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

4.7
estrelas
7,387 classificações
1,150 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!

JM

11 de set de 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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976 — 1000 de 1,152 Avaliações para o Convolutional Neural Networks in TensorFlow

por Paulo A C

23 de abr de 2020

Great content!!

por ashraf s t m

31 de jul de 2019

Voice is low

por Venkatesh S

2 de dez de 2019

Excellent!

por Bingcheng L

12 de nov de 2019

quite easy

por Suraj

11 de fev de 2020

Too easy.

por Hamzeh A

6 de ago de 2019

Very Cool

por Omar M

16 de jul de 2019

Was okay

por Sung-Hyun S

25 de ago de 2021

Good!

por S. M S H

21 de set de 2020

Good

por Anastasia C

8 de abr de 2021

I had a problem with the weekly assignments 1 and 4, which I think asked for things that were not presented at all in the videos. On the first week, without any preparation, we were asked to create the directories, supposedly without any python background, which was tough. But that was just a couple of commands. In the last weekly assignment, the file reading and loading was very problematic for those who hadn't previous python knowledge, and pretty advanced too. In the course of the lessons and the notebooks shown previously, never had we encountered .csv files, and the way to load the images that we were introduced to, with directories, was not at all present in the final project. Also, the methods that were needed afterwards (.flow(), evaluate) were not even hinted before, even in the comments of the assignment, if not before, during the videos/notebooks. All in all, the last exercise took me by surprise and was really tough to get working, because the course was almost irrelevant to it (no transfer learning or directories, the two main points in the course).

por Michael M

26 de jul de 2019

A bit too basic and shallow in terms of conducting the lecture. You are left doing most of the things on your own as the trainer assumes you know. Like using the jupyter notebook, configuring the tensorfow. Some of the google collab books do not work or took too long to load, the videos are too short no notes provided at all. After finishing the course there is nothing to refer to and its starting all over again. Given the level of machine learning course with Professor Adrew Ng, the standard is very high and you will expect that same level. Nevertheless, the concepts are very useful and the lecture explain very well. There level of material left for students to practice on their own,like assignments, notes. Not to be referred to existing material.

por Muthukumarasamy S

4 de ago de 2019

Overall learning from this course is less compared to the expectations from a 4 week course. I was expecting to learn variety of TensorFlow implementations for CNN like Face recognition, Object detection. But this course only talks about Image classification. It would have been better if you could also discuss more about implementing various architectures in TensorFlow like ResNets, Inception. Also, You talked only about using sequential layers in Keras and concatenation of layers in Keras is not discussed here. I know all these concepts are discussed in Deep Learning specialization. I was only expecting to learn their implementation in TensorFlow from this course.

por Pablo A

4 de set de 2020

It's a nice next step after the first course in this series, however, I think a lot of this could be summarized in a shorter course or even added to course 1. I was particularly annoyed by some of the assignments as they required knowledge of other libraries that are not part of the course. Particularly Week 2 and 4, I spent a lot of time figuring out how different libraries worked just so I could preprocess my data before even gettin on to the course material. Week 4 in particular feels cramped up and the assignment uses a lot of tools not previously discussed, I don't think I learned much from it, I just wanted to be done.

por Dhruv D

21 de jan de 2021

I wanted to rate this course 5 stars as it really is the best intro to CNN's ive seen but the last assignment was so egregious that I just have to dock 2 stars to bring this to instructor attention. Lots of people are having this problem. It was not the difficulty (in fact it was a nice change of pace from the usual flow_from_directory assignments) but instead the marking criteria and timeouts etc. My first submission took 2.5 hours to fail. I submitted again with next to no changes and it passed within 5 minutes - I lost the time it couldve taken to do .5-1 week of the next module

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 Thomas K

24 de out de 2021

Some E​xcercises are not great:

-​ Requirements and goals are not set in the outset

-​ Functions need to be used that were not explained, nor even hinted at

-​ Sometimes significant focus of an exercise is put onto modifying file structure and/or csv importing (which is not what I want to spend my time on in a Tensorflow Specialization)

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.