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学生对 提供的 神经网络与深度学习 的评价和反馈

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20,273 条评论


If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "superpower" that will let you build AI systems that just weren't possible a few years ago. In this course, you will learn the foundations of deep learning. When you finish this class, you will: - Understand the major technology trends driving Deep Learning - Be able to build, train and apply fully connected deep neural networks - Know how to implement efficient (vectorized) neural networks - Understand the key parameters in a neural network's architecture This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description. So after completing it, you will be able to apply deep learning to a your own applications. If you are looking for a job in AI, after this course you will also be able to answer basic interview questions. This is the first course of the Deep Learning Specialization....


Dec 5, 2020

This course helped me understand the basics of neural network. After this course I learned to built base neural network model. Looking forward to do the next course of the deeplearning specialization.

Dec 3, 2018

Extremely helpful review of the basics, rooted in mathematics, but not overly cumbersome. Very clear, and example coding exercises greatly improved my understanding of the importance of vectorization.


451 - 神经网络与深度学习 的 475 个评论(共 10,000 个)

创建者 shaila a

Jul 11, 2020

I am a fan of all courses delivered by Andrew Ng. This one gives a thorough understanding of shallow and deep Neural Networks. After the course, you can be sure to have a sound understanding of how a model is built from scratch. The assignments are also organized in a way to reduce your effort on redundant tasks like creating the structure of functions. The focus is only on having you write the code that tests your understanding. I really enjoyed this course and I am looking forward to the next one. Thanks :)

创建者 Wilson C

May 14, 2020

Following the lectures and completiong quizzes and programming assignments puts the student through rigorous math, the math in the course is overwhelming at a college math/engineering level - but as the student continues throughout the course, the redundancy of theory does get absorbed and by the end of the course the student develops a solid understanding of the course material. The programming assignments implement full scale deep layer neural networks with practical applications to illustrate the concepts.

创建者 Tanmay G

Aug 22, 2018

This was an amazing with a lot of new and interesting things to learn. I am really glad that I decided to take it. Its approach toward neural networks is quite easily understandable and allows oneself to use those concepts as he wishes. The programming assignments are a really big help as well. You can learn all the math but without the programming skills, there is hardly any point in doing deep learning. A special thanks to Prof. Andrew Ng. I was already a fan, but this course was just amazing. Thanks a lot.

创建者 Kai-Peter M

Oct 28, 2019

Great course!!! The best online course I have ever taken! I enjoyed almost every day I participated in that course, really an educational treasure! It is so comprehensive and detailed at the same time. Due to the good presentation of the topics it was really understandable. The only thing I would wish for future participants: please make it easier to get the complete Jupyter notebook environments from the Coursera platform once completed. I spent a lot of time here - even after consuming the related blogs.

创建者 Vishnu J

Jul 22, 2018

The intro course has been a phenomenal experience learning. The concepts were clearly explained along with derivations. I thank Coursera, Andrew Ng and all others who were involved in this for taking this massive step in teaching deep learning and AI. I would be happy to take more practical oriented courses under this banner especially computer vision, NLP, AI in specific. Another suggestion from me would be to include lessons on building neural networks from libraries like tensorflow, pytorch, keras etc.

创建者 Martin V

May 2, 2018

Very helpful course. Great, well prepared assignments! Even without python knowledge I was able to code essential parts of algorithms. Practical assignments were really good reward at the and of each week and a motivation for me to keep going. You will not be forced to learn python in parallel but occasionally I have to read library reference guide to debug. I also installed python locally to test syntax and get more in, but it is not necessary, provided python jupyter notebooks is also usable for this.


Nov 12, 2017

Thank you for the easy-to-follow content. The explanation about back propagation in details is great. The Python code is elegant and should be a good starting point for learners to make more progress in expanding it.

Some time assignment submission gave errors even there is no problem with networking issue. This definitely need to be improved, or learners need to resubmit many times.

If you need translation of the course to Vietnamese language, let me know. I will do it for free, for my Vietnamese students.

创建者 Brandon E

Sep 10, 2017

A great introduction to neural networks! The videos and assignments were helpful, and the repetition helps things sink in. I would've preferred more mathematical rigor and a little less hand-holding in the assignments, but I understand that this course is meant to appeal to a wider audience and it does a good job of being approachable. I particularly enjoyed the weekly "Heroes of Deep Learning" videos, and tips and pros/cons of studying machine learning in industry vs. academia. I'd recommend this course.

创建者 César J N R

Aug 23, 2017

It is a relly nice course, well explained as Andrew Ng. has always done. Because it is still a new course, there are few erratas of course, but those are being already corrected. I suggest a lot to take the Machine Learning course by Stanford University here on Coursera first, unless you already know about Neural networks, since sometimes there are things that you should know. These kind of courses have made me going really deep into Data Science and I'm quire sure this specialization will help. Thanks !

