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

4.8
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Оценки: 45,739
Рецензии: 5,219

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

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.

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!).

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76–100 из 5,162 отзывов о курсе Structuring Machine Learning Projects

автор: GurArpan S D

21 окт. 2017 г.

This may be an optional course in the deep learning specialization, but I beg to differ. If you plan to do actually start a project in machine learning, it is imperative you take this course. You could finish your project ten times faster with a fraction of the work. All in all, every one of these courses in this specialization have been beautifully organized and taught very well.

Thank you so much for offering this course along with the others in the specialization!

автор: MANRAJ S C

27 окт. 2019 г.

The course is really great! It offers an in-depth understanding of the practical aspects and applications where deep learning can be applied. Most importantly the content in this course will help you iterate faster with your machine learning problem by doing error analysis on it. This course tells you exactly what can be done in which situation to improve the performance by analyzing the data and other statistical aspects of the data as well as the algorithm.

автор: SK A F

10 янв. 2020 г.

Error analysis and Learning the methodology to handle the errors. Besides the traditional systematic way of performance analysis like train-dev-test and cross-validation, Andrew focused on data mismatch and train-dev data. These two are the most important things that are described very well. Like other courses, Andrew was very good to describe the real-life practice. In this course, two simulation quiz really helps a lot to deeply understand the application.

автор: Ryan M

14 сент. 2017 г.

This is a VERY valuable course, with tons of practical advice on how to understand problems all machine learning / neural network developers experience and how to tackle them. I have never seen such high quality practical advice in any textbook or in any other course before, and I believe that even those who are not taking the full five course deep learning specialization should seriously considered this course. Another truly excellent Andrew Ng course!

автор: Chan-Se-Yeun

1 мая 2018 г.

This course introduces some general principles for developing a deep learning project. It points out the difference of setting of train/dev/test sets between deep learning and traditional machine learning. That's a practical advice. And it's notable to include human performance and regard it as Bayesian bound, almost the best we would expect an algorithm to achieve. That saves you from spending unnecessary time to make a subtle improvement. Learnt a lot!

автор: Curt D

8 сент. 2018 г.

In this course I learned about ways to approach some of the real world challenges that I have already faced on some of my own projects. For example, what actions should you consider when you find a significant number of labeling errors in the dev/test sets that affects your ROC. I also was motivated by the last module on end to end training and the interview with Ruslan Salakhutdinov to pursue an end to end training idea that I have been thinking about.

автор: 刘尧

1 нояб. 2018 г.

Great Course! Many students will choose to skip this course since they think there are less knowledges than other course in the 5-course specialization. But I have to tell you: this is the best course in the specialization, because you can learn a lot knowledges especially skills and experiences in practice from this course that you can't learn from other books, courses or universities. BTW, I'm not telling that the other 4 courses are not important.

автор: Daniel S

17 дек. 2017 г.

Andrew Ng is brilliant! I have never seen such a great tutor in my life. He bring extremely useful concepts and explains them so easily in a way the concepts stay in your mind.

Like the backprop algorithms he talks, he has learned so much from his old course and he has made great improvements to focus on New people. He sure has a good deep network up his brain that has gone through lot of iterations (without overfitting) with beautiful set of features.

автор: Pawan S S

8 янв. 2021 г.

One of the rare courses to learn about structuring our own deep learning projects. I found the material I learnt in this course very useful in my carrier as well. All the subject matter are well structured and the flow of the module is very easy to follow and understand. Together with the case studies, it was very enjoyable and very easy to test the applicability of the knowledge gained. I highly recommend this course for any deep learning enthusiast.

автор: Kryštof C

7 нояб. 2018 г.

It is very good probe to practice. I would very appreciate to take this course before I have started in machine learning. It would help me to realize some mistakes I have maid before. On the other hand, for people, who have some experience with machine learning, some chapters are being over-explained, as the topics are quite clear to those people. Overall: I would recommend this course to everyone, who wants to start with his/her own NN training.

автор: Teguh H

29 нояб. 2017 г.

No coding at all. But this is one of the best course on AI, because it does not talk about coding or anything, but most importantly, the one thing that is not taught by many others. Experience of Andrew Ng trials and errors in approaching ML projects. How to create structure, how to observe what results to see. In short this course is like 'how to save time in doing AI projects and make optimal use of it, avoid trial error which can cost months.'

автор: Luis C G

19 окт. 2017 г.

Despite of its relative simplicity (from a technical point of view), it is probably one of the most practical courses I have taken in Coursera. Even though it only mentions deep learning, the overall methodology can be applied to any machine learning work. It is important to get familiar with the heart of the models, but it is probably even more important how to work on an end-to-end machine learning project. In summary: Highly recommendable!

