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Отзывы учащихся о курсе Convolutional Neural Networks in TensorFlow от партнера

Оценки: 6,611
Рецензии: 1,030

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

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!

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.

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676–700 из 1,021 отзывов о курсе Convolutional Neural Networks in TensorFlow

автор: Ben B

6 мая 2020 г.


автор: Aji S

3 мая 2020 г.


автор: Alivia Z

25 апр. 2021 г.


автор: Roberto

16 апр. 2021 г.


автор: Ahmad H N

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: 林韋銘

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: 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: 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.

автор: Mikhail C

6 апр. 2020 г.

Content was clear building upon each topic however the lab submissions need work. Most of the "write your own code" complexities and issues where around data wrangling, directories, and memory efficient code which was not too relevant to the main learning objectives. I spent 90% of the coding exercises fixing or waiting for the data prep functions instead of experimenting with the different layers, dropouts, augmentation values.

автор: Henrique G

24 июня 2020 г.

The course is well-paced and the instructor provides good coverage on the main topics on Convolutional Neural Networks. I'd recommend watching Andrew Ng videos from the Deep Learning specialization for a better understanding of topics like dropout, transfer learning, and optimization methods. The final exam is quite difficult as you need a lot of trial and error to get things to work properly - just like the real messy world.

автор: Jennifer J

16 июля 2020 г.

Whilst I very much enjoyed playing around with convolutional neural networks, transfer learning and using image transformation to augment standard convolution, this course lacked an proper introduction in how to use python and will require a course into python or a good python language reference book which should help you build the necessary functions for completing the tasks required. Otherwise, this was a great course!

автор: Bob K

29 мар. 2020 г.

As another reviewer mentioned, this course is much simpler than Andrew Ng's deep learning specialisation but even so it has it's uses. I'm taking it to prepare for the Google TensorFlow certificate and it's forcing me to learn more of the api.

Andrew Ng's course was how to implement

the theory from papers, whereas this course is how to use TensorFlow. Each has it's place, although the former is probably more valuable.