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Отзывы учащихся о курсе Sequence Models от партнера deeplearning.ai

4.8
звезд
Оценки: 19,333
Рецензии: 2,101

О курсе

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....

Лучшие рецензии

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.

WK

Mar 14, 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!

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176–200 из 2,076 отзывов о курсе Sequence Models

автор: Vladimir B

Mar 14, 2018

Very good course. In quite short time you get understanding of a lot of principles and intuitions. The pace is good, explanations are consistent and clear, top-down approach from generic to specific, from simple to complex, very good instructional videos and interesting projects

автор: Kevin P B

Mar 02, 2018

Remarkably lucid exposition of complex learning architectures with directly applicable programming exercises. Highest recommendation. Thanks to the deep learning.ai staff for putting this entire specialization together and sharing their abundantly clear mastery of the subjects.

автор: Tun C

Aug 28, 2018

Some of the lectures were not quite up to par with professor Ng's standard. Some of the programming assignments were hard to follow and missing some details. Nevertheless, I came away with good understanding of sequence models and RNN. I can't thank enough. 5 stars from me.

автор: Julia C

May 27, 2019

This is very logical and especially the addition of probability between the words to improve predictions. It will be interesting to compare language , which language is easier to predict and why and study backward- how human creates them. We might learn something unexpected.

автор: Andrew M

Jul 09, 2019

Before starting the course, I wanted to have a strong knowledge of the basics of Natural Language Processing as I wanted to specialize in this domain. I am thoroughly satisfied at the end of this course. This course has given me the confidence to dive deeper into this domain.

автор: Koki S

Mar 30, 2019

Course of lectures are excellent, but please fix the following problem

week 1 Programming Assignments

Improvise a Jazz Solo with an LSTM Network - v3

Dimensionality in djmodel()

https://www.coursera.org/learn/nlp-sequence-models/discussions/weeks/1/threads/NAoSHgf0Eei8aw6tWi-efA

автор: Lucas G S P

Jan 21, 2019

I appreciate all the hard work and effort Andrew and his team puts in all his material.

I had hard time with most of Keras homework’s, I think it's hard to get the overall logic of a framework without an extensive explanation.

Besides that, the topics discussed are amazing.

автор: Haider A K

Dec 08, 2019

A great introduction to Recurrent Neural Network models with lots of examples (text generation, music generation, sentiment analysis, word embedding, speech recognition, attention-based machine translations etc.). Thanks a lot to Andrew and the team for this awesome course.

автор: Fang S

Sep 12, 2018

Andrew, again explained complicated structures very clearly. The first week might be a bit overwhelming and you might get lost with huge information overflow, but trust me, in week2 and week3 you will see how the dots are connected. Thank you Andrew for this amazing course.

автор: ABHINAV G

Feb 15, 2020

Thanks for making this course! I have been through all the courses in this specialization, and they are really excellent! Andrew has a great way of explaining things simply. It was easier for me to look at a couple of research papers after having gone through the lectures.

автор: Mohamad K

Feb 26, 2019

Prof Andrew such a great person. He teaching from the heart where 99 % of Prof not doing it today.

He summarized Deep learning+ Computer vision+ NLP in easy way. I am thankful for Coursera and Prof Andrew. I strongly recommend this course and all his courses to everybody .

автор: Akanksha D

Sep 26, 2018

Awesome. But the programming assignments need to be less erroneous and lectures and assignments could contain more technical and mathematical details to build the foundations. The programming assignments could be designed to allow the students to do more that spoonfeeding.

автор: Alouini M Y

Feb 20, 2018

This was for me the best course on the deeplearning.ai series since I am a complete novice regarding sequence models. Nonetheless, I have managed to learn a lot and the material was very good (with often state of the art techniques). The assignments were excellent as well!

автор: Aleksa G

Jan 20, 2019

I really liked this last course as I did not have much experience with NLP and with audio in general, as I did have with computer vision and image processing. The keyword detection model is really cool! After this specialization I am starting to build my own ML projects!

автор: Raivis J

Mar 18, 2019

This is the hardest course in the specialisation, and may take some extra effort. For practical assignments I recommend getting familiar with Keras syntax and workflow, as here there is little hand-holding here,. the focus is on actual model architecture and algorithms.

автор: Zein S

Feb 15, 2018

This is not a good course, and even not a good tutor.. It is a great course and Andrew is really incredible tutor... I like this course so much and got tons of benefits...

I am so happy to take this course..

PS: You can add this review to the sentiment analysis data set

автор: Guilherme

Jun 10, 2018

I really enjoyed the models presented in the course, as well as the accompanying exercises; I think Andrew and the team did a good job at giving intuition about the problems and coupling that with enough hands exercises to give better understanding of the implemtations

автор: Juan V M

Jul 01, 2018

Greatly explained. A lot of things explained in detail in just no that-much videos. Totally worth-it the specialisation!! Went from zero knowledge to know a lot of things about how it really works. Learned python (including keras and tensorflow) along the way as well.

автор: David W

Feb 19, 2018

Thanks for an enlightening course about sequences and how to apply machine learning concepts. This course has brought new light to how to solve some difficult sequencing tasks in my day to day work and I plan on looking for future courses from Andrew Ng in the future!

автор: Michał G

Mar 25, 2018

Very good course as all run by Andrew Ng :)

Even though I passed all the assignments I will go through the whole course again to make sure I understand everything very well.

On the other side it would be nice to get some reference to public data to train more on my own

автор: Raffaele T

Mar 09, 2018

It's a course about a lot of things: speech recognition, music generation, image captioning, machine translation, ecc.

It's highly recommended to study previous courses to fully understand the concepts.

There are some errors in the exercises because it's a new course.

автор: Rohan K

Mar 27, 2018

Pellucid, succinct, and the final bomb of this specialization! Felt a little dazed sometimes during the course and some things didn't really make some sense, but after things do settle after a re-watch. This course is so good it is actually re-watch recommended. :P

автор: Jonathan E

Jun 26, 2019

As usual, an amazing course by Professor Andrew Ng. The course manages to convey the most important elements of such a critical field, teaching from papers which were published only a couple of years ago. For anyone who wants to work in AI/ML, this is a must-do.

автор: Tín N T

Aug 22, 2019

This course helps me understand more about RNN and its variations, especially I learnt about the basic of Attention mechanism. What I enjoy the most in this course is its assignments, these are very practical and easy to understand. Thank you for reading these.

автор: WALEED E

Mar 20, 2019

This course was quite amazing in understanding with top papers discussed how machine translation and word trigger detection is carried using RNN. Code assignment was the best to show detailed steps of building RNN with a lot of hints to keep you moving forward