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Comentários e feedback de alunos de Sequence Models da instituição deeplearning.ai

4.8
estrelas
26,718 classificações
3,158 avaliações

Sobre o curso

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....

Melhores avaliações

WK
13 de Mar de 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!

AM
30 de Jun de 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.

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351 — 375 de 3,130 Avaliações para o Sequence Models

por Daniel G

16 de Mar de 2018

My only constructive criticism is: more difficult homework, or more homework in general. The programming assignments seem very important for internalizing concepts, and this course covers a lot of material in a short period of time.

por S.KRISHNA K

17 de Fev de 2021

It wasn't easy to finish. Comparatively 3 weeks only, but the content is hard. Compared to Computer Vision techniques, NLP techniques are harder to develop an intuition around. But a great learning experience. Interesting projects.

por Maria V

10 de Jan de 2021

Every course with Andrew is worth it. He is great at explaining details and providing examples even for quite complex topics.

The most useful thing for me in Andrew's are programming assignments. I enjoyed them immensely, thank you!

por Jagruti P

27 de Ago de 2020

A very good course on Sequence Models starting from the basics to designing a trigger word detection model. I will surely recommend the course if you are a deep learning enthusiast or you want to brush up your skills in this field.

por Aliaksandr S

22 de Out de 2018

Excellent material; though VERY dense. Notebooks still have some issues, but forums are really helpful. I feel that first 3 courses were much better prepared; though CNNs/RNNs are still super-useful despite not being 100% polished.

por Junliang Z

17 de Out de 2018

Really funny section about sequence model with interesting examples, helping me to understand how to learn the pattern of sequence data and transfer it to useful information. arouse my curiosity about other application in this area

por Sundar S

13 de Set de 2018

Amazing set of courses. Learned a ton of information that I will directly use at my current work and beyond. And Andrew is a fantastic teacher that really engages with his students (despite this being an on-demand online course).

por Nitesh K

24 de Set de 2020

I enjoyed this course thoroughly. Excellent platform to start Deep Learning. This course has touched almost every concept of deep learning. It has provided me the solid base now I am confident enough to take it to the next level.

por AKSHAY K C

21 de Mar de 2020

The course was very well structured from the basics of RNN progressing slowly towards LSTM, GRU, word embedding and attention model finally. Kudos to the instructor and the team for providing such a good course on sequence models.

por Meer H A

2 de Jun de 2019

Thank you Andrew! Learned great things from this deep learning specialization course. The knowledge and certification I gained will help long way, in shaping my career. Thanks once again to the creators of this wonderful course :)

por Carlos A C G

11 de Jun de 2020

Amazing job once again by Andrew and his team. The world needs much more of this! Specially more implementations that can be put into use easily in real-life apps and projects. Now we know the theory, we can put it into practice.

por ongole s s

27 de Mai de 2020

I have learnt a lot from this course and it is very interesting to be part of this course as i understood the concepts of NLP in deep learning which is the most fascinating technology to learn and implement in real world problems

por Sergio B S

14 de Set de 2018

The first week of this course is maybe the most harder week of all the Deep Learning Specialization. But, with Sequence Models I have understand infinite better the great possibilities of this techniques for improving the world.

por Beltus W N

19 de Out de 2020

After finishing this course, I'm so fired up by the potential projects I can build with this knowledge that I feel adrenaline coursing through my veins. Thank you Prof. Andrew Ng. Thank you Deeplearning.ai team. You're the best.

por Shashti K N M

8 de Jun de 2020

The Course was Excellent. Sir's teaching was Excellent. I understood the techniques of Sequence Models and Natural Language Processing. The Programming Assignments were Excellent. The Deep Learning Specialization was Excellent.

por George Z

29 de Set de 2019

Amazing course and what a finish line. I only with the graded assignments are revisited as few of them have bugs in them. Also I hope the Word2Vec algorithm and word embedding in general is explained better and with exact steps.

por Sam D

24 de Fev de 2019

Awesome course and specialization. Now, to implement everything I learned in my own programs, and of course I will be sure to revisit the videos until everything becomes second nature. Learn, program, improve and repeat. Thanks!

por Himanshu G

26 de Abr de 2020

This was particularly intensive course of this whole series, learned a lot.

Thanks to Prof. Andrew NG, accept a Natmastak Pranam from this Student of yours, will always be indebted for what I have learned here. You are the Best

por Rahi A

8 de Jan de 2020

I have many of books and blogs related to RNN, but was not clear and confident about it. And after studying only the first week video and lectures, I am so confident and happy that cant tell you!!! Thank you so much Andrew...;)

por leonardo d

4 de Dez de 2019

It seems like there are several and very useful RNN models. Many of them are very good at specific tasks, and if you take this course you will be abe to understand and implement many of them. It was a really amazing experience.

por Sandeep P

27 de Jun de 2018

An excellent introduction to the theory and practice on recurrent deep neural networks. Great usage of all the 4 courses in this series to culminate with this course as a great finish to deep learning theory and implementation.

por Rafael E

10 de Fev de 2018

Yet another amazing class! I'm so grateful for the existence of these classes. It makes mastering deep learning very much easier. My thanks to Andrew, and all others who have worked so hard to make this course possible! :-)

por Hristo B

25 de Fev de 2019

Most notably, an exercise guides one through the building of a recurrent network from scratch. More exercises show the value of different architectures and make the learner proficient in using neural network libraries (Keras).

por Aparna D

30 de Out de 2018

This was quite a tough one.. But it was almost magical when the outputs of the assignment were successfully completed. Excellent. The discussion forums helped a lot, as the instructions were not very clear to novices like me.

por Jeffrey T

2 de Abr de 2020

Amazing course, Andrew Ng presents the material in a concise and intuitive manner. It would be nice to have access to all of the material needed to fool around with the assignments on our computers in an offline environment.