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Comentários e feedback de alunos de Convolutional Neural Networks in TensorFlow da instituição deeplearning.ai

4.7
estrelas
6,927 classificações
1,081 avaliações

Sobre o curso

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....

Melhores avaliações

MS
12 de Nov de 2020

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!

RB
14 de Mar de 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..

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926 — 950 de 1,078 Avaliações para o Convolutional Neural Networks in TensorFlow

por Jesus E R

14 de Mar de 2021

I think it is too basic and not a lot of depth into what you are learning, specifically on the differences between binary and categorical classes, reading from disk vs manipulating data already in an array.

I was expecting more depth on tradeoff within Tensorflow's API choices. More direct comparisons between optimizers and the data generators. I feel like one straightforward exercise at the end of a really shallow week of videos is not enough to understand what students are really doing/learning.

por Chris

29 de Out de 2020

The course lectures are solid, but the assignments are pretty dismal for beginners. There isn't much guidance built into the assignments, and sometimes they require the use of things that were absolutely not covered in the lectures(classic academic mistake). My suggestion for the course creators is to examine how Andrew Ng's assignments are in his Coursera course and model them after that. Or simply make sure that the assignments are clear(clear to someone beginning, not a TF expert).

por Artem D

29 de Jan de 2020

I liked the lectures (videos). And I did not like that the course has no mandatory programming assignments. I pay for the course to make myself study. And I believe that there is no study without practice. Hence, this course did not make me study, thus I don't understand why I need this course :-(. And I could find free lectures about TF/Keras (maybe not so good, but free) and/or read the documentation. BTW, I really like Andrew NG's courses, but this one really disappointed me.

por Shehryar M K K

3 de Mai de 2020

This course focuses on the teaching of TensorFlow modules related to CNNs and does a good job in introducing some modules of tf and keras for data loading and manipulation. However, it is very light on theory and is only helpful if Deep learning specialization is taken beforehand or in conjunction. Furthermore, this course will need some refresh soon for its modules as it is still using version v1.x of tf as well as some code re-organization.

por Benjamin D

6 de Ago de 2021

The use of the .flow() method on the last exercise would deserve some explanations : the labels need to be transformed from sparse (int format) to one_hot (with tf.keras.utils.to_categorical for example), so the loss='categorical_crossentropy' actually works in model.compile().

There is no mention of the different ways of structuring the labels in the course, this can be misleading.

Other than that, good material.

por Zhuang L

20 de Abr de 2020

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.

por Christos P

19 de Ago de 2021

The course generally was fine and it taught me many things on how to use tensorflow for aumentation and regularization. But...I think that notebooks need a little bit more clarification. Many times I didnt know exactly what to do and other times the comments were misleading. Overall I would recommend to get the free trial and see if you like it before you spend the 40-50$

por Thomas B

10 de Abr de 2020

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.

por Bakhtawar U R

9 de Dez de 2019

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

por Niklas T

25 de Nov de 2020

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.

por Philip D

5 de Set de 2019

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.

por Ajit P

2 de Set de 2020

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.

por tqch

15 de Ago de 2020

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.

por AGAM S

31 de Mai de 2020

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.

por Pete C

20 de Fev de 2020

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.

por Lukas K

29 de Dez de 2020

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.

por Giulia T

27 de Abr de 2020

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

por Raul D M

1 de Nov de 2019

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.

por Tobias L

31 de Out de 2020

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.

por Salih K

9 de Nov de 2020

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.

por Varun C

10 de Jul de 2020

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.

por Ambroise L

29 de Dez de 2019

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,

por Ignacio R L

28 de Mar de 2020

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

por Michael R

18 de Set de 2019

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.

por Matías B

28 de Mai de 2020

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.