创建者 Sumeet K H M

Apr 17, 2020

Thanks a lot Dr. Andrew NG. According to me, this course ranks very high in terms of course content, delivery and practical assignments in Python. Specially, the assignments are designed in such a way that almost all the concepts are revisited and the conceptual understanding is re-intensified during the assignments. The assignments also help us to understand how a neural network can be implemented in practice in a systematic way by breaking it into subcomponents, which is the most enriching experience.

创建者 Subianto W

Jun 10, 2019

Excellent class, wonderful instructor and extensive practice problems. The theoretical explanations on deep networks are very thorough with the math behind it. Unlike other deep learning courses that take shortcuts with using pre-made keras or pytorch libraries, this course went through the math behind the functions and then went on to build them with python from scratch. The exercises are also well prepared with clear notes and test functions to make sure the codes work as intended. Highly recommended!

创建者 Yu S

Feb 11, 2018

I hope instructor could fix the notation in back prop. I think this should be easy, because he just need to stick a red color comments beside in the video.

One big misleading is by back prop:

Because the notation for back propagation algorithm presented in the lectures treats dA and dZ differently from dW and db(I ignored layer l index in my notation). Namely, 'dA' and 'dZ' are always computing the derivatives

dL/dA and dL/dZ

respectively, but 'dW' and 'db' are computing the derivatives

dJ/dW and dJ/db.

创建者 onkar p

Nov 28, 2017

Again an awesome course ,hats off to NG for this brilliant series of courses.

One thing which i liked so much was the interview session with Ian ,Peter etc.Came to know about further research and development going in Field of ML & DL.

i liked the way Ng has put up the lucid explanation of vectorized implementation and how to do random initialization.

And the ending was super with DNN for image classification.

Its a good experienced learning so far with Prof Andrew.

Thanks & looking forward to next course .

创建者 krishna

Aug 18, 2017

I took Andrew's Machine Learning course but was never able to complete the course. This time I have completed this course and hope to complete the remaining 4 as well.

Andrew has been very successful in developing the intuition for the neural networks and once it becomes intuitive it's all imagination.

I loved all the interviews with "Heroes of deep learning". To be honest, I never knew about any one of them prior to those interviews. It is great to know the best people in the industry.

Thank You Everyone

创建者 Omar Z

Jul 30, 2019

a basic course, given the depth of mathematics it discusses. One good thing in the course is the frequency of the practical assignments, however, I feel the course needs one small project where each student writes the whole program on his own to get used to the whole process, rather than just implementing the functions. One thing I believe needs to be added, is to offer hints as an Optional thing, so that some people feel challenged (as well as grasping the idea in a deeper way hehe) during the course.

创建者 Ajinkya C

Jul 19, 2020

Awesome Course for Diving into the World of Deep Learning and AI. ANDREW NG Sir Explains the Concepts of Neural Networks in such an Excellent Way so that they are Understood Easily and also in Depth. Also, the Programming Assignments are Well Designed so that you can Understand the Concepts Deeply and Practically Apply them in Python. A little notion of Machine Learning is required to make more sense but you will still understand the concepts.

Huge Thanks to Him for Creating such a Great Platform!!!

创建者 Anil R

May 12, 2020

The whole course had an excellent pace and covered all the vital topics in great detail. Being an engineer myself it was easy to grasp the principles of forward and backward propagation, the chain rule of differentiation. Using python program was also a great plus. Though I have some programming experience I had never sued python before. Lastly I would like to Professor Andrew NG. His sounds so cool and peaceful, and puts the students in relaxed mode, ,thus improving the learning experience manyfolds

创建者 Rúben G

Oct 1, 2019

I am software engineer looking to expand my skill set to cover Deep Learning. I first learned that Andrew NG was a big reference on AI when I read Life 3.0. Then I searched about him and found he has a DL course on coursera and so I didn't even hesitated. This is my first course in Coursera. I found the classes super smooth to follow as Andrew NG introduces the topics in a very easy to understand way. I am super excited to cover the next courses. Thank you so much for sharing your knowledge this way!

创建者 Rehan S

Aug 24, 2019

Beginner friendly course. This is Andrew's Ng first but very important course and that is prerequisite of next courses of same specialization. Assignments are well designed by instructor very helpful to understand the

theoretical material. Assignments designed according to real world problems like image classification. Well effort by instructor that makes easy all the difficult topics for us and thanks to coursera team that providing us such a great platform where we learn something new at any time.

创建者 Kiran W

Jul 30, 2019

Professor Andrew Ng's teaching style is simply amazing! I was able to absorb the material fairly quickly and reinforce my learning with very well structured exercises. I, now, have the confidence that Deep Learning is no rocket science. It is pure mathematics and art at play! If your algebra fundamentals are in place and you are creative, there is no better path to AI than Deep Learning. Believe me, when you start "getting" DL concepts, it quickly grows on you and you are addicted to its philosophy!

创建者 Melissa C

Jul 8, 2019

So happy I completed the first course in the series of Deep Learning. I got a great foundation for how neural networks work, with good instruction, good illustrations, and plenty of resources. The lab notebooks are particularly well-written, with thoughtful instruction