Manutej M

20 мая 2021 г.

Week 4 was a rather challenging exercise and was out of left-field compared to the pace of the other exercises. This last exercise felt more like a "final exam" There were several things not mentioned in depth in the class that could have aided in bolstering the understanding necessary for the labs and for the real world. The class can get a bit repetitive and narrow sometimes in its focus and perhaps that's for simplicity, but I believe people could benefit from more depth being taught in the course.

автор: João A J d S

3 авг. 2019 г.

I think I might say this for every course of this specialisation:

Great content all around!

It has some great colab examples explaining how to put these models into action on TensorFlow, which I'm know I'm going to revisit time and again.

There's only one thing that I think it might not be quite so good: the evaluation of the course. There isn't one, apart from the quizes. A bit more evaluation steps, as per in Andrew's Deep Learning Specialisation, would require more commitment from students.

автор: Anand H

12 сент. 2019 г.

One challenge i have faced is with deploying the trained models. I find very little coverage on that across courses. It's one thing to save a model.h5 or model.pb. It would be nice if you can add a small piece on deployment of these models using TF Serving or something similar. There is some distance between just getting these files outputted and deploying. TF documentation is confusing about some of these things. Would be nice if you can include a module on that.

автор: AbdulSamad M Z

1 авг. 2020 г.

Great course! Builds on the concepts of Course 1 in this Specialization although the course can be taken without having completed Course 1. Concepts are explained in a super clear and engaging way and the hands-on exercises give you the experience you need to become proficient. The course covers plenty of practical concepts including some pitfalls for practitioners to avoid, but the theoretical concepts are covered less than I expected.