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

8,472 calificaciones
1,818 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:

51 - 75 de 1,822 revisiones para Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

por Samarendra P

Mar 11, 2019

Excellent set of videos and practice assignments!

por Jui W

Dec 30, 2019

This is a very nice introduction to Tensorflow.

por Min H S

Jan 05, 2020

Thanks for awesome lectures.

por Ahmed S

Mar 10, 2019

Good Course but too short!

por Juan A

Mar 10, 2019

Another Amazing Course :)

por Jun W

Mar 08, 2019

Brief and interesting.

por akash k d

Jan 04, 2020

Enplaned very nicely

por 毛昊

Jan 05, 2020

excellent course

por Georji G

Jan 05, 2020

Great content

por Hadi F

Jan 09, 2020

Very good!

por Henrique C G

Dec 31, 2019

Some of the material is a little confusing: sometimes the exercises will open on Google Collab, others in a classic Jupyter Notebook; instructions sometimes seem to lack revision to make them more organized and less convoluted. Also, references in videos should be always made into links (e.g. Andrew's videos are usually referenced as links in the class video and they are not clickable. Since the graded course is paid (and is not cheap for most third world countries) it seems that a little more care and polish should be applied. The contents are excellent, but they lack the organization and quality of the original course from Professor Andrew Ng, which was, by the way, 100% free and didn't have exercise grading locked by payment, for example.

por J.A. M P

Dec 31, 2019

The course offers a great introduction to TensorFlow methods for handling data, training models, and inferring results. Two things could be enhanced, in my opinion:

1) A better estimate of the time required to read the materials and do the exercises (the course takes less time than stated).

2) More in-depth explanations for certain parameters (although it could be argued that you should just follow the other specialisation for that).

Overall, though, a great crash-course for getting started with Tensorflow!

por Hao H

Jan 05, 2020

I took this course after taking deep learning ai CNN course. I found this course complement the other course really well.On itself, it is a little thin on theory size, but if you have already taken the other course, then this is a great consolidation of the material.

por Arkady T

Jan 04, 2020

It take some time to change the code and run examples from this course with TensorFlow 2.0 locally on my computer. Today TF 2.0 is state of the art and required in practice. Please rewrite code for TensorFlow 2.0

por Kumar N S

Jul 05, 2019

More or less the course takes on Tensorflow's implementation of Keras rather than Tensorflow native env. It also only focuses on computer vision domain. Kind of misleading course title.

por Guillaume G

Apr 23, 2019

Ce cours balaye les fonctions de bases de la librairie d'abstraction Keras et permet de construire rapidement des réseaux de neurones complexes.

por Rudresh M

Jan 07, 2020

When each layer visualization was taught, I didnt get that part nor in the program. Else its a great starter course

por Lu A

Apr 23, 2019

It's relatively simple course if you've already finished Andrew Ng's deep learning specialization

por Rana T J

May 14, 2019

The assignments need to be polished. They were very lackluster and non-rewarding.

por Bhabani D

Jan 06, 2020

Great introductory course to learn the application of TensorFlow with Keras.

por Saravanaram

Jan 01, 2020

Great course, but can be completed shortly instead of many weeks session

por Hakesh k

Jan 05, 2020

Amazing way of putting all the stuff together

por Muthiah A

Jan 06, 2020

Useful start for practitioner.

por Rushikesh W

Jan 04, 2020

Good practice for coding on tf

por Ivan N

May 19, 2019

I think this is a great way to introduce NN to people that have never seen one.

But there was very little depth in this course. I finished the 4 weeks in an afternoon. The external references were at times way too advanced, while the exercise code was way too simple. That being said, the Jupyter notebooks were a great material and helped me start with NN really quickly. The MNIST dataset is brilliant and hank you for showing how to do it.

The reason why I gave 3 stars is because the MOOCs aI have done in the past were much more extensive and gave plenty of theoretical background. Some people might think that the lack of theory lowers the entry bar for students, but in my book that's a tutorial not a course.

Save yourself the $40 price tag and buy a book on the topic, there are plenty out there.