Chevron Left
Вернуться к Convolutional Neural Networks in TensorFlow

Отзывы учащихся о курсе Convolutional Neural Networks in TensorFlow от партнера deeplearning.ai

4.7
звезд
Оценки: 6,512
Рецензии: 1,014

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

RB
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..

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.

Фильтр по:

876–900 из 1,009 отзывов о курсе Convolutional Neural Networks in TensorFlow

автор: Zhuang L

20 апр. 2020 г.

The videos were quite solid. The programming assignments were poorly designed to accept identical answers, but not other solutions that work. This did not evaluate students' creativity and depth of understanding. The Jupyter notebook environment was quite fragile. The resources allocated for each notebook was quite limited. I expect more computer or human resources allocated for each student paying the tuition.

автор: Thomas B

10 апр. 2020 г.

This course teaches you how to apply CNN to image data, how to augment image data with ImageDataGenerator, and how to do transfer learning. It is very easy to follow, and quite possible to finish in half a days worth of effort. It would be nice to be more explicit with what is required by the grader, as assignment instructions not always are clear.

автор: Bakhtawar U R

9 дек. 2019 г.

Good but too basic.

Specialization's first course already covered the basic of tensorlfow. This course is suppose to expose to sota topics in computer vision using cnns. The content in this course can be easily fetched from many online forums. Thus the curators need to put some advance topic like attention, spatial transformer etc etc

автор: Niklas T

25 нояб. 2020 г.

The videos and explanations by Laurence and Andrew are good, but I did not like the programming assignments in this course, because of their lack of explanation 'what to do'.

The programming assignments really need some fixing. They are not to difficult, but they lack explanation of what to do, which parameters to use, etc.

автор: Philip D

5 сент. 2019 г.

A good course, but again, not nearly as in depth as the original deeplearning.ai set of classes. The material feels introductory and at times superficial, with no real work required of the student to complete the class. At best a very early start to using convolutional networks with the keras apis in tensorflow.

автор: Ajit P

2 сент. 2020 г.

I am giving only 3 stars because of two reasons: 1)the content is not significantly different than course 1. I didn't feel that I learned a lot more than course 1.

2)Assignment for week 4 is not well structured. Instructions are not clear. Moreover grader is poor quality and keeps running out of memory.

автор: tqch

15 авг. 2020 г.

Not much recommended! Leave out too many details both theoretically and technically. The quizzes and the coding assignments are not well-designed. Specifically, the expressions in the quizzes are kind of sloppy and the coding sometimes requires tedious and repeated (no more than copy and paste) work.

автор: AGAM S

31 мая 2020 г.

I learnt a lot about CNNs and how to implement them, but I was taken aback to see advanced coding concepts being used in the programming assignments. I thought the concepts taught in the course itself were to be used only, but some parts of the assignments had parts which were too much to grasp well.

автор: Pete C

20 февр. 2020 г.

The course was very repetitive, not challenging, and therefore not particularly helpful. Andrew Ng's Deep Learning Specialization is vastly superior. Aside from getting used to TF and CoLab, I'm not sure what this helps with. I found it odd that it was recommended to me after the DL specialization.

автор: Lukas K

29 дек. 2020 г.

Videos are great, but a little bit short. Comparing to AndrewNG courses and slides, the videos are merely the trailer for course. Grading is not what I would be expecting and it is one of worst I have seen on Coursera related to AI/ML. I was expecting a little bit more from this course.

автор: Giulia T

27 апр. 2020 г.

This course is a really light introduction with CNNs in TensorFlow. While I enjoyed the videos, the content feels far too shallow. I completed the course in a couple days (and I'm not an expert in the field). It felt more like having gone through a TF tutorial than a grad-level MOOC

автор: Raul D M

1 нояб. 2019 г.

It is a good course for a fast overview on this topic. Be aware that it is not an introduction on ConvNN (but there are several courses of deeplearning.ai on this topic). If you are looking for a detailed course on Tf for ConvNN, I suggest you a book, the official documentation.

автор: Tobias L

31 окт. 2020 г.

Basically a shallow introduction to programming simple CNNs with Keras. A lot is reused from the first course in the specialization. Reading one of the Tensorflow Tutorials/API documents on CNNs, Dropout, and TransferLearning will be time better spend, than doing this course.

автор: Salih K

9 нояб. 2020 г.

The course itself is really good; however, homework problems at the end of the chapters are very unorganized. There is almost no guide at all. You may end up spending hours while trying to figure out why grader is having problems or your model's accuracy is very low.

автор: Varun C

10 июля 2020 г.

Giving it 3 stars because of the last week's assignment. There is little to no information about the dataset and the learner is just expected to know how to deal with the data. No information on how many classes to expect as output and other necessary information.

автор: Ambroise L

29 дек. 2019 г.

What could improve it: Not enough depth in the practicals if you have already done Andrew Ng's course on Conv nets. No graded practical exercise.

What was good: Clear examples, Good setup to experiment with the algorithms & Speak explains concepts very clearly,

автор: Ignacio R L

28 мар. 2020 г.

Good course, but the notebooks need a deep review to fix the problems related to balance between the requirements of the exercise and the resources available also a better explanation of the exercise aims would be a nice to have to avoid misunderstandings

автор: Michael R

18 сент. 2019 г.

Actually a great course. Only not getting more stars due to the issue encountered with the last exercise where there is an issue in loading the data files. The workbook keeps on crashing and there is no solution provided to resolve that.

автор: Matías F B

28 мая 2020 г.

The material is good, but there is not much thereof.

The duration of the assignmentsis greatly exaggerated, since most of the lengths for the readings and exercises are wrong.

The course can easily be done in 25% of the official time.

автор: Dirk H

7 нояб. 2019 г.

If you have taken the first course of the specialization this class was repetitive at some points. I also did not like that there have not been graded coding problems. I still got some practice and learned some new techniques.

автор: Wenyu Y

20 мар. 2020 г.

The materials about implmentation of transfer learning is helpfu, but again, I think the whole content of the first two courses could be compressed into one week. There're really not too much new things.

автор: Sumit c

18 мая 2020 г.

some clear instructions should be given for students. In exercise of week 4, there was no specific instruction about using .flow instead of .flow_from_directory, for labels we had to use to_catagorical.

автор: Amir S

24 мая 2020 г.

Course assignments need a good overhaul. The two environments to practice the assignments (Jupyter workbooks and Google Colab) are not consistent, one throws an error while the other one works fine.

автор: Nermeen M

13 дек. 2019 г.

Very good course but please consider reordering the videos and reading especially in week 3. It is better to discuss the code in the video before moving to the notebook not the opposite.

Thank you

автор: Ashok N

26 июня 2020 г.

Course content was super nice.

But exercise organization is very annoying. not at all satisfied with the exercises. sometimes not loading and sometimes is really annoying . very disappointed