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Kursteilnehmer-Bewertung und -Feedback für Sequence Models von deeplearning.ai

4.8
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23,682 Bewertungen
2,751 Bewertungen

Über den Kurs

This course will teach you how to build models for natural language, audio, and other sequence data. Thanks to deep learning, sequence algorithms are working far better than just two years ago, and this is enabling numerous exciting applications in speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and many others. You will: - Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs. - Be able to apply sequence models to natural language problems, including text synthesis. - Be able to apply sequence models to audio applications, including speech recognition and music synthesis. This is the fifth and final course of the Deep Learning Specialization. deeplearning.ai is also partnering with the NVIDIA Deep Learning Institute (DLI) in Course 5, Sequence Models, to provide a programming assignment on Machine Translation with deep learning. You will have the opportunity to build a deep learning project with cutting-edge, industry-relevant content....

Top-Bewertungen

WK

Mar 14, 2018

I was really happy because I could learn deep learning from Andrew Ng.\n\nThe lectures were fantastic and amazing.\n\nI was able to catch really important concepts of sequence models.\n\nThanks a lot!

JY

Oct 30, 2018

The lectures covers lots of SOTA deep learning algorithms and the lectures are well-designed and easy to understand. The programming assignment is really good to enhance the understanding of lectures.

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2401 - 2425 von 2,725 Bewertungen für Sequence Models

von Xiao

Mar 08, 2018

Some techniques for keras need to be clarified. Generally a good course

von Vamvakaris M

Sep 08, 2019

It required coding on keras and tensorflow not appropriate introduced.

von Rafael B d S

Aug 07, 2019

The Course is great! But the programming assignments has too many bugs

von Gorden

Dec 12, 2018

it's very difficult to submit last programming exercise "trigger word"

von Ishan S

Jun 27, 2020

More clarification on what we are doing in the programming exercises

von Emanuel G

Dec 13, 2018

Great introduction to LSTMs, RNNs, GRUs, NLP and speech recognition.

von Nilesh R

Mar 20, 2018

Great content but I felt it was bit rushed and squeezed in 3 weeks .

von Alex M

Mar 14, 2018

The quality was a bit down but still very worthwhile and interesting

von Vivek K

Jul 20, 2018

Great practical experience. Would have preferred a bit more theory.

von Fady B

Jun 01, 2018

it covered a lot of interesting topics but it was a bit high level.

von Seyyed A S

Jun 18, 2020

great course to understand intuition of sequence modeling for NLP.

von guolianghu

Apr 05, 2020

课程虽然很短,只有三周的课程,但难度明显比之前四门课程要大,编程练习一共有7个,第一周的三个是最难的。但仍然是最优秀的深度学习课程。

von Bobby A

Jul 02, 2019

Well explained, I feel like it could go a bit more in depth though

von Martin T

Feb 13, 2018

Muy buen curso, resulta sumamente estimulante el ejemplo de woebot

von vikas c

Jun 20, 2019

The good course as the theoretical basis for RNN and other models

von Shilpa S

Mar 19, 2019

Attention Models is not that clear. Everything else is excellent.

von Andres R

Apr 02, 2018

Excellent lectures. Some programming exercises need more clarity.

von Armand L

Apr 28, 2019

Very hard, but not your fault, very good course ! Thank you !!!!

von Jose L F L

May 29, 2019

El curso esta muy bien peor deben añadir subtitulos en español

von Marisa F

Nov 18, 2018

I think this course needs to have a continuation to go deeper.

von Alex N

Mar 27, 2018

Grader was wrong sometimes. Typos everywhere in the notebooks.

von Akshay G

May 14, 2020

Good course but can add more models to get a deep dive in NLP

von Xinyu Y

Jul 01, 2019

There is some noise in the video which is greatly disturbing.

von SeptemberHX

Jun 30, 2018

Maybe should give some advice about the future learning path.

von Vitor M A

May 28, 2020

Great course focus on natural processing and music examples.