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Отзывы учащихся о курсе Structuring Machine Learning Projects от партнера deeplearning.ai

4.8
звезд
Оценки: 47,754
Рецензии: 5,482

О курсе

In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader. By the end, you will be able to diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance; and apply end-to-end learning, transfer learning, and multi-task learning. This is also a standalone course for learners who have basic machine learning knowledge. This course draws on Andrew Ng’s experience building and shipping many deep learning products. If you aspire to become a technical leader who can set the direction for an AI team, this course provides the "industry experience" that you might otherwise get only after years of ML work experience. The Deep Learning Specialization is our 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 gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI....

Лучшие рецензии

JB
1 июля 2020 г.

While the information from this course was awesome I would've liked some hand on projects to get the information running. Nonetheless, the two simulation task were the best (more would've been neat!).

MG
30 мар. 2020 г.

It is very nice to have a very experienced deep learning practitioner showing you the "magic" of making DNN works. That is usually passed from Professor to graduate student, but is available here now.

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401–425 из 5,446 отзывов о курсе Structuring Machine Learning Projects

автор: yi-chun t

2 сент. 2020 г.

Andrew again delivered a great course! He talked about his real-world experience in creating and delivering ML projects in this course that is super helpful. I never see any other instructors who cover this important aspect of ML projects.

автор: Kursat P

26 авг. 2020 г.

Great course! It would also be very helpful to have the final slides of handwritten notes by Andrew as .pdf files to take a look at the content later without having to watch the videos. This course is extremely helpful. Thanks to all of you!

автор: Edson C

4 авг. 2020 г.

this course is very wonderful. I was really impressed with the amount of information acquired and that will be very useful when developing my ML applications. thank you very much for these many years of experience transmitted in this course.

автор: Tanmay K

20 мар. 2020 г.

Amazing course taught by Professor Andrew Ng. I love the piratical insights that I gained through this course. I believe this course is a must have for someone who wishes to tackle deep learning problems in a systematic and organized manner.

автор: Cyprien H

7 нояб. 2018 г.

Very instructive course, full of practical and actionable advice to focus on the right problems in an ML project. The "flight simulators" are concrete examples of decisions one has to make in an ML project and it is good to practice with it.

автор: Jean V

12 мая 2019 г.

It does a great job of providing guidance on how you would plan a deep learning project. Transfer learning in particular is a very intriguing approach to leveraging previous work to speed up training a new neural network for your new task.

автор: Jeroen M

26 янв. 2018 г.

Material is excellent, Andrew is a brilliant teacher. Learned a lot. (Minor complaint: week 2's questions are formulated in a confusing way, making it hard to answer correctly even if you've understood the material of the course perfectly.)

автор: Deleted A

7 нояб. 2017 г.

@Andrew Ng: Your statement "And I think that phonemes are an artifact created by human linguists. I actually think that phonemes are a fantasy of linguists." in: Whether to use end-to-end deep learning" Week 2, ROCKS !!!! GREAT and agree...

автор: Dr. M E J I

1 сент. 2017 г.

This is an excellent course for anyone in Deep Learning, Data Science, or Machine Learning. It is a little on the short side, but packed with good ideas about how to structure your projects when considering various differing data scenarios.

автор: Prerana H B

16 апр. 2020 г.

Exceptionally good course.It gives brief idea of how and what strategies should be used while approaching any problem or building the system .Also gives idea about how to improve the efficiency of already build system and upto what extend.

автор: rahul m

10 апр. 2020 г.

Excellent course that discusses a lot of details and nuances about machine learning and deep learning that are drawn from Andrew's own experience as a prominent researcher and pioneer in the field . I feel I gained a lot from this course .

автор: Madagama G B S

25 янв. 2020 г.

This course helped me to systematically analyze errors in deep learning implementations. The machine learning flight simulator is a great way quickly learn how to address issues you would face in making practical machine learning problems.

автор: Fawad H

8 нояб. 2019 г.

This Course is best for all level and it teaches in the best way to how to make your project to do well and how to suggest solution and how to detect problems in the training of the neural network. Thank you Andrew for making this course.

автор: Yingxiang Z

11 июля 2019 г.

Very useful introduction to the real applied machine learning procedures. This course enables us to know exactly what steps to take in different phases of a project, and could potentially saves us a lot of time by avoiding useless efforts.

автор: Wong C H

18 февр. 2018 г.

"Experience can only be learnt by practicing" This course showed us some useful scenario which I think is very likely to be encountered in future projects. I think this will help to save time to develop deep learning model in the future.

автор: yugandhar n

29 авг. 2017 г.

Initially I thought It would be boring. But after taking the course, I feel the difference. Once again, Andrew Ng rocked it with composition of this course and quiz. I feel this is must course in deep learning, who is working in industry.

автор: Daniel

14 мар. 2020 г.

It's a theoretical approach of Machine Learning projects that gives a lot of awesome insights of many real world problems that you face when building your model. It's a short course with great insights ! I definitely recommend taking it.

автор: Khaled J

20 мая 2019 г.

Excellent class with practical advise to accelerate the application of best practices based on Andrew's experience. I would highly recommend this to practitioners wanting to save a lot of time learning these best practices the hard way.

автор: sujith

28 окт. 2018 г.

Excellent course on understanding how and what to prioritise in ML projects. Not just helpful for people leading ML teams, but also for people who are doing some independent projects. ML is a lot of fun when you do experiments for fun :)

автор: Mohamed C S

19 июля 2018 г.

Excellent Course, though it is an optional course, it is really worth taking it!

The Use case studies are just excellent! You can really have a taste of the problems encountered when you have to manage a deep learning project. Great work!

автор: Omid M

20 янв. 2018 г.

Great tips!

Minor issue: often the request for feedback for a lecture came right at the beginning of the lecture, covering big portion of the video ('was this video helpful'! ). It was annoying (I couldn't figure out how to minimize it).

автор: Ashish P

19 июля 2020 г.

Amazing course. This course is really a practical understanding of what DL is. Apart from learning the algorithms practical aspects are very necessary for DL and this course provided me with the same.

Thanks to ANDREW NG for this course.

автор: Long C

26 июня 2020 г.

Great and detailed strategies especially for people working on a machine learning projects. With good strategies, time and money may be saved. A really good complimentary material to Andrew's new digital book: Machine Learning Yearning.

автор: Aditya G

23 дек. 2019 г.

Highly recommended as it helps one think how to improve their ML models. Just do a 60/40 split and hoping for the best result is not the way to go, and this course definitely helps unveiling how to remove bias and variance from a model.

автор: Chulhoon J

15 окт. 2018 г.

this course has very practical and helpful advices to solve problems related to the deep learning algorithms. I believe those valuable advices and tips will be able to reduce tremendous times and efforts when you stuck with the problem.