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Voltar para Redes neurais e aprendizagem profunda

Comentários e feedback de alunos de Redes neurais e aprendizagem profunda da instituição deeplearning.ai

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Sobre o curso

In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning. By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to your own applications. 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

SS

26 de nov de 2017

Fantastic introduction to deep NNs starting from the shallow case of logistic regression and generalizing across multiple layers. The material is very well structured and Dr. Ng is an amazing teacher.

GC

30 de mai de 2019

I have learnt a lot of tricks with numpy and I believe I have a better understanding of what a NN does. Now it does not look like a black box anymore. I look forward to see what's in the next courses!

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401 — 425 de 10,000 Avaliações para o Redes neurais e aprendizagem profunda

por Casey

7 de mar de 2018

Definitely recommended. I've taken various other deep-learning lessons and tutorials, but none of them gave me as much insight and practice as this course. I get the feeling that a lot of work went into the design of the course and even the homework problems.

A practical note for people considering the class: it'd be a good idea to review how matrix multiplication works before diving in, because that comes up again and again. There's a review in the course itself, but it doesn't come until week 4, and I found it necessary to analyze matrix dimensions as early as week 2.

por Abdur R K

24 de dez de 2017

Amazing course! I didn't even know python when I begun properly (only C++,C and C# and octave/MATLAB) but all the required functions/commands were introduced in a way that I faced no issues whatsoever. Of course I did need to google a lot of syntax differences (like for loops and stuff), but the experience was very fluid and everything connects extremely well to Andrew's famous Stanford ML course. If you're somebody who has only taken that course and are wondering if you can take the Deep learning specialization without having to study python first, I would say GO FOR IT!

por David P

10 de abr de 2021

Wasn't sure if I would like this going in, but I definitely recommend this to anyone interested in the topic, who has seen linear algebra and calculus before. Very well structured. It has been decades since my last "class" in calculus or linear algebra, but I was able to follow the math and instructions quite clearly. Instructor does an excellent balancing act, exposing the class to necessary details without going into unnecessary depth on derivations. Exercises are extremely easy but take you through all necessary steps, and you produce a simple, functioning classifier.

por Dimitar T

23 de jul de 2020

Andrew Ng is an amazing tutor and this is a great introduction in the world of Deep Learning. This course is for anyone interested in the topic, however, in my opinion it is advisable to first go through his ML course as this one feels like a direct continuation that builds on top of it. That beind said, the lectures are really well structured and the assignments are fun. One minor downside is that the assignments tend to hold your hand through the process, so to really test your knowledge, you may want to implement the algorithms on your own, using different datasets.

por Artur S

23 de dez de 2017

Very good course that can build understanding of neural networks and machine learning key concepts in a straight way. It is also interesting for some people, who thinks that he is advanced in machine learning, like me, but have only conceptual understanding of neural nets and no coding practice (just some experience with visual matlab plugin for NN).

Thanks for professor Ng and his deeplearning.ai team for preparing this course and for Coursera team for hosting it and making available.

P.S. It is so cool course that I'm helping with translation of this course to Russian.

por Francois R

29 de jun de 2018

The Super Excellent: How the course is built, with a lot of small block well placed on top of each others. The honest rendering (cutting over the hype) by Andrew Ng of DL and ML in general.The Excellent: The new notation and organization of the matrices (compared to Andrew Ng's previous Machine Learning course). The new explanation of backward propagation.The Good: The use of caches between Forward Prop. and Backward Prop., but also between the different functions. Note: The latter would benefit cleaner names and the usage of assert() on entry of the functions.Thanks,

por Victor D L M

14 de mai de 2019

Great introduction to Deep Learning and Neural Networks! I took the Machine Learning course offered by Stanford University and Professor Ng. and did not quite (fully) grasp what a Neural Network was doing. However, with this course, my intuition and understanding about Neural Networks and their inner workings was greatly enhanced. In addition, the course offers the most recent and best practices seen in the industry (e.g. introduction of the tanh and Relu activation functions ). I would recommend this course to anyone interested in Deep Learning and its applications.

por Abdelhak M

20 de ago de 2017

Hi Andrew,

It's just Awesome Andrew !.. it was a pleasure to achieve this course 8 years after I achieved your first course in 2011 (before coursera borns). Thanks to you and to all your team at stanford.

