Chevron Left
Volver a Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

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

5,494 calificaciones
1,114 revisiones

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 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 revisiones


Mar 09, 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?


Aug 14, 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.

Filtrar por:

851 - 875 de 1,120 revisiones para Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning


Oct 01, 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 courses have been in the past.

por Avinash M

Sep 06, 2019

I thoroughly enjoyed the course and programming various CNNs on TensorFlow. However, in certain lectures (especially the ones with the horse/human data sets), the instructor could spend some more time explaining the process of downloading and storing the training and validation images. It took me some effort and quite a lot of Googling to figure out those parts of the code. While that might not be directly related to the task at hand (binary classification) it is, in my opinion, necessary to understand some of these ancillary tasks as well. Perhaps these explanations could be included as optional videos for those who wish to understand these features of TF.

por Kaustubh D

Jul 28, 2019

This is an excellent course to get hands-on. Keeping some tasks as repetitive like those of the callback functions help make the person strongly hands-on and remember them. Just the way, every week's programming assignment involved writing the callback function, if there would be other TF functions/methods that the coder gets to implement and override and other TF abstract classes to extend from, that would have been cherry on top!

Drilling down from the bigger picture of model definition to seemed extremely useful.

And since there are tons of courses on theory of ML and DL, thank god this one just focusses on coding it out.

por Egor E

Aug 02, 2019

I like structure and content of this introdactory course. And like the easy and clear way Laurence Moroney told about all this stuff. Particulary, I like clear formulated exercises. During course we got great bulk of working examples in jupiter notebookes, containg full lecture, notes, likns to supporting materials!

What I would improve in course it is the change a litle bit a balance from solving problems to technical implementation. We learn a lot of using CNN for image recognition. However, it would be great to listen in more details about calculating the shape for input and outputs for layers.

por Devansh K

Feb 11, 2020

I loved the instructors and the content. For the first time, I found a course that actually taught me the practical aspects of deep learning in a fun and interactive way. The content was very good and the right level of difficulty, i.e. not too difficult but also reasonably challenging. One thing I would change about the course to make it better would be to have longer instructional videos that go over all the code in more detail. I did not completely understand some sections of code and I think this would have changed if there were more code explanations.

por João A J d S

Apr 30, 2019

It's a great course! Very well structured, with an amazing amount of jupyter Notebooks (Colab) to work with, in a real hands on approach.

Just one criticism, which is why I didn't classify it as 5 Star: There isn't much of an evaluation. The tests are a bit easy, and it would be good to have at least one extensive assignment (maybe with other datasets...).

It's just that I feel the contents were really good. But if I can just pass the tests easily, I feel it doesn't really count as much of a "quality stamp" (to have passed this course).

por Samuel M

Jan 24, 2020

Nice class, covers some basics of tensorflow and learns how to quickly build a NN. Not too fond of the quizzes: a few unclear question/choices and lots of "learn by heart" questions (like: what is the size of the pictures in this specific dataset, what is that specific param name) which you can easily answer without understanding too much. The assignments are simple enough for an introduction, quite close to the lesson examples but still interesting.

por Jim D

Sep 25, 2019

I really liked that it was very hands-on and made it very quick and easy to get up to speed on using TensorFlow for Machine Learning. That said, there was a lot less content that I expected (I finished the '4-week' course in about 1.5 days), and I was a bit disappointed that the focus was exclusively on image classification. A little variety in terms of the problems being solved would've been nice.

por Elias B

Aug 07, 2019

Overall a very good course (with knowledge from the deep learning specialization) to get a deeper knowledge in tensorflow. But sometimes in the exercises you feel a little left alone, because of missing information (example Week 4, which folder should you use, what's the resolution of the images, you can find out but that requiered (for me I had to downloaded the files and check what i needed).

por Andrei N

Aug 03, 2019

The course is quite light even for introductory one. At the same time, I enjoyed examples of NN implementations in colab and elaboration on how the code works. The most interesting for me personaly were hints and techics helping to develop a better understending on how the NN are builing its knowlarge on input data. I look forward to checking other courses of specialization out.

por Umberto

Dec 29, 2019

A really simple and intuitive approach to Neural Networks in TensorFlow. Smart and simple examples to experiment by yourself how to build image classification with a few lines of code in python. For me was useful to recap what I already studied in courses. This course was pretty good to do some practical exercises and an overall recap.

por Kristopher J

Sep 01, 2019

The course has a lot of good material, and is a great follow-up to the more theoretical Deep Learning Specialization.

