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Voltar para Structuring Machine Learning Projects

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

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43,570 classificações
4,907 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

JB

Jul 02, 2020

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

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.

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401 — 425 de 4,854 Avaliações para o Structuring Machine Learning Projects

por Girish G

May 03, 2020

This particular course details all the minute aspects needed to have a better model. All the concepts were explained clearly in the course. I felt this course to be a like a "icing on the cake" to basic Neural Network course.

por GUSTAVO E Z

Mar 06, 2020

Once more Andrew is greaqt teachng and very clear in his explanations. This course let me learn how to improve the development of a Deep learning project aiming at the right parameters and algorithms to be worked on the road.

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 Gudivada R K

Jun 21, 2020

Much needed course for those who are in their starting/middle stages of DL/ML projects. This course gonna play a vital role in their projects. The explanation from Andrew Ng was interesting with real-time scenarios examples.

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 Marko N

Aug 26, 2020

Pretty interesting ideas on how you can improve your deep learning system. It teaches you a number of strategies that help you identify the most promising things to try. Quizzes are especially interesting in this course.

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 Andrés 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 Naresh K P

Jul 25, 2020

This course helped me understand how to prioritize problems that we encounter in Machine Learning space. On the surface this might look simple, but I think this course will have a huge impact as I implement ML problems.

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.