I can't wait for the next four courses :)

I was teaching the machine learning course to my students in the past 3 years and I plan to teach this current course to my students this year. They have the barrier of English language and I'm trying to do my best to explain the main ideas I understood from your course.

Abdelhak,

Professor at Mohammed V University

Rabat, Morocco

por Michio S

18 de set de 2020

Prof. Andrew Ng made it easy for the beginner to digest the systematic discipline of Deep Learning.

In addition, I cannot say enough thank you to all those teaching staffs and other peers who helped me better understand the course through Discussion Forum. In particular, I would like to mention that my smooth learning progress owed to Mr. Paul T Mielke. Thanks, Paul!

After the completion of this course, I would like to view Prof. Ng's course CS230 at Stanford via YouTube videos to enhance the understanding of what I learned from the Coursera course.

Thank you very much

por Peter A

3 de mai de 2020

This course is a perfect introduction to neural networks. It builds on simple concepts to then put together larger processes that link and compound these concepts together. In all, the user begins to see how something like fitting a Logistic Regression model is not that different from some other learning models that are regarded as more complex, such as neural networks. Surely, as one continues, these things will diverge, but this course does a good job os using the knowledge users likely already have, in order to better introduce them to some more complex concepts.

por Hamed K

15 de mai de 2020

I liked the course. Without any previous knowledge of neural networks or deep learning, now I can claim that I know the basics and the reasoning behind fundamental steps of deep learning. The slides that were downloadable also were a great point of this course, since it made it easier to take and add your own notes. The instructor also explained topics quite clearly. I also like the system of grading for programming assignments where were divided in different sections unlike some similar courses on Coursera. Totally recommended for people interested in this topic.

por S. M F

20 de fev de 2020

The best thing about this course was that the course gets easier and easier! Prof. Andrew Ng, the community, and the arrangement of the assignments always got my back! I never felt like "I must skip this line as this is out of my scope". With a little bit of hard work, anyone can build any layer NN for image classification problem with 80%+ accuracy. If there is any scope for improvement, I'd say that the notebooks get disconnected frequently, which should be improved. Otherwise, this is the best course I've ever had! Thanks all who are involved with this course!

por JAGANNADHA L

22 de ago de 2017

Amazingly well done course. The best thing I liked about is the attention to detail that Prof. Ng has paid. For example I always had tons of problems with the rank 1 matrix. The frustration levels used to be so high. However, being the consummate practitioner and teacher, he identified what kind of problems one encounters when one learns python and deep learning for the very first time. It was more like symphony. I tried other courses in other websites. But this easily is the best of it all. I strongly recommend it to everyone who wants to get into deep learning.

por Thorbjørn Ø B G

22 de ago de 2017

This is an excellent starting point for learning about Neural Networks and Deep Learning.

Many technical derivations and details are left out but this is only a plus. These details are much better learned with a working knowledge of the basics/implementation of neural networks. Besides, it is clearly stated whenever such details are omitted. This course will not make you an immediate expert in coding nor neural networks but it is the best starting point out there for a broad audience. Regarding becoming an expert, always remember that Rome wasn't build in one day.

por den

12 de jul de 2020

Andrew Ng literally hacked the teaching method of deep learning. I have also finished his 2011 zero to hero machine learning course. I can easily say that, Let if flow people. Take the journey with him.

You need to know Python syntax and semantics (types, functions, lists, tuples, Especially Dictionaries.). Otherwise you will be going on a adventure. Watch out for "cache" dragon.

I suggest you to learn partial derivatives.

And everything else you will need in Deep Learning will be given to you in a perfect order.