автор: VIJAY S P

18 нояб. 2020 г.

Hello learners, and respected teachers ! As I go through this course, I was not clear about how to use test set. train set and dev set, was not able to rectify about how to break data set for so closely prediction. As we have teacher like Andrew ng, we won't miss anything about structuring machine learning project.

This course is really helpful for me and will recommend to all learns who want have command on ML & Deep Learning.

автор: Danilo Đ

4 янв. 2018 г.

Unlike most of the Deep Learning knowledge which can be found in literature and other MOOCs, this course provides you with insights that can only be acquired trough (often painful) trial and error. Here you learn how to approach Deep Learning projects, how to avoid most common mistakes, and how to quickly identify errors in your model.

Do yourselves a favor, and finish this course before taking on your very first DL project.

автор: Johnathan T

4 сент. 2017 г.

This course is my favorite so far. It has really given me a way to systematically and strategically set up a machine learning experiment and iterate in a way that make sense. For me the toughest part of ML projects has always been figuring out where and how to start. Now that I have some solid guidelines to follow, I don't feel as anxious about jumping into a new problem and it turning into a wild goose chase. Thanks a lot!

автор: Tamim-Ul-Haq M

17 июля 2020 г.

Such an amazing course. This course should be done by every Deep Learning researcher or enthusiast. If the previous course taught how to debug Neural Networks then this course teaches how to perform advanced debugging on the dataset. The contents of this course are invaluable and not taught in most institutions and they are not known to the wider community that actually follow the guidelines and techniques presented here.

автор: Shankar G

3 июля 2018 г.

Wow! This course was more of real time application scenarios and the kind of tweeks to build, transform learning plus multi-task learning was excellent. The end-to-end learning with a split approach of solving was really something new I found in this learning. Not to forget the application level quizzes were so tricky it was challenging to understand and interpret the possible solutions but, was great learning experience.

автор: Kayne A D

4 февр. 2020 г.

More of a general comment for the specialization but I love the Andrew and the teaching team have set up the content delivery. Simple, clear and well-paced delivery with consistent use of well-considered examples. On top of that, the summaries are great representations of key concepts. I am greatly appreciating the entire specialization and seeing the bigger picture in terms of why it is structured as such. Thank you!

автор: Andrei K

21 апр. 2020 г.

I find this particular course in the whole specialization especially useful so far. Andrew teaches great strategy that helps think and act on deep learning projects in a more systematical way, and does so with crystal clean examples. Quizzes in this course, similar to flight simulator, are great at ensuring you can apply the principles you have just learnt and see where your understanding is a little bit vague.

автор: Robert K

18 нояб. 2017 г.

Fantastic lectures combined with case-studies for real world applications. In this course you don't program, but don't underestimate the ability to abstract out and systematically assess your thinking. This could speed up your project development and save you tone of time. Any potential employers would also be happy that you know some practical aspects of implementing a deep neural network for a particular use.

автор: Shringar K

28 июля 2019 г.

Honestly speaking, this is the best course in the whole deep learning specialization. This course is the one which tells us what to do as a Deep learning engineer in real world scenario.

People can do the coding and everything, but without proper directions the product might fail.

Andrew Sir has given his expertise in a very neat and compact way, good enough for starting our own research or whatever we want to.

автор: George Z

4 авг. 2019 г.

Amazing 3rd course, I learned so much related to error analysis, bias, variance, data mismatch, data synthesis as well as transfer learning, multi-task learning, end to end deep learning and more. I really loved both case studies in the end of each week. The interviews especially with Andrej Karpathy was my favorite :) Excellent best practices and strategies that you don't learn from any other course or book.

автор: Johan B

22 сент. 2017 г.

This course in the specialization is less about how to build a model but gives you a structured way of how to approach a deeplearning project. It shows how much some manual (and maybe boring) counting can speed up your project and that starting with a simple model and iterating on that often outperformes very detailed thinking about your project at forehand.

The practical tips Andrew shares are very valuable!

автор: Sebastian E G

18 авг. 2017 г.

Liked this way more than I thought I would. Machine learning project management is vital in a professional setting (I would assume), and I often leave it as an afterthought. It's not just building the fanciest model, it's about how to iterate from an okay model to your best model in an efficient manner. This course teaches you what to look for with your results and pinpoints what areas to focus on to improve.

автор: Johannes B

27 февр. 2018 г.

Very nice introduction to the aspects of a machine learning project that is not covered other places, but is very important. Most of it is very intuitive and comes as no surprise, but it is still very usefull to collect it into a single course. It is a good resource to have in case you are in doubt about how to structure your project, where to focus your energies and how to make progress in a systematic way.