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Opiniones y comentarios de aprendices correspondientes a Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning por parte de deeplearning.ai

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2,803 reseña

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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. 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....

Principales reseñas

AS
8 de mar. de 2019

Good intro course, but google colab assignments need to be improved. And submitting a jupyter notebook was much more easier, why would I want to login to my google account to be a part of this course?

RD
13 de ago. de 2019

Great course to get started with building Convolutional Neural Networks in Keras for building Image Classifiers. This is probably the best way to get beginners into Deep Learning for Computer Vision.

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2626 - 2650 de 2,796 revisiones para Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

por Dave M

13 de may. de 2020

Good course content, but I frequently got lost by the organization of the datasets, files, etc. I learned to set up neural networks but I can't, for example, see how to run them on data on my own computer. Data is just magically present during the course.

Also, it would help to have the Laurence's notebooks available somewhere in the course summary. They are accessible in the unit AFTER he has talked through them in a video, but I always want to see them WHILE he's talking through them (not just the image in the video, the actual notebook), not afterwards.

por Chiel B

12 de jun. de 2019

Some course material is mixed up (e.g. MNIST and Fashion MNIST datsets and examples are 'convoluted';-). Also, the performance of the resulting models is overstated. I don't think it is very impressive to make models that still make mistakes such as qualifying a horse as a human (or worse: an attractive woman as a horse). The idea from the media is, that computers/algorithms beat humans in image recognition easily (e.g. recognizing diseases in medical images), but this is not evidenced by the contents of this course.

por Quentin P

9 de jul. de 2019

The course is fine, but quite basic. I didn't like the fact that there was no way to submit any code homework (as in the other deeplearning.ai specialisation). Just reading some code and experimenting with it is not a good way to learn in my opinion. A suggestion: show a picture depicting the NN that is being built in the code so *this* code implements *this* CNN (or whatever) with depictions of the NN structures as in the other specialisation.

por Deleted A

2 de abr. de 2020

I consider programming assigments could be better. In comparison to the deep learning specialization assigments, the ones we had in these course are really poor in terms of information and clarity on what we have to do. Specially, it is incredible that there are some unfunctional code on the notebooks such as the javascript part to save the notebook, it only caused error for many students as seen in the forum

por Walter G

1 de may. de 2019

This is a great introduction to Keras, and I learned about some unknown features. Unfortunately, I had thought it would be more focused on Tensorflow, since it's in the title of the course. I had decided to take this course midway through the Deep Learning specialization. I was hoping to gain more practice with easier Tensorflow examples, but the course didn't cover any core Tensorflow.

por Matthieu S

6 de oct. de 2019

Very approachable course, probably a little too much. Assignments can be done by simply copy-pasting notebooks from the videos without any modification in the model. The generated images are also not that varied, and give skewed image of what humans should look like.

Anyhow, the videos are good, as well as the annotated notebooks, to familiarize ourselves with CNN and the Keras API.

por Alex S

12 de mar. de 2019

A good quick walkthrough of how TensorFlow works, but not very in-depth. Definately worth taking, but not worth the $60 for the certificate in my opinion. My 3 star rating was for two reasons. 1. Price was too high for what was taught. 2. I feel like he could have gone a little more in-depth on how some of the functions were working, maybe had more complicated exercises.

por Adrian B

20 de ago. de 2020

The course can be done in 1 week or less.

Graded exercises end up being a variation of the already available week's notebook + a bit of code that is also repeated among every exercise. Not much to learn there. First exercise was a bit confusing in its wording also.