I love you Coursera Team <3 Have a great time.

por Mark P

1 de nov de 2017

Great quick overview and introduction to neural networks and basic deep neural nets. Great intro for those without a lot of the required math background. I would have liked to see some more quizzes (even if optional) on the derivations of the gradients. That was a bit of black box and we were just given the equations. I also thought it was a bit odd to have examples-by-column rather than rows. Assuming this was done to simplify notation (less transposes) - but it's counter to almost every other presentation in machine learning and stats that use example by rows.

por Zhengyang L

12 de jul de 2020

This course has provided me the most suitable level of math details. Many other books and tutorials tend to overlook the fact that learners are usually not experts in machine learning and statistics. An example is the explanation of why the cost function of logistic regression should be the form provided. When I first saw the formula in a book, I was confused and could not rationalize it myself, which troubled me a lot because I don't think I can implement an algorithm without knowing the cost function. This course is really good for beginners who cares "why".

por dsp

23 de ago de 2018

Well motivated. Clearly structured. Generalizing from Logistic Regression over shallow Neural Network to Deep Neural Networks was easy to follow and reinforced the structure of the approach. I overall liked the presentation of the maths and assume that it is well suited for an audience of differing affinity to maths. For myself, I will have to do the calculations again on my own to get a real grip on them. [Writing db (=something that should grow with steeper b) for dL/db (=which shrinks with steeper b, given the same change in L) still feels wrong.] Thanks!

por Raimond L

19 de ago de 2017

Nice basic course, gives a clear look at what is happening inside neural networks, all details are explained in quite clear and understandable form with practical tasks of implementing everything, so that you really know what is going on.

After that course you will have a knowledge of how to implement a simple neural network and it's learning algorithm from zero. Also you will get some knowledge about matrices operations, derivatives and python programming.

I do highly recommend this course for novices and for more skilled people. It was a positive experience.

por Nicholas M W

2 de jan de 2018

Excellent presentation of the material. The homework assignments made this approachable by holding my hand as I learned "how to walk" with matrices and multilayer neural networks. I feel like there could have been one more "do everything yourself" assignment, where we had to build another L-layer neural network completely from scratch, but maybe that isn't the point of this course, since I expect I'll be using keras or something in "the real world". An optional quiz involving some of the derivations for some equations might have been a nice stretch, as well.

por Augden S

11 de out de 2018

A solid introduction into discussing the basics of machine learning. Although I had to research some details on specifics topics which I could not completely understand in the course, that was my own problem, really. The basic steps for creating a neural network and understanding the functions behind initializing parameters, forward propagation, cost and backward propagation are explained well, and since the assignments are in python, I've learned a few packages and helpful coding hacks to better implement efficiency in programs. Overall, I would recommend!

por Akshay B S

6 de set de 2017

It is a great course to get started on Neural Networks and their practical implementation. The whole course is constructed keeping the end result of building an OPTIMIZED program in python for building a neural network and everything connects together in the final programming assignment. Not only do you learn what are neural networks and how they work but you also learn very importantly how to code in a very optimized manner so that you decrease the training time as much as possible. Definitely a great course, looking forward to complete the specialization.

por Ning P

26 de jan de 2021

I understand so much more about deep learning. Learning about some basic Mathematics of calculus and derivatives really helps in understanding. Professor Andrew also repeated lots of things several time which is good in order to check whether I got that particular part correctly or not. Somehow having no python basic at all will definitely be difficult in order to finish the assignment since the video lesson did not give lots of example on this but eventually after following along and spending quite lots of time on the Lab do help understand how it works.

por Hendra B

28 de jan de 2020

This course is the best course to start learning deep learning. You will enjoy the step-by-step creation of shallow and deep neural networks.

Frankly speaking, I am amazed at the creativity and brilliance of the Andrew Ng's team for preparing the programming assignments and quizzes. Therefore, I am speechless.

Last but not least, I am also really grateful and thankful for Coursera who has given me Financial Aid for this course. In return for this act of kindness, I am able to finish it before any deadlines.

Thank you deeplearning.ai!

Thank you Coursera!

Thank

por Mattias K

4 de nov de 2017

Great intro to deep learning. Although it's a bit repetitive at times, especially coding bits - one is not really forced to understand the components at times but can instead just follow instructions and copy paste bits and pieces. Would for example have appreciated that more time was spent on explaining the details of derivation of backwards propagation especially within "deep domain". The intuition is clear, but either forcing the user to do (or giving a link to) a step by step derivation would have been useful and saved time. Thanks for a great course.