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Avis et commentaires pour d'étudiants pour modèles de séquences par deeplearning.ai

4.8
étoiles
28,072 évaluations
3,363 avis

À propos du cours

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....

Meilleurs avis

AM

30 juin 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

29 oct. 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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3326 - 3350 sur 3,364 Avis pour modèles de séquences

par yuvaraj

11 déc. 2020

The course videos were very lengthy and difficult to follow. Many topics discussed in course video were not part of programming assignment

par Simeon S

18 mars 2020

Good introduction to the concepts. Really poor quality videos and exercises. Very frustrating when working on the assignments.

par David L

28 juin 2020

Good lectures. Programming assignments are useless fill-in-the-blank exercises, you don't really learn much from them.

par Thomas A

10 oct. 2019

The programming assignments really are like pulling teeth. There's not really enough guidance leading up to them.

par Mark

24 oct. 2018

The course videos and the programming assignments were lacking. And there was no support in the forums.

par Jeffrey S

2 juin 2018

Spent more time trying to work around a buggy grader than learning the underlying concepts.

par Frank T

23 oct. 2019

Too hard to understand compared to the previous coursed in this specification.

par Hamid A

13 nov. 2020

Was very difficult. please add more expiation of mathematical equations.

par Sukeesh

18 avr. 2020

Little unsatisfied with the final part of the specialization.

par Clashing P

12 sept. 2021

assignments are very hard and needs lots and lots of search

par Arsh K

20 août 2019

Lack of Keras training made it often hard to do layer code.

par Tom T

9 janv. 2020

This course needs more instruction on Keras.

par Mark N

12 févr. 2018

Poor explanation for alot of things

par Milica M

10 mai 2020

boring and uninformative

par João P B D

4 janv. 2019

Too difficult.

par Martin B

11 mars 2018

Needs work.

par Alex L

5 mars 2018

I feel sad.

par zhesihuang

3 mars 2019

sucks

par Selina M

6 août 2021

The course overall taught me new things, but I am still kinda unsure how to exactly use it.

The exercises and explanations weren't as enlightening as earlier and unfortunately left me rather confused, despite passing 100%. You definitely need to consult a lot of other sources for understanding the topic.

The last transformer exercise left me stunned though in how bad it was. When I understood something it contained obvious mathematical inconsistencies. It was the first time I needed the forum help, which is outside the coursera website and they force you to sign up in addition to coursera.

The tutor reacted fast but extremely patronising, going so far as pretending mistakes in the exercise didn't exist, but very eager to blame me for using an outdated version, that I wasn't using.

Did not enjoy the experience.

par Aldiyar K

12 mars 2021

Oversimplifying material, such as not showing any math foundations and proofs, does not lead to an intrinsic understanding of the material as well as fill-the-gap assignments do not enhance comprehension.

I understand that the course is intended for the broad audience but will one be able to implement those Keras and TensorFlow algorithms on a moderately complex problem, which is the ultimate goal of these courses? Highly doubt it because the code is pre-written for students and step-by-step guide is provided. In my opinion, one could go straight to assignments and induct / deduct the answers.

par devesh r

19 oct. 2020

I love Dr. Andrew, I seriously do. He has inspired me in the field of Deep Learning like no one else did. But I detest how this course is made so expensive and in a wrong direction. I subscribed and paid $225 and I still was not given a decent amount of time to finish the course, even when I asked for extension. If this is the way these courses are, its better of learning from youtube. It is not worth the money

par Franjo I

10 mai 2020

Dry and uninformative. Immense space for improvement. Corrections should be made to videos instead of having numerous revisions comments after lecture. Some variables introduced haphazardly. Notation not explained well, some clashing with linear algebra conventions. Coding exercises are elementary hyper guided.

par Krishnamurthy N

2 avr. 2021

Grader is the worst and no help even in the forums inspite of getting the same output the grader is still failing and no one is telling why. Forced to drop out of this course now

par Inaki O d L

7 avr. 2021

Clearly the less structured and more confusing Course within the DeepLearning Specialization, a bittersweet end to an otherwise great set of courses. Quite disappointed.

par Deleted A

8 sept. 2020

I would like to abandon this course and stop paying for a course I want to prepare better before maybe launching a new attempt..