I think we should demand more from these courses if we want MOOCs to be taken seriously.

por Soo J K

14 de ago. de 2020

I liked the instructor, but the way the course was put together is little awkward to me. Especially reading tasks, they give you a few sentences and ask you to mark as complete, and this is the end of the task. What's point? Also, i think the coding tasks should also be self-sustaining. You can still learn through this course, though.

por Haider A K

4 de feb. de 2020

The course title should indicate that it introduces Keras API NOT TensorFlow. The course seems to be focused on students who are new to neural networks. However, it does not explain any concept with detail. On the other hand, students like I, who have already completed Deep Learning Specialization would find this course too basic.

por Trinadh

18 de may. de 2019

This is my specific review. I have done a lot of deep learning before and doing tensorflow , thought of getting rigorous exercises but there are only 2 examples. May be this is not the right course if you want to become expert in tensorflow, but it definitely has some organized information though to start off.

por Atilla B

17 de mar. de 2020

The course a bit weird in the sense that if you have some knowledge in deep learning (i.e. you have passed the deeplearning.ai courses) you would learn almost nothing. On the other hand, if you did not take any other courses or do not have any knowledge you wouldn't understand whats going on.

por Henrique G

23 de jun. de 2020

This course is really hands-on and quite easy. You'll be using Keras during the course and it is assumed you had completed the Deep Learning specialization or another deep learning course previously. You'll be able to finish it in a couple of hours. So grab a beer and enjoy the light tour.

por mgbacher

8 de feb. de 2020

The course speed is relative high for an introduction. I, personally, have covered most of the contents using Keras's master book "Deep Learning with Python". I would have expected more insights into TensorFlow and not out-of-the-shelves code using Keras sequential models to play around.

por Aleksandr F

9 de may. de 2020

Course materials are great, but there is a minor mismatch between Jupyter and Collab in versions of code and TensorFlow, as well as some comments in code, do not match the actual code below in constants. So it would be good somebody could check it through to make sure all is right.

por Alen B

6 de feb. de 2020

A bit bored of only doing image recognition in every TensorFlow tutorial under the sun, including this course, as if there are no other problems to solve with it. The title is so broad that frankly I was expecting a bit more for the money than just doing image recognition.

por Li P Z

18 de ene. de 2020

Instructor does sound job of explaining how to use TensorFlow for deep learning. Compared to Andrew's courses, much less content in the videos and exercises. I feel that much more thought and effort could have gone into this course, just look at how verbose the name is.

por alex c

4 de ago. de 2020

it would be ok if this course were free but it's not. there are technical mistakes in the multiple choice quizzes and the mandatory programming tests. some important topics are left unexplained while some are repeated unnecessarily. check the forums for more feedback.

por Nimit J

15 de ago. de 2020

Though it does give you a good introduction how to make neural networks, i felt that this course doesn't explain the Keras API library, which makes it difficult to remember and understand how things work.

I hope further courses in the specialization take care of that

por Giovanni C

29 de abr. de 2019

I feel there are gaps in this course. But it was still worthwhile going through the material, to reinforce certain concepts. I had the impression that the course was initially classified for beginners, and that later on that classification was modified to advanced.

por Jan B

28 de abr. de 2020

Rather basic, but the learner can strengthen it by broadening his reading. What I missed most is a conceptual introduction of what Tensorflow is and does.

I did not take the Deep Learning specialization before this one. Maybe that would have made a difference.

por Michael

26 de jul. de 2019

Good introduction but lacks materials and practice to use after the class. Most of the materials its only referral. The trainer is good, except very shot videos and the setup on your pc are not discussed. But generally enoyed the lecture and learned a lot.

por Carlos A V P

20 de jun. de 2019

This course is simple in comparison with the Deep Learning Especialization, However you can learn new things like training from folders and using callback for early stopping. I consider the course is of, but the course use Keras and not tensorflow directly

por Christopher N

17 de sep. de 2020

The graded programming assignments are a little too ambiguous for beginners. They should really spend a day or two and model their programming assignments after the way that Andrew Ng did his programming assignments in the Deep Learning Specialization.

por Edward D

11 de ago. de 2019

The homework is not designed well:1. The notebook is inconsistent with the colab env, and there are always problems here and there due to the inconsistency in tf version. 2. All 4 homeworks are similar, and it's simply a copy of the video lectures.