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

4.8
stars
18,834 classificações
2,036 avaliações

Sobre o curso

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....

Melhores avaliações

AM

Jul 01, 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 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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376 — 400 de {totalReviews} Avaliações para o Sequence Models

por Andrew S

Feb 15, 2018

The lectures are amazing. The assignments were difficult for me, but forced me to understand the concepts better. Really glad to have taken the course.

por Peter D

Feb 11, 2018

Excellent course that not only provides a good foundation for deep learning in general, but also gives some insights into more recent developments in AI.

por Subrata S

Aug 17, 2019

Very much satisfied, to its content. Learnt a lot, thanks a ton to Prof. Andrew Ng for your hard work on its topics & how making learning easy.

😁️☺️👍️

por Marcus B

Jun 28, 2019

Very effective at improving my understanding of RNNs (and its variants), Natural Language Processing, and some basics regarding working with audio data.

por Benjamin S S

Aug 16, 2018

Great course, but needs more checks for understanding during the lecture. Course would also benefit with a dedicated module on TensorFlow and/or Keras.

por Uyen H

Mar 16, 2018

The course is well-structured, and a nice introduction to sequence-related neural networks. The programming assignments cover interesting applications.

por Kseniia P

Jun 30, 2019

Probably the hardest course of the deeplearning.ai specialization, but made easier with thorough explanations of basic sequence models' architectures.

por Gökhan

Feb 19, 2018

This is an awesome course like other courses in this specialization. You can easily understand concepts and apply them thanks to Andrew and his team.

por Emmanuel J A

Feb 09, 2019

Great course on how RNNs work and how they are used to solve real problems (speech recognition, translation, names generation, music generation...).

por Ramesh N

Jan 19, 2019

Systematic, step by step approach to understanding sequence models and practical exercises to see them implemented with lots of guidance.

Thanks you!

por Shantanu B

Dec 19, 2018

The toughest course in the deep learning specialization for me. Learnt a lot. Made me ready for further readings and consolidation of the materials.

por Jun W

Nov 06, 2018

Concepts are covered very well. They are not very easy to grasp. But Professor Ng makes it easy. Hopefully, I will practice some of the knowledge.

por David G

Feb 08, 2018

Thank you Andrew for sharing all these great and latest staff in the AI Deep Learning field. Fantastic course. Will recommend it to all my IT staff.

por Fuat O

Jul 10, 2019

This course have been very useful to learn fundamentals of sequence models. I'm really very happy to apply this course and being able to finish it.

por Aishwarya R

May 07, 2019

Excellent course. RNN is a complicated topic which has been taught so easily. Thank you Professor Andrew Ng. Loved every programming exercises too.

por Alberto G

Sep 30, 2018

Very good quality course taught by Professor Andrew Ng. You will learn the basics to master Deep Learning Sequence Models using Keras / TensorFlow.

por Du L

Mar 03, 2018

Excellent course! I learned a lot. The assignment are not as well prepared as previous courses. Probably they'll be better as students raise errors

por Mihajlo

Feb 21, 2018

As a novice in seq-2-seq models, I learned so much! This is a great source of state-of-the art knowledge. I only wish it was at least 4 weeks long.

por WAI F C

Feb 17, 2018

Professor Ng's lectures provided intuitive ways to understand the complex recurrent neural networks and how to apply it in real world applications.

por Armaan

Sep 11, 2019

Andrew Ng does not hold anything back while discussing sequence models, including attention mechanisms and how to process and generate audio data.

por Wadigzon D

Feb 24, 2019

excellent, I did some speech recognition & neural nets in the past, I am surprised at how much the field has evolved, this was a great refreshener

por Yashwanth R V

Feb 20, 2018

The specialisation and this course have truly helped me gain a profound knowledge in theory as well as in programming of the Deep Learning models.

por Bruno F B V

Jan 07, 2020

It has been a long way, but in my opinion this is one of the best set of courses related to Deep Learning someone can find on the internet today.

por Daniel W

Sep 30, 2019

The course really well introduces to the key concepts of machine learning sequence modelling and NLP tasks. Furthermore, it is really up-to-date.

por Michel M

Sep 29, 2019

This module didn't look as prepared than the others. The assignments had far more errors than the others and also the video were less compelling