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学生对 deeplearning.ai 提供的 序列模型 的评价和反馈

4.8
27,231 个评分
3,246 条评论

课程概述

In the fifth course of the Deep Learning Specialization, you will become familiar with sequence models and their exciting applications such as speech recognition, music synthesis, chatbots, machine translation, natural language processing (NLP), and more. By the end, you will be able to build and train Recurrent Neural Networks (RNNs) and commonly-used variants such as GRUs and LSTMs; apply RNNs to Character-level Language Modeling; gain experience with natural language processing and Word Embeddings; and use HuggingFace tokenizers and transformer models to solve different NLP tasks such as NER and Question Answering. The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career....

热门审阅

JY
Oct 29, 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.

AM
Jun 30, 2019

The course is very good and has taught me the all the important concepts required to build a sequence model. The assignments are also very neatly and precisely designed for the real world application.

筛选依据:

2701 - 序列模型 的 2725 个评论(共 3,242 个)

创建者 Cazaubieilh G

Mar 18, 2020

To the point ; sometimes it would be nice to explain the research papers more in depth, and link other courses to have more formal mathematical explanations

创建者 ignacio v

Oct 18, 2018

Give us one more week to learn RNN for time series in economics, finance, etc!

Programming Exercises need more hints and more training in simple Keras models

创建者 Péter D

Feb 8, 2018

Well-made course, but unfortunately there are tons of mistakes in the programming assignments - in the comments, formulas, even in the prepared code pieces.

创建者 Matheus B

Feb 3, 2018

The best course in the Deep Learning Specialization. Really good and well explained. There are some problems and mistakes in the problem assignments though.

创建者 Дубровицкий А А

Jul 24, 2019

Somes basics, tiny bit of theory, a bit of keras and insights for practical tasks. Some strage errors in notebook exercises makes it 2x time longer though.

创建者 Markus B

Dec 5, 2018

Great course. The only tiny flaw is that the introduction to Tensorflow and Keras was a bit shallow so that I struggled a bit with programming these parts.

创建者 Andreea A

Mar 31, 2019

Instructive course with useful concepts. However, there were many more mistakes in the notebooks compared to the previous 4 courses in the specialization.

创建者 shengtian z

Mar 22, 2018

Awesome introduction, but feels like Andrew is a little bit rushing since it is the last course in the series, I dont feel it is as clear as other courses

创建者 Mahendra S S

Jul 21, 2020

The CNN course was better in this series of courses. This course is also good, but more content could be provided. Still the best small course out there.

创建者 SHAHAPURKAR S M

May 16, 2020

Faced issues regarding assignment submissions. Otherwise, the course is perfect. Would upgrade my review to 5 stars if this issue seems to be fixed later

创建者 Alex M

Feb 15, 2020

Es buen, algo extenso, pero suficiente para avanzar. Algo importante es actualizar los cursos con los nuevos algoritmos, al menos uno, por ejemplo BERT.

创建者 minsq n

Aug 19, 2019

This course is quite challenging, but at least the concepts were well explained. Wished that Andrew and his team could conduct a crash course on Keras :)

创建者 Maxim V

Oct 5, 2019

A great intro to RNN, LSTM, GRU, Activation. Programming assignments are rather messy though (unlike those in the other courses of this specialisation).

创建者 Harshit S

May 25, 2019

Great course, I like the practical application and assignments discussed in this course , wish latest research papers were also discussed in the course,

创建者 Jun W

May 16, 2019

This course introduces mainly about RNN, GRU and LSTM. Great assignments. 1 score off for the in-correction in assignments. 4.5 scores from me actually.

创建者 Octav I

Dec 23, 2018

Great lectures, really well explained, assignments could request more from the trainee to devise the logic instead of having it already defined for him.

创建者 Marcela H B

Jun 28, 2021

Good course, however I would like to have more Transformers application in the last part as well as some information regarding the fine tuning of them.

创建者 Thierry L

Jun 30, 2020

Thank you very much for all the work you have done. I have learned so many things... I will try to use this stuff in the coming months. Yours, Thierry

创建者 Tiago C G M

Mar 3, 2019

The course is really good, I would recommend it to anyone who wants to learn the subject, but it lacks support from the staff in the discussion forums.

创建者 Tomasz D

Oct 3, 2020

Very good course. Some editing issues in the lectures and small issues with the programming exercises (outdated Keras instructions and documentation).

创建者 Nicola P

Feb 14, 2018

The lectures are excellent. The assignments are an extremely valid trace of significant deep learning application, while they lack a bit of challenge.

创建者 Alon M

Oct 13, 2018

As always, this course is great. however, for some reason this course is much more difficult then the others, and i feel as if it is packed too much.

创建者 Michael S

Jul 12, 2018

Really good course, like the others. A bit too black box in some of the programming exercises, so I expect to struggle when developing my own models.

创建者 Yifan E X

Apr 10, 2018

The videos are really informative and well structured. However, the exams felt like Keras tests. A detailed Keras tutorial would have been helpful.

创建者 Takeo S

Mar 28, 2019

It was great course,

I wish we have more speech recognition contents

Hope, you add new course a bit focus on audio/speech recognition etc

Thank you!