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Отзывы учащихся о курсе Convolutional Neural Networks in TensorFlow от партнера

Оценки: 5,543
Рецензии: 835

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


Mar 15, 2020

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..


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.

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576–600 из 829 отзывов о курсе Convolutional Neural Networks in TensorFlow

автор: zhenzhen w

Nov 18, 2019


автор: Jurassic

Sep 06, 2019


автор: 林韋銘

Aug 20, 2019


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: Anand H

Sep 12, 2019

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

Aug 01, 2020

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

Apr 06, 2020

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

Jun 25, 2020

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

Jul 16, 2020

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

Mar 29, 2020

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.

автор: Tom G

Jun 06, 2020

Overall very helpful. I wish debugging on the jupyter notebook assignments was better and that it gave pop text descriptions, etc. Google collab is much better that way. I wish the assignments could use that environment instead. Also, the assignments us model.fit_generator which is now deprecated in TF 2.2. Would be good if the assignments were updated to use instead.

автор: Danilo B

Aug 22, 2020

The course is very good, but coming from the Deep Learning Specialization, also offered by, it feels somewhat like a downgrade having 15 minutes of video for each week, while the other specialization had real extense and complete explanations with over 2h of video. I feel like 10min more of explanations going through the code would make a huge difference.

автор: Raman S

Jun 01, 2020

The grader memory availability does not match the one available to us during the exercise. as a result insufficient memory is shown as grader remarks whereas we do not face such a problem. This becomes hard to debug and is more of analysis, trial and error. Can be avoided if we also get the same type of warning when we create/update our notebook

автор: Cameron W

Sep 01, 2020

Course material was good. The only issue I found was that the graded exercises are graded by automated systems that have different requirements to the notebook environment used for development. This 'black box' strategy by Coursera makes some of the exercises difficult. If you don't have debugging skills with Python, don't attempt this course.

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: RUDRA P D

Jul 08, 2020

What I feel in this course is that, a lot of the exercises are much about file handling operations instead of CNN implementation. Also, in the exercises there are missing task allotments/comments.

I liked the explanation and implementation part of Transfer Learning, I think it's the best part of this course.

автор: Stefan B

Apr 09, 2020

The course gives you an eagle eye view of how to use keras tensorflow for convnets. While they lectures are good, they are very short. I would have loved to hear more about training and storing your own networks for transfer learning and a bit more on regularization. A bit too shallow and easy for my taste.

автор: Suhan A

Apr 08, 2020

I really did enjoy learning and playing around with the workbooks, however the exercise problems needed more explanation as how to go about since sometimes some of the concepts are not very obvious unless we dig into the documentation of the tensor flow and keras libraries which can be a good thing.

автор: Narayana S

Mar 17, 2020

Good coverage of practical stuff in image recognition but it only covers the basic introductory stuff. There is a lot more to image recognition than what Is covered in this course. This will give a foundation to a novice user to learn more advanced deep learning techniques.

автор: Henk M

Dec 22, 2019

This course explores the topics of the first course for image classification with neural networks. All the tests are multiple choice questions. There are some code examples to work with as well as extra exercises but it would have been good to have a programming test as well.

автор: Przemek D

Jun 14, 2020

Generally a really good course, but the last assignment is out of nothing very badly explained in terms of data processing, which causes the grader to fail or run out of memory and therefore passing it is quite a challenge. Besides that, a very good intro to CNNs.

автор: Faiz A

Aug 02, 2020

Course was quite good, but the last assignment was a little challenging,Well..that's what i really liked!. Also, i felt like more concepts in computer vision had to be covered like Object detection, segmentation. Fairly basic concepts were emphasized here.

автор: Marco D G S

Jul 26, 2020

I think some parts of the assignments are not really the main objective of the course, they focus more on methods that involve just creating folders and copying files, which is not what I was there for. Aside from that, great ML content right here :)

автор: Oscar D D L T

Sep 07, 2020

Excelente curso, casi no necesitas saber programar los conceptos super actuales y las actividades te permiten ejecutar procesos de inteligencia artificial y lograr resultados interesantes con un conocimiento tecnico minimo....super recomandable!!!!!

автор: Saeif A

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.