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Comentários e feedback de alunos de Structuring Machine Learning Projects da instituição deeplearning.ai

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38,941 classificações
4,271 avaliações

Sobre o curso

You will learn how to build a successful machine learning project. If you aspire to be a technical leader in AI, and know how to set direction for your team's work, this course will show you how. Much of this content has never been taught elsewhere, and is drawn from my experience building and shipping many deep learning products. This course also has two "flight simulators" that let you practice decision-making as a machine learning project leader. This provides "industry experience" that you might otherwise get only after years of ML work experience. After 2 weeks, you will: - Understand how to diagnose errors in a machine learning system, and - Be able to prioritize the most promising directions for reducing error - Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance - Know how to apply end-to-end learning, transfer learning, and multi-task learning I've seen teams waste months or years through not understanding the principles taught in this course. I hope this two week course will save you months of time. This is a standalone course, and you can take this so long as you have basic machine learning knowledge. This is the third course in the Deep Learning Specialization....

Melhores avaliações

MG

Mar 31, 2020

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.

AM

Nov 23, 2017

I learned so many things in this module. I learned that how to do error analysys and different kind of the learning techniques. Thanks Professor Andrew Ng to provide such a valuable and updated stuff.

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351 — 375 de {totalReviews} Avaliações para o Structuring Machine Learning Projects

por 吳沛燊(Pei-shen W

Aug 31, 2017

Very useful ! It is a common problem of getting lost in ML projects, although the guidance seems abstract at first glance, it proves to be invaluable when ever we are in the midst of struggling for better modeling performance

por Gourav K

Aug 22, 2019

Thank You, Professor Ng, for creating so much valuable learning. The values to those are added and we get ambitious and inspired being through the interviews you took with great Deep Learning and Machine Learning scientists!

por David C

Jul 11, 2018

I came into this course with the bias that it would be the least applicable of the five in the series-- however, I really feel that the information conveyed was extremely important for practical application of deep learning.

por Salim L

Mar 25, 2018

Really helpful project strategy for Deep Learning that can save many months of work. While this course is a bit repetitive at times, Andrew Ng's recommendations are hugely important and his simulation tests quite innovative.

por Sanket D

May 25, 2020

In depth learning of most sought and required concepts and giving insight on how to structure a ML project from scratch practically. The quizzes are just wow! They give a very good insight of how ML projects are structured!

por Justin K

Apr 27, 2020

Short course with no programming exercises, but full of good information that is immediately useful such as where your time will be best spent depending on situations you're likely to encounter in pretty much every project.

por Aloysius F

Mar 20, 2020

Excellent, this really goes into the nuance of successfully executing a project. Setting up an initial system is not that difficult. Understanding the sources of error a systematically resolving requires judgment and graft.

por amin s

Jul 26, 2019

This course is great. Recommend it to anyone working on Deep Learning projects. Saved me lots of time, and taught me how to systematically think about my problem and opened new windows to improve my network. Thanks, Andrew!

por Sean C

Feb 15, 2018

This was a valuable stepping stone in applying Andrew Ng's other teachings to realistic scenarios. The "simulators" were actually a great representation of realistic machine learning project issues & potential resolutions.

por Kurt K

Nov 27, 2017

A clear explanation of a difficult subject with an emphasis on being able to create and to understand your own neural networks.- Plus in this module how to allocate your resources so you can achieve a successful project.

por Asad A

Sep 02, 2019

Really good insights into the practical aspects of structuring projects. Large scale deep learning/ ML is as much about people management and strategic prioritization as it is about complex algorithms and big data handling

por 邱依强

Mar 10, 2018

This is a very useful course since that you can get an impotant instruction to build your own project. You can reduce your time cost and iterate quickly to produce more value by using the knowladges taught by this course.

por arulvenugopal

Jan 08, 2018

Good. However, understanding the importance of strategy, either additional scenario quiz (the simulation type quiz is good) or a programming assignment would reinforce the understanding (given short duration of the course)

por Henderika V B

May 17, 2020

I loved the translation of all the different succesfactors to the daily practice and examples in the course. It gave me an general idea of what to look out for when identifying my own AI problems and defining a NN for it.

por Abhishek R

Sep 15, 2019

This was probably the most useful course of the entire specialization with real-world examples, tips, tricks and techniques on how to approach the problems in Machine Learning world as a whole and Deep Learning in general

por Francisco R

Sep 28, 2017

Even though it's a short course and it doesn't have programming assignments, which I love doing, it has though these case study, which are quite fun and educative, helping you to get started in a Machine Learning project.

por Andres S

May 24, 2020

I liked this course because I gave me an idea of real situations I could face working on Machine Learning, but I think a little code would've been helpful, for example, to better understand how to do a transfer knowledge

por Ladislav Š

Oct 20, 2019

This part of Deep learning specialization is similar to Machine Learning Yearning written by prof. Andrew Ng. I read the whole book and for me this was mostly a repetitive information - however, very useful and relevant.

por TANVEER M

Aug 18, 2019

The course taught me about errors how to minimise the errors .How we can improve model performance.satisficing and optimising metrics.Overall the course was quite good.The case studies I found more interesting to solve.

por Akshat A

May 16, 2020

Amazing Course! I generally don't feel like I gain much from lectures and would prefer reading but I'm really glad I took this course, gave me lots of insights into how one would go about improving performance quickly.

por Madalena R

Nov 20, 2019

I really enjoyed this course, I think Andrew has a lot of knowledge on the subject matter and he is able to explain it in a very detailed and understandable manner. The interviews were a plus and also very interesting!

por Utsav A

Apr 24, 2020

This case was useful for getting an experienced way of approaching the real-world problems of ML. The quizzes further added to the application of the basics learnt throughout the course. Overall, it was a good course!

por 谢宁翔

Apr 08, 2020

very much wonderful. especially the simulation process, which extracts the pure logic decision process during implementing DNN without actually experiencing all the detailed procedures which are not really challenging

por Lewis C

Jan 27, 2020

Good course. Very interesting!

Having done the course, most of the ideas seem fairly obvious. However, the chances of me coming up with them on my own are almost 0.

Therefore I think the training has been successful.

por Muhammad S K

Aug 01, 2019

It was an amazing experience and I learn a lot of new Machine Learning strategies and error analysis techniques that will help me a lot in my future research work. Thanks a lot, Mr. Andrew, you are an awesome speaker.