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Voltar para Natural Language Processing in TensorFlow

Comentários e feedback de alunos de Natural Language Processing in TensorFlow da instituição

1,811 classificações
250 avaliações

Sobre o 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 Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In Course 3 of the TensorFlow 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 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....

Melhores avaliações


Aug 27, 2019

Excellent. Isn't Laurence just great! Fantastically deep knowledge, easy learning style, very practical presentation. And funny! A pure joy, highly relevant and extremely useful of course. Thank you!


Sep 23, 2019

Excellent course. Teaches NLP thoroughly, going from the basics such as tokenization and padding to complex topics such as word embeddings and sequence models (like RNNs, LSTMs and GRUs).

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201 — 225 de {totalReviews} Avaliações para o Natural Language Processing in TensorFlow

por Nitin K M

Dec 26, 2019

They are course is well oriented for beginners. But no so much explanation provided for models

por Xinhui H

Sep 15, 2019

intro level course. all projects are toy models. would like to see industry-level projects.

por Mats E

Jul 12, 2019

Very good high level introduction, some of the videos are 100% duplicated material though.

por Ambesh S

Feb 12, 2020

Explaination bu Laurence Moorney was not enough, needed some extra stuff but still good.

por Rodrigo V R

Nov 19, 2019

really good course. in some point you need to know or study the LSTM (the essential).

por Bernardo A

Feb 09, 2020

A little too superficial, but great introduction to NLP resources on tensorflow

por Aniruddha S

Jul 12, 2019

Innovative Course but little bit difficult to visualize what is going in deep

por Robert G

Dec 11, 2019

I would like to see examples with some sound, video and web scrapping input.

por Rodrigo S

Aug 17, 2019

Le agregaría más ejercicios obligatorios, como el curso 1. Igual está bueno!

por arnaud k

Jul 19, 2019

great course but could have gone a little bit deeper into the subject.

por Sai K R J

Nov 07, 2019

Basic Level course to get knowledge on Natural Language Processing

por Stanislav Z

Aug 08, 2019

A good introduction but they don't explain anything in detail.

por Eddy P

Nov 09, 2019

helpful but only focus on a very introductory level..

por Kailyn W

Sep 17, 2019

I get errors when trying to run the notebooks

por Phuong N D

Sep 12, 2019

Good overview of tensorflow with keras API.

por Davide M

Dec 08, 2019

Very useful l stuff to start with ML

por mc.meng

Oct 31, 2019

No really understand it in fact.

por Otavio D Z B

Jan 23, 2020

Missed graded assigments

por Luigi s

Dec 26, 2019

Nice and clear

por Ezeuz

Oct 21, 2019

Concise explanations and nice demos leading into a very easily digested lessons. Covering every important fundamental aspects without being bloated by too much technicalities (which are only useful in a more advanced implementation). But again, basic is still basic. The quizzes def need more work as to not rely on a simplistic memorization problems (which almost doesn't exist on always-connected working environment) and instead should ask for actual concepts or understanding.

Def not deep enough if you pay for it, but a good one if you can finish it during the trial period.

por Corrie V S

Feb 08, 2020

Some lessons in this course were so repetitive that it seemed like a waste of time. Week 2, in particular, felt monotonous and really put a damper on my interest in the information. Despite there being some useful code to learn, Laurence talks though the code in video clips, and then does a screencast of himself talking through the same code in a workbook. I have really enjoyed the 2 courses prior to the NLP course in the TensorFlow in Practice Specialization, but this one seems less developed.

por Ethan V

Aug 25, 2019

I'm a bit disappointed with this specialization overall. I think I expected a deeper familiarity with tensorflow, more exposure to the TFData abstraction for large datasets, more low-level exposure to extending your models to fit a specific problem in your domain. Instead I feel like this specialiaztion would better be titled "Black box manipulation of the Keras API". That's a shame, given how solid the first specialization was.

por luis a

Oct 11, 2019

In my opinion, the course was too simple. There are many many concepts that are not covered properly. Even if they recommend going to the deep learning course from Andrew, I believe that at least could explain a bit more some parameters used in the functions and how actually work.

On the other side, you make cool thinks like text generation!

por Rajat Y

Nov 30, 2019

Since the course doesn't mention "Introduction" to NLP, I thought that the course will provide a detail insights to Natural Language Processing but the course only covers basics of it. Also as far as tensorflow is concerned I was expecting more hands-on experience in it.

por afshin m

Jan 17, 2020

week 2 and week3 are disorganized - the examples don't run without making modifications based on information in the forums.

However the overall course is worth it. I hope they pay more attention to making the examples accessible and making them work.