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学生对 deeplearning.ai 提供的 序列模型 的评价和反馈

4.8
28,122 个评分

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

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.

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.

筛选依据:

2801 - 序列模型 的 2825 个评论(共 3,373 个)

创建者 joris b

Feb 15, 2018

If the programming exercises weren't plagued by some bugs, I would have given 5 star. It's a very complex subject matter, but Andrew takes you through it by the hand.

创建者 Robert L

Aug 28, 2020

I feel week 2 and week 3 materials were covered a bit too quick. Would appreciate more explanation of the implementation details of beam search and activation model.

创建者 Alexander V

May 19, 2022

This last course (and especially the Week 4 material about transformers) feels much more rushed and less detailed than the previous courses in that specialization.

创建者 Amir A

Aug 31, 2019

It was really helpful, every topics explained very well. However, in my standpoint of view, It did not cover some part in sequence learning, like graphical models.

创建者 Jurgen R

Apr 4, 2018

Really nice course. Very informative. Unfortunately some programming exercises were a little buggy (the grader especially)...only a total reset of notebook helped!

创建者 Fábio N R

Aug 12, 2022

Great course, I learned a lot. The notebooks in courses 4 and 5 are a bit too long, they take a considerable amount of time, much more than the videos and quizes.

创建者 Ferdinando R

Jul 3, 2022

Wonderful, I just felt at times that the exercises were more similar to general programming than sequence modelling. Still, 10/10 course, would totally recommend.

创建者 dann p

May 22, 2018

this course provide an adequate and what you want to know about recurrent neural network but it does require lots of programming skills to accomplish this course.

创建者 Tom S

Apr 26, 2018

Good course, but I needed more time than expected, especially for the exercises. For me, that was the most demanding course out of the 5 from that specialization.

创建者 Timothy A

May 15, 2020

A lot of cool material covered from RNNs to LSTMs to Sequence Modeling. But it is a lot to grasp and a lot to understand. Overall, rigor and course is decent.

创建者 Yogeshwar D

Apr 29, 2020

programming assignments are not teaching us to code independently because of the helpers functions given in utils file. Feels like copy pasting the assignments

创建者 SIRAM N N D S K

Jun 6, 2020

It is a really awesome course for those who want to get started with deep learning methods in NLP.

Got a very clear insight about GRU,LSTM,RNN,Word Embeddings.

创建者 Rohan S

Dec 17, 2019

The course is really good, one star less because it requires keras understanding to complete assignments properly. Including a basic intro of keras will help

创建者 Nitin S

Jul 11, 2020

The time allocated to some of the assigments should be increased. The estimated time in many cases seems to assume that one is aware of Keras and Tensorflow

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.