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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

4.7
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13,256 calificaciones
2,810 reseña

Acerca del 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. 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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2176 - 2200 de 2,800 revisiones para Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

por Петрук П О

17 de abr. de 2020

Cute

por Philip V j

3 de abr. de 2020

good

por LIU,SHI-HONG

14 de feb. de 2020

GOOD

por zhenzhen w

18 de nov. de 2019

nice

por M n n

10 de oct. de 2019

Gr8!

por 申桂可

26 de sep. de 2019

good

por Jurassic

6 de sep. de 2019

good

por Rupsa R

4 de jul. de 2019

Good

por Nazarii N

25 de jun. de 2019

cool

por Dr S P

20 de jun. de 2019

good

por Sui X

26 de may. de 2019

good

por ankit k

21 de abr. de 2019

good

por Ashok R A

3 de abr. de 2019

Good

por Mohamed M

30 de sep. de 2020

<3

por keshav b

12 de jul. de 2020

<3

por Srijeet C

30 de mar. de 2020

N

A

por 林韋銘

16 de ago. de 2019

gj

por Ruben M

27 de ago. de 2019

I

por Raman M

4 de ago. de 2019

i

por Richard H

13 de sep. de 2019

Great introduction to using Tensorflow to implement convolutional networks.

I took the Stanford course by Andrew Ng first, so many of the concepts were very familiar - in some cases, the detail was just a little bit shallow - probably to avoid interfering with getting on with implementation - but this course certainly had references outside the course to some more detailed information on topics like how convolutions help identify features or the learning factor.

The jupiter notebooks were great in that you don't need to worry about the environment much - it's already set up - a big worry for me for many of these types of courses. But there were quirks, and a few times I (and some of the other students) could get tripped up for a little while. If you are a developer like I used to be, then troubleshooting and debugging environment/code issues is a small hurdle though.

Kudos to the instructors and those that set up the course - this is otherwise very hard material to teach and set up good "hands on" evaluation, which they did really well, a couple kinks aside.

por César R P

19 de ago. de 2020

The course is a great introduction to the use of tensorflow. Keep in mind, this course is a practisioners guide to Deep Learning, so there's not much theory involved, you just get an intuition to how things work. I'd say this is a great start to the specialization, which I feel will probably compliment Andrew Ng's specialization greatly (that one delves into the theory, but the code you make isn't really what one would use in day to day ML projects).

The only thing I didn't like (why I docked a star) where the programming assignments. Too easy, and the autograder barely checks anything. You need to explore thigs by yoursefl and be disciplined, as the programming excercises let you get away with anything. That said, that seems like it only happens in this first course (maybe as a way to encourage people to keep moving forward), and the excercises get better in what I've seen of the next course.

All in all, great course. Mr Moroney is one of the best teachers I've ever see, and communicates his knowledge and pasion with great ease.

por narendra@live.com

1 de oct. de 2019

This is a great course with very useful lessons that helps the students feel confident about implementing Deep Learning solutions. It is a perfect follow up for Deep Learning Specialization which lays down the theoretical foundations. The instructor is great, and he talks about real world problems (not just Fashon MNIST but non centered, colored and large images) and explains them very clearly.

There is some amount of lack of attention to details in the course which manifest itself specially in the code (typos, code and code comments not agreeing with each other, and entire lessons which are slotted for 10 minutes or more but dont have any action other than pressing the "mark as complete" button, which makes you feel that you are missing something. Also the discussion board isnt as responsive (especially moderators) as the other Deeplearning.ai courses have been in the past.

por Ilya R

31 de jul. de 2020

I like Laurence's teaching style. This isn't the first his course I've taken. It's nice that he has some interesting datasets of his own and some research questions. But I have a couple of suggestions to this course.

First of all tensorflow documentation has a lot (and I mean A LOT) of good tutorials so I'd expect some of them to be included in the course. That what you can expect of Google's developer advocate to do. I really need some help in understanding those tutorials.

The second suggestion - the course is far too basic. That's probably OK but we really need a follow-up course to dig much deeper into tf.data.Datasets, image processing and custom metrics and losses as an example. It's really not enough background to really reproduce results from say AI for medicine courses that you may get from this series.

por John S

4 de ago. de 2020

The course does a good job of teaching basic TF functionality. It doesn't go deep into actually how NNs work which I supposed is fine if you already have that knowledge. The exercises are a little finicky when it comes to grading. My biggest hangup is that the time they calculate for each week is extremely over-estimated. Half the modules are "Readings" which they allot "10 minutes" for and most of these are a single paragraph or just a rehashing of what was just said in the previous video. The exercises also shouldn't take anyone near 3 hours to complete. So, keep in mind that each week's material can be completed in probably 30-45 minutes at most...not 6 hours!