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Learner Reviews & Feedback for Natural Language Processing in TensorFlow by DeepLearning.AI

4.6
stars
6,448 ratings

About the Course

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 Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In Course 3 of the DeepLearning.AI TensorFlow Developer Specialization, you will build natural language processing systems using TensorFlow. You will learn to process text, including tokenizing and representing sentences as vectors, so that they can be input to a neural network. You’ll also learn to apply RNNs, GRUs, and LSTMs in TensorFlow. Finally, you’ll get to train an LSTM on existing text to create original poetry! 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 Developer 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 reviews

AK

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Laurence Moroney is the best. Before taking up the course, i didnt know anything about the AI or ML or Tensorflow. The concepts were explained in such a manner that anyone can learn Tensorflow.

DW

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These classes are excelling practical examples of how to use tensorflow for various problem types. My only objection is they are slightly light on the actual, behind the scenes, math and intuition.

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901 - 925 of 999 Reviews for Natural Language Processing in TensorFlow

By FARROUK_ABDERRAHIM B

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Oct 17, 2021

i was hoping to go more into details about building Transformer for RNN using TensorFlow

By WU F

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May 16, 2020

I don't think it teaches well. The materials are not organized suitably for beginners.

By JiahuiWEI

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Sep 9, 2019

It would be better to have acess to the notebook of lessons and some explainations

By Yves B

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Nov 16, 2019

I wished to learn more details about the different types of RNN like GRU or LSTM.

By ROHAN G

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Dec 14, 2021

There sh'd be a good description in one course instead of visiting other courses

By Angel S

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Nov 10, 2020

I think the course is a very basic in the topic. Many details are not explained.

By Venkatesh S

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Jan 16, 2020

The code should be explained a bit better on the advanced RNN, LSTM examples.

By Max W

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Jan 3, 2020

moved very quickly...needed a bit more theory and slower code walk throughs

By Nicolai W

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Sep 14, 2019

Comprehensive explanation videos but not enough practical coding exercises.

By Abhishek B U

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Dec 25, 2021

Course was realli nice.I liked how they focussed on self learning approch.

By Jakub P

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Dec 24, 2020

Surprisingly little material. The ungraded labs are okay but very simple

By Matteo T

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Apr 23, 2020

Compared with the two previous courses I found this one very boring

By Hyunuk L

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Jun 25, 2020

good overall! but the course materials are maybe too basic...

By Aleksander W

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Mar 24, 2021

no graded coding exercises, scope is extremely simplistic

By Shankar G

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Apr 1, 2020

Could've lot of small exercises to understand much better

By Ondrej S

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Apr 16, 2020

it would be better to include graded assignments as well

By Rohit R C

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May 9, 2020

Could be more detailed, lots of doubts are still there.

By NIKITHA M T

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Apr 30, 2020

The sound is very low and I have to strain to hear them

By Ho Y C

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Sep 6, 2019

The materials are not up to date to the latest research

By kishore r

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Apr 2, 2020

some portion of the course content is not interactive.

By Miguel R

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Oct 25, 2020

Feels there where too many concepts not well covered

By Apoorv G

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Jul 17, 2020

Short Videos are annoying. Overall content is good.

By Swetha S

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May 10, 2020

No graded assignments. No conceptual explanation.

By Biao W

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Apr 16, 2020

Need more explanations on the RNN models itself.