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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
Оценки: 1,798
Рецензии: 250

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

Sep 12, 2019

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.

PS

Sep 14, 2019

An excellent course by Laurence Moroney on explaining how ConvNets are prepared using Tensorflow. A really good strategy to have the programming exercises on Google Colab to speed up the processing.

Фильтр по:

176–200 из 248 отзывов о курсе Convolutional Neural Networks in TensorFlow

автор: Marco V E

Oct 31, 2019

Great intro to CNN

автор: SOURAV S

Nov 06, 2019

Thank you for your good explanation

автор: Hiten S

Nov 11, 2019

Excellent CNN Course. This Course Covers All The Topics Like Data Augmentation, Transfer Learning, Drop-Out, And Multi-Class-Classification Problem.

автор: Vivek S

Jun 24, 2019

Super cool stuff!

автор: Zhi Z

Jul 06, 2019

A good course for Keras but not for tensoflow.

автор: Oleksiy S

May 23, 2019

Exellent tutorial for using Tensorflow and convolutional networks. Useful usage examples, interesting and challenging exercises. A few minor mistakes prevent five star grading. But please note that mistakes happen and we have to live with this :-). Nice work, looking forward for the next course of the specialization.

автор: Prabesh G

May 23, 2019

Okey.. So easy but okey

автор: Humberto d S N

Jun 09, 2019

It's an great course with simple explanations about the Deep Learning topic. It's a perfect fit for beginners or those who want to have a practical review before starting using Tensorflow 2.0 with keras implemetations.

автор: Guilherme R M

Jun 10, 2019

Bom curso, muito prático.

автор: Omar M

Jul 16, 2019

Was okay

автор: Yufei M

Jul 26, 2019

I think the quiz should be harder

автор: ashraf s t m

Jul 31, 2019

Voice is low

автор: João A J d S

Aug 03, 2019

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: hamzeh a

Aug 06, 2019

Very Cool

автор: Sharvil G

Aug 06, 2019

Transfer learning part should have been in more detail. Thanks.

автор: Michel M

Aug 06, 2019

The final assignment was somewhat a steep step

автор: Super-intelligent S o t C B

Aug 10, 2019

Very good course that teaches you basics of convolutions, augmentation, transfer learning. Thank you to Mr. Moroney and the Coursera team for making it available.

автор: William G

Aug 16, 2019

It was good, but similar to other learners I feel a little light in content. Though in tandem with the deep learning specialization gives a good view on convolutional neural networks as well as its implementation in tensorflow.

автор: Xiangzhen Z

Aug 18, 2019

a little bit too easy compared to Andrew Ng's deep learning course.

автор: Muhammad U

Aug 18, 2019

A well taught course with interesting coursework and projects

автор: Saeif

Aug 20, 2019

This is another great course in the specialization. I wish only there were graded exercises like the previous course that we can submit and get a grade for. I understand maybe this is due to the long time of training and that is not possible to do.

автор: Xinhui H

Sep 16, 2019

Some overlap with first course.

автор: Nicolas

Aug 30, 2019

First, I think the course was great, very instructive. Thanks to Andrew and Laurence for putting this together, is a great source of information to understand more about DL. Some things I think could improve the course.

I found the transfer learning lessons a bit unclear and I struggle generalizing this to other cases. Also, I was a bit confused by the flow of the course. The course starts with a multi classifier (or actually, the previous course), then the lessons focus on binary classifiers and it ends again with multi classifiers, because these should be the more complex ones.

One last technical thing, only on the last lesson of this course it is mentioned that the classifiers output the probabilities on alphabetical order when using ImageDataGenerators (or at least, that's my impresision). I've wondered since the course introduced the ImageDataGenerators, how the probabilities are assigned on the outputs. I could figure out on the sigmoid that the classifier would look for the first class on the directory and output 1 or 0 based on that, but it would be good to have this mentioned at some point on the video when the ImageDataGen is introduced.

Thanks again! Great course

автор: Thomas L

Nov 04, 2019

Maybe a bit repetitive, when you just finished Course 1. We see a lot of lines of codes explained again from course 1 and I think that could be avoided.

However, the new concepts are nicely introduced and very interesting to implement!