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Отзывы учащихся о курсе модели последовательности от партнера

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О курсе

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....

Лучшие рецензии


30 июня 2019 г.

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.


29 окт. 2018 г.

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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251–275 из 3,365 отзывов о курсе модели последовательности

автор: Xiang J

9 нояб. 2019 г.

overall it is another great course, clear explain on the RNN, LSTM, GRU, etc. really like the assignment to implement RNN from scratch. assignments related to Keras needs "googling" outside resources, and there is still some keras homework to be done in order to fully understand the assignment code.

автор: Jampana b

14 февр. 2018 г.

Thank you very much instructors. I learnt both fundamentals of deep learning and application of them in simple and efficient way. I have been long and fruitful journey with Andrew Ng. I learnt sound mathematics required for deep learning, tensorflow software, applications of sequence and RNN models.

автор: 邓佳阳

8 июня 2020 г.


Thank you very much for the course provided by the teacher. The original course experiment has relevant research needs for LSTM. This course provides the teaching of relevant knowledge points. Thank you again for the teacher and platform!

автор: Pawan S S

8 янв. 2021 г.

A very good course to learn the fundamentals of Sequence models. It contain a lot of important developments of the sequence models and together with the programming assignments, it makes easier to learn. I found this course very easy to follow and understand the theories. I highly recommend this.

автор: TANVEER M

25 авг. 2019 г.

I have always found difficult how RNN and LSTM works as theretically I was not getting a clear picture how it was working .The programming assignments helped clear my doubts and I got a clear understanding to a lot of extent how this mechanism is working and how it is useful in speech synthesis.

автор: Zhiming C

14 июня 2020 г.

This course introduces the basic idea of RNN, GRU and LSTM models. They are obviously harder than the CNN models and the concepts are not so easy to understand. Thanks to the systematic introduction! Together wit the excises I can understand better the theory from the applications. It's great!

автор: Andrei N

21 сент. 2019 г.

The content, examples, assignments, and quizzes are thoroughly developed. All the courses of the specialization share the same notation and lead a student from basic concepts to complex ones helping to develop an intuition on each step. The best course on topic of Deep Learning one could find.

автор: Nilesh K S

5 дек. 2018 г.

It was a great experience to learn from Andrew NG and it helped a lot to me personally and professionally. I have gained so much confidence after completing these set of 5 courses and looking forward to build some cool projects on my own using the concepts that i have learned in past 5 months.

автор: Mihir T

23 сент. 2018 г.

A great course on latest technology used in NLP. The course is well structured and provides an in-depth knowledge on sequence models. This course is a all-in-one package for starting your career in NLP. Mr. Andrew is a great teacher, and explains everything in a very simple yet effective way.

автор: Wesley H

8 авг. 2019 г.

Great finish to the specialisation. I have learned a lot of the core details of how to proceed with my own Deep Learning projects. My one piece of feedback would be for an intermediary step, that requires more of the programming myself, as a lot of the intricate coding has already been done.

автор: Toshi T

8 июня 2022 г.

Great selection of topics, clear explanations from Dr. Ng, and coding exercises to allow the understanding of the topics. Perhaps I would have liked to see some Time series analysis in the Sequential Networks parts, but even though, it was a great course. Thank you Dr. Ng and all your team!

автор: Challa S

7 мая 2020 г.

The course content is very good but the mistakes in the videos are being mentioned after the video. This is making us get confused a bit. It would be good if those errors are mentioned before the video itself so that we can look into that before watching the video and get prepared for that.

автор: Wingyan C

1 мар. 2022 г.

Excellent teaching, materials, and organization! It's great to include state-of-the-art technologies like Transformer and LSTM. I would recommend also teaching some practical skills (like TensorFlow) that students can apply directly in practical programming (beyond the course assignments).

автор: Isaac S J C

5 нояб. 2018 г.

Great appreciation to Dr. Andrew Ng. The course has been incredibly well taught. Thank you so much for your enlightening lectures. I very much enjoyed the course, and I think it is very well structured and organized. The forum was very helpful when I got stuck in the programming exercises.

автор: Mikhail K

22 июля 2022 г.

It is a great course, and the same applies to the whole Deep Learning Specialisation. It is very nice to see that many people all around the world can relatively easily get access to these learning materials as well as to the problem exercises. Thank you for making it widely available !

автор: Anujay S

30 сент. 2019 г.

I am amazed with the learning experience of Seq2Seq Modules created by team! Loved the way it's taught by Andrew Ng and the hands on experience helped the mentee very well. Keep building such courses, would like to contribute more in this space as in research or products.

автор: Kyle L

15 февр. 2018 г.

Insightful detail on model architectures and how they influence (and are influenced by) data generation for sequence-based applications. For those that have grasped the theory behind DNNs and are interested in applying ML to language and text, I highly recommend checking out this course!

автор: Cezary B

19 июня 2022 г.

Great course, well explained. Sometimes the course material gets a bit too general but this is done when the details would be unbearable to cover. The overview of all the machine learning conepts is an amazing start for purusing anything deep learning related in my novice point of view.

автор: AS A

7 апр. 2021 г.

I like the course. It's beneficial and clear. Also, the concept is clear.

for more improvement

I would suggest that for jupyter implementation :

I hope you put 2 versions of the code

thus, the student can have a choice to work on a famous frame

1- using Tensorflow (TF)

2- using PyTorch

автор: Kumar S

30 авг. 2019 г.

This course was really awsome,learning has been fun in all the 4 courses, the number of new things learnt in this course was remarkable.Even the mot complicated things were taught in such a way that it never seemed tough.Doing assignments really helped to make concepts even more clear.

автор: Lee F

17 февр. 2018 г.

Fantastic course! Presents both the theory and practical uses in a straightforward manner that is easy to grasp. Programming assignments are a mix of NumPy and Keras API, with the former being more illustrative of the inner workings of RNNs and the latter being more practically useful.

автор: Nicolas C

17 февр. 2018 г.

Excellent! Amazing! Such good quality of lecture and assignments. Thank you Andrew and team for giving me such a good overview of what i can use this for. I feel as though this series dramatically lowered the barriers to entry for me to get started on any ML project i decide to. Thanks

автор: Manmohan K

2 июля 2020 г.

No better introductory material. I suggest doing NLP specialization by after this though I have still not tried it out myself yet but hoping to do it some time. Thank you Andrew! I got emotional in your last video of the course. You are such an example for educators <3

автор: Aman K

13 февр. 2018 г.

This was by far the Best Course and Specialization that I have done. Thank You Coursera and Thank You Sir Andrew NG . You have made me confident and able in the Field of Deep Learning. I am grateful to you Sir. I will try my best to use this knowledge as a superpower in the right way.

автор: Laks P

30 апр. 2022 г.

1) Course over all is good.

2) I had a slight difficulty understanding the WW4 section, Transformer concept "guts" even though I happed to score 100 on the course. Possibly with some more practice, I should be able to understnd Transformer, multi-head attention model concepts better.