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Kursteilnehmer-Bewertung und -Feedback für Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning von deeplearning.ai

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5,284 Bewertungen
1,060 Bewertungen

Über den Kurs

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....

Top-Bewertungen

AS

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?

RD

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.

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51 - 75 von 1,058 Bewertungen für Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

von Jui W

Dec 30, 2019

This is a very nice introduction to Tensorflow.

von Min H S

Jan 05, 2020

Thanks for awesome lectures.

von Ahmed S

Mar 10, 2019

Good Course but too short!

von Juan A

Mar 10, 2019

Another Amazing Course :)

von Jun W

Mar 08, 2019

Brief and interesting.

von akash k d

Jan 04, 2020

Enplaned very nicely

von 毛昊

Jan 05, 2020

excellent course

von Georji G

Jan 05, 2020

Great content

von Hadi F

Jan 09, 2020

Very good!

von 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.

von 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 deeplearning.ai specialisation for that).

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

von 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.

von 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

von 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.

von 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.

von 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

von Lu A

Apr 23, 2019

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

von Rana T J

May 14, 2019

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

von Bhabani D

Jan 06, 2020

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

von Saravanaram

Jan 01, 2020

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

von Hakesh k

Jan 05, 2020

Amazing way of putting all the stuff together

von Muthiah A

Jan 06, 2020

Useful start for practitioner.

von Rushikesh W

Jan 04, 2020

Good practice for coding on tf

von 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.

von Alon L

Mar 19, 2019

Material is very well explained and very relevant but the course is short in comparison to other deeplearning.ai courses before and could be richer both in content and in exercises (which are also not graded)