However, I can't give it five stars because the exercises are a bit repetitive, and the quizzes have some very poorly worded questions. I know this is a new course, so I hope they can smooth out some of these rough edges.

por Xuanlong Y

Nov 09, 2019

It's good enough for beginners, but I have to say it's still a little bit easy. Maybe teacher can give us more reading materials or show us more interesting projects that we can reach after this Specialization and I think that can be called an introduction. But anyway, I think the course is better than many tutorials on the Internet.

por Kaan A

Aug 14, 2019

With this course I learned new things on tensorflow. However, It feels like it is very very simple introduction to tensorflow. After finishing Deep Learning Specialization I thought that this Specialization will be complementary. I'm disappointed a little bit on the first course. I believe other courses will be better!

por Rafael R C

Mar 25, 2019

Me gustó el curso, muy explicativo y lleno de recursos, aunque solo rasca la superficial. Hay muchas cosas mas por aprender. Los foros no reciben respuesta tan rápida. Si bien es cierto google Colab, ayuda a esta tareas, cuando se quiere pasar al computadora personal se complica por versiones o librerías faltantes.

por Nikolay R

Sep 11, 2019

Very, very basic course for absolute beginners. It makes sure you know enough to build and train models for simple image recognition tasks. 4 weeks is a crazy long period for it, though. I finished it in 2.5 days (I have previous exposure though), but even a beginner should be able to do it in 1 week.

por Mashood M M

Dec 05, 2019

This course although thought me the basics of DNN but there can be improvements in the code (notebooks) and in the videos as they only tell the abstract part of the concepts. Overall the course was great it helped me alot from knowing what is keras to the journey of building my own model. Thanks

por Vaibhav G

Dec 01, 2019

The course content was really good. But as greed for more never ends, it would be great if we could be able to shed some more light on few gray areas like in what situations we should go for 64 filters or 32 filters, how to determine size of the hidden layer in terms of neurons etc.

por pavan b g

Mar 15, 2019

Thanks to Laurence and Andrew for the sharing there knowledge which have all the foundation requires to get the AI understanding using Tensorflow , keras. Simply awosome .

This is really a refresher for those who already are into datascience field, even for the aspiring students too.

por Kaushal D

Aug 02, 2019

This is fantastic stuff, made simple if you have done earlier course of Deep Learning from Dr. Ng this is at another level, I feel little confident that I may be able to code without copy pasting.. hopefully the next course in the series gets introduced serious stuff..

por Ganesh M S

Jul 06, 2019

Good course which gives the brief explination on how to use the TensorFlow framework to solve many computer vision problems. This course is designed to such that the beginner too will feel more confident understanding the details of the machine learning techniques.

por Nader A

Aug 15, 2019

Hello ,

This was very helpful , I gained some new information.

I can not believe that I finally used the famous tensorflow library , and this is my first time to do picture classification.

This course leaved me with some questions that I will try to research.

por Jay M

Aug 24, 2019

Not being able to run the notebooks on Coursera was frustrating. Fortunately, running them on colab wasn't difficult - just an unnecessary impediment.

Was nice to see some of the more abstract deep learning terms be put to use fairly easily.

por Yueqi W

Aug 11, 2019

May be provide resources to learn some senior grammar knowledge for python, because basic knowledge for python does guarantee we could understand the code perfectly, but simply remember its form in case of a particular complex line of code.

por Stephen B

Apr 11, 2019

Good course, somewhat easy, but I anticipate a lot more new and interesting and useful stuff in the remaining sessions yet to be offered. It would be good to point out what is now Tensorflow 2.0. I anxiously await the new material. Thanks!