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学生对 deeplearning.ai 提供的 神经网络与深度学习 的评价和反馈

4.9
102,949 个评分
20,597 条评论

课程概述

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....

热门审阅

MZ
Sep 12, 2018

This course is really great.The lectures are really easy to understand and grasp.The assignment instructions are really helpful and one does not need to know python before hand to complete the course.

NL
Oct 3, 2020

This course helps me to understand the basic concept of Deep Learning. However I think this course should include at least 1 week (or 2-3 videos) about math so learners can have a better understanding

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351 - 神经网络与深度学习 的 375 个评论(共 10,000 个)

创建者 Rahul D

Sep 6, 2020

I think this course was designed as what I need. I always want to learn from the teacher who taught me the basics and the development of the core part of the technology rather than touching its outer part and showing me where it can apply. This course tatught me how actually a neural network is built in the system and how it actually works. This course helped me lot to understand the nural networks. Everywhere on the internet what I found is just application of neural networks, no one focused on teaching the basic way designing neural networks. Thank you for this amazing course.

创建者 Sabarish V

Dec 3, 2018

The course takes a very direct approach to building your first neural network. It has very little maths, and the coding is extremely simplified in the assignments. For someone with a little bit of background, it wouldn't take more than a couple hours to be done with the course and running your first multi-layer model. If you have prior knowledge of NNs, machine learning, or calculus and vectorization, the course could feel a bit tedious. In this case, I'd recommend running the videos at 1.5x or 2x speed .

My only gripe is the quality of the audio. It could have been much better.

创建者 Eche I

Oct 12, 2018

I was initially running from the maths that underpins deep learning but this course made it some much easy to understand and gain intuition on how to operate deep learning. I thoroughly enjoyed Andrew's style of delivery and with his constant reassurance and I quote, "don't worry about it …", that holds very true and gradually makes you fall and gets the underlying linear algebra, calculus and derivatives that form the theoretical backbone of deep learning. The course really left me on the high and with a strong grounding to begin to press further in this deep learning journey.

创建者 Assaf B

Aug 22, 2018

If you had Prof Ng's "Machine learning" in the past, you expect perfection, so you may say that this course had imperfections such as Jupyter work instead of offline work, which confines your creativity when working on an exercise, and the course bit short, even for a chapter in a specialization.

However, when comparing to other courses, to nearly any other MOOC except "Machine learning" and perhaps "Complex Analysis", this course is still a DEFINITE five stars course. In content, in knowledge bang for your time invested, in usefulness, in teaching ability, and the list goes on.

创建者 Karim W E A

Aug 15, 2017

A lot of repeated material from Stanford's Introduction to Machine Learning, especially week 4. But of course, implementing all the assignments in Python, which is probably the most widely used language for ML and one of the most efficient ones as well; That was a big advantage over the material covered in Introduction to Machine Learning. Also, the material was explained in great detail and was tremendously organized. Would highly recommend the course to anyone who's looking into expanding their knowledge in Deep Learning. I can't wait to start Course 2 in the specialization!

创建者 Fabian Z

Aug 5, 2020

This is by far the best course I've had. It's detailed, intuitive, well-explained, and engaging. This course kept me having fun all the time. The brief questions in-between the videos are amazing. The videos aren't really that in-depth mathematically but the discussion forum provides those additional details for those who need it. I also really like the fact that you guys added the heroes of deep learning videos, they really are amazing people and I think it helps me to get to know the field better. Well, I'm out of words. To summarize, it's amazing and I really recommend it.

创建者 Ricardo S

Nov 24, 2017

I found this course to be a good introduction to neural networks and deep learning geared toward the uninitiated. For anyone with some experience however, the course can be rather easy, though it can serve as a review and it is fast enough to go through. I find it to be always good to start from basics, especially in the complex and always evolving field of machine learning, and this is an adequate starting point. I suggest that anyone taking this course with serious aims should seek to understand the mathematics introduced in it, though it is often mentioned as "not needed".

创建者 Mark M

Jun 20, 2019

The programming assignments in this course provide practical experience in building a deep learning neural network. The lectures are thorough and easy to understand, and they connect clearly to the quizzes and assignments. I'm grateful that Professor Ng and staff put this excellent resource together and make it accessible to all. I currently work in Cambodia, where I hope to introduce courses such as this to young people who have no educational opportunities. I highly recommend this course to all who wish to be aware of the incremental significance of AI in our time. Thanks!

创建者 Aayush K S

Apr 6, 2019

Really great course material. With minimal mathematics behind this, this course provided a great start to deep dive into deep learning. The video length and the quizzes and exercises were great. Also, since jupyter notebook was hosted by coursera itself, I didn't had to invest setting up infrastructure or downloading packages in my local system which was unlike AndrewNg's MachineLearning course which used Octave. This experience made completing the exercises more efficiently. helping me to utilize most of my time in solving it. Looking forward to complete the next courses.

创建者 Matteo C

Mar 8, 2018

A great course.

The topic is very compelling on its own, but the magic is all in the instructor. Andrew Ng is passionate and explains complex concepts by slowly building up to them. It was very important for me that he introduced the math and notation required, without assuming a lot of prior knowledge.

The programming assignments are worked on and submitted with Jupiter notebooks, which is great.

To make the most of this course, I would recommend doing the "Machine Learning" course from Andrew Ng, as it has a lot of relevant content and a good refresher on linear algebra.

创建者 Casey

Mar 7, 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.

创建者 Abdur R K

Dec 24, 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!

创建者 Dimitar T

Jul 23, 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.

创建者 Artur S

Dec 23, 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.

创建者 Francois R

Jun 29, 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,

创建者 Victor D L M

May 14, 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.

创建者 Abdelhak M

Aug 20, 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

创建者 Michio S

Sep 18, 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

创建者 Peter A

May 3, 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.

创建者 Hamed K

May 15, 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.

创建者 S. M F

Feb 20, 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!

创建者 JAGANNADHA L

Aug 22, 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.

创建者 Thorbjørn Ø B G

Aug 22, 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.

创建者 den

Jul 12, 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.

创建者 Mark P

Nov 1, 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.