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Volver a Building Deep Learning Models with TensorFlow

Opiniones y comentarios de aprendices correspondientes a Building Deep Learning Models with TensorFlow por parte de IBM

552 calificaciones
112 reseña

Acerca del Curso

The majority of data in the world is unlabeled and unstructured. Shallow neural networks cannot easily capture relevant structure in, for instance, images, sound, and textual data. Deep networks are capable of discovering hidden structures within this type of data. In this course you’ll use TensorFlow library to apply deep learning to different data types in order to solve real world problems. Learning Outcomes: After completing this course, learners will be able to: • explain foundational TensorFlow concepts such as the main functions, operations and the execution pipelines. • describe how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. • understand different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders. • apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained....

Principales reseñas

2 de jul. de 2020

Deep Learning made me feel that there is a way to build models and classify data so easily and in a skillful way. Amazing course!

26 de may. de 2020

Not so often i wish a course would be longer and more in depth I really enjoyed using TF I'll look some other courses about it

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101 - 112 de 112 revisiones para Building Deep Learning Models with TensorFlow

por Junsoo P

22 de sep. de 2020

The lectures only cover various neural nets and not how to actually implement them on Tensorflow, which should be the gist of the course. Further, the labs are at many places not compatible with the most recent Tensorflow version 2's, and only work for previous Tensorflow version 1's which are quite different. The labs must be re-written for the newest versions given Tensorflow's backward incompatibility.

por Stefan L

4 de jul. de 2020

This course was very informative and the labs are really well written.... however the code is SEVERELY out of date. It needs to be updated for TensorFlow 2.0, there is simply no excuse at this point

por Farrukh N A

13 de ene. de 2020

First of all it was too complex, unlike the course on PyTorch which focused on both Theory + Practical part. It focus only on theory.

por Pakawat N

31 de may. de 2020

It is too basic and almost no technical detail about the DNN. It is not good for who have basic knowledge about this before.

por Sowmyashree S

2 de may. de 2020

The codes should be provided with Tensorflow 2.0. Practical implementation should also be shown.

por César A C

29 de jun. de 2020

I think that the labs should have been updated to tensorflow 2.

por Eric

30 de ene. de 2020

Way too short in terms of the amount of content

por Julian S

21 de jun. de 2020

The Material needs to be updated!

por Ustinov A

6 de ago. de 2020

Good videos and bad labs. An old TensorFlow version is used. Therefore all code is useless for the current version of TensorFlow (ver. 2). It had to become the most important part of the specialization for me... There are a lot of topics about problems on the forum. And it's strange that IBM can't change it for a half year.

por Onno v E

24 de ago. de 2020

This course is very outdated, it needs to be updated to Tensor Flow 2.0.

There are NO EXERCISES at all, only labs that contain the some content as the video's. I don't think I really learned much during this course, as the course does not dive deep into the models selected for the course.

por Anas O

2 de jun. de 2020

the labs are based on an outdated tensorflow version, also, the instructor is not Alex Akilson as it is mentioned in the course info.

por Martin B

13 de jun. de 2020

All the code in the course is obsolete using an old version of TF. The course does not have a project nor a final assignment.