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

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47,496 classificações
5,445 avaliações

Sobre o curso

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....

Melhores avaliações

TG
1 de Dez de 2020

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

MG
30 de Mar de 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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4326 — 4350 de 5,407 Avaliações para o Structuring Machine Learning Projects

por Caoliangjie

20 de Fev de 2019

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por Dayvid V R d O

31 de Out de 2018

f

por Michele C

25 de Jul de 2018

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por Huifang L

7 de Mai de 2018

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por Yujie C

1 de Fev de 2018

por Jampana b

18 de Out de 2017

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por StudyExchange

21 de Ago de 2017

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por Ali K

29 de Mar de 2020

In this course, the instructor from his experience gained through several machine learning and deep learning projects explains how to prioritize tasks in a big machine learning projects. This course does not introduce the reader to CNN or RNN but rather makes the user aware of some ML/DL tips to make the most efficient use of time and resources. Some of the most important questions addressed in this course are: 1) Why a single evaluation metric is important and what are some of the widely used metrics? 2) What is human-level performance and is it a good estimate of Bayes error? 3) What is Orthogonalization in the context of ML tasks and why is it important? 4) How to measure avoidable bias, variance error, data mismatch etc? 5) How to address data mismatch error? What is transfer learning and how is it different from multi-tasking 6) Whether one should opt for traditional or end-to-end deep learning approach?

por jxtxzzw

3 de Abr de 2020

This course from setting machine learning strategies, setting goals, error analysis and data distribution, migration and multitasking learning, and depth of the end-to-end neural network training and so on about the strategy of machine learning, to strengthen the depth of the first two lessons we learn the basic knowledge have the very big help, deep understanding of the depth of these knowledge is very good for our study harder to learn knowledge, such as convolution neural network. The greatest help of this course is that it makes us understand how to solve problems encountered in the actual development process and what is the most reasonable solution through two case studies.

por Oleg P

30 de Nov de 2018

In this course, Andrew is giving very interesting practical insights into how to proceed in different project settings and how to speed up each iteration. Think of it as a stand-alone optimization algorithm for deep learning projects. What I'd further expect from this course are practical assignments, e.g., data acquisition and preprocessing patterns, data (image) augmentation, and transfer learning and multi-task learning (preferably building upon introduction to tensorflow in the previous course). As I already stated in the previous previews, optional assignments without grading would also do the work in motivating the students to do something on their own.

por Joe Z

6 de Jan de 2019

Great insights as usual for these courses. Especially useful are the strategic insights for dealing with data mismatch between train and dev/test data sets; my favorite is the idea of a "train-dev" set to separate variance from the differences in data distributions, which had never occurred to me despite it being obvious in hindsight. The "flight sim" tests were more challenging than I expected, and really helped to cement the concepts into memory. The only criticism is that some coding assignments would have been helpful to put these ideas into practice in a guided manner. Otherwise, great course as I have come to expect from Andrew.

por Vignesh R

1 de Jun de 2018

This course is more about explaining how to set your analysis universe(train/dev sets etc.) and where to go when u hit a road block i.e. when to concentrate on bias/variance etc.

Suggestions: Unlike other courses, no programming assignments here .. may be some programming assignments + Quiz in a case study format would have been more helpful. E.g. present a case, ask the student to write piece of code to calculate bias and other metrics, and then ask questions from the metrics derived instead of mentioning directly the values for human level error, Bayes estimate.

por AEAM

16 de Jun de 2019

This is a great course, something I will keep coming back to even after I'm done because it talks about strategy and rules of thumb re: Machine Learning/ Deep Learning approaches. It introduced me to certain concepts that were brand new for me and that was a great outcome for me. I wish the audio was better and the notes were better because writing on the small screen really hinders expressibility. I would rather have Dr. Ng write/draw on a chalk board than the small screen, I feel it really constrains his process. Still it's a great course!

por Tobie

24 de Out de 2017

A very quick course (significantly faster to complete than the preceding two courses in the specialization) that is usefully targeted at the practical aspects of how to go about developing a neural network. Prof Ng sets out a clear and logical approach to building, diagnosing issues, and iteratively improving models. The one critique I have is that a few of the topics are repeated from things already covered in the earlier courses and the editing of the videos is not done quite as well. Still, a very worthwhile use of very little time!

por Teodor C

14 de Jun de 2020

Very useful tips and insights on how to approach supervised ML projects. However a more in-depth case study would be interesting, to try to answer questions like: where does easily-available input data come from ? (sure CCD cameras & other tech, but but do not forget all those little labelling hands ^^) what makes the success of hand-design features in such or such domain ? can we bootstrap ML back into tools that help with valuable hand-design ? can unsupervised learning help with cleaning up input data for ML ?

por Julien B

21 de Set de 2017

The course content is very instructive and will greatly improve your performance on real world machine learning projects. Basically, this course gives you recipes to improve the performance of your model when something is wrong in your data and if you have not enough data.

Compared to the first two courses in the deep learning specialization, videos were a bit of lower quality, not completely edited and the course could have featured programming assignments, notably on transfer and multitask learning.

por Andre G

12 de Ago de 2021

t​he pronounciation of the presenter is increasingly difficult to understand. Some things are endlessly repeated. The videos have big editing problems. -- Overall it pretends to instill some wisdom on the learner, which is completely misplaced given the audience of this course ... much more real world experience would be needed here. From the perspective of a beginner, this was very theoretical and probably already forgotten before it becomes useful in real life. Not a fan of this course at all.

por Dorian P

22 de Fev de 2020

The course is absolutely fantastic, Andrew is a fantastic lecturer, however I could not give it 5 stars as there were parts of the videos which had not been edited well. There are parts where Andrew pauses and repeat himself again which seems intentionally done to allow the editor to appropriately remove the stumbled sections and make the talking seem continuous, but editing these sections has been overlooked. Its a shame as its a slight but noticeable issue in an otherwise flawless course.

por Gilles D

5 de Set de 2017

The course content is very interesting and opened my eyes of different strategies to improve the results of a Machine Learning project.

The recommendations also helped me greatly to dispel some of the myths and bluffs that run rampant in this developing field.

It makes me a better engineer. On the minus side, the course is not as polished as the two previous ones and some edit could help to cut in the content that is repeated.

Other than that, some great ideas and I am glad I took it

por Anton D

26 de Out de 2017

I liked how the course gives such insights that would help in progressing efficiently with a DL project in hand. This is the kind of thing that one needs to know about in addition to all the technical aspects.

I think the course would benefit from even more examples where a concrete project is examined and the student could see how the team was progressing: what were the iterations, what challenges were resolved, what were the intermediate results and of course: the final result.

por Joshua H

30 de Mai de 2020

The course gave an extremely wholistic insight into what applying deep learning theory may be like in a commercial context. It felt as if Andrew left no stone unturned, answering every question a student could have either in the video, or in the weekly quizzes. The only adjustment I'd have liked to see is Andrew spending more time elaborating on multi-task learning networks (such as how to initialize back propagation along a network which uses multi-task learning).

por Andreea A

15 de Fev de 2019

Liking this course is subjective. It is indeed based on the experience of others, but since experience can't always be generalized and transferred, the lectures are repetitive and bland (they are also badly edited in Week 2). On the other hand, the two "ML flight simulators" are really interesting and answering them is not obvious. It requires a lot of thinking and focus to choose correctly from apparently equivalent solutions, which might happen in real projects.

por Vrajesh I

4 de Jun de 2018

Course was very theoretical as compared to the previous 2 courses in this specialization, and maybe a programming assignment could have been included (optional) where in the student could maybe learn how to play around with distributing the train/dev/test data and calculating the errors. Personally since I like hands on stuff more, these are my two cents on what could have been better :) amazing work by the teaching team and others on the backend as always!! :D

por Rob S

11 de Jun de 2018

Once again, Andrew bringin' the heat!However, I docked a star for a couple of reasons. First off, I feel like there could be a bit more material here, perhaps an example notebook with noising and illustrating avoidable bias / variance / data mismatch.Most importantly though, I strongly, strongly recommend you go through the Week 2 Quiz (Autonomous Driving) and double check it for spelling/typing errors. There are quite a few of them!

por Rameses

15 de Nov de 2019

Great practical advice on actually structuring and implementing machine learning projects. However the case study approach is more useful for people already in the field and working on projects than for some of us who are not yet in the field but attempting to gain exposure and knowledge in Machine Learning. I guess the value of these case studies will be more apparent when I actually start implementing ML projects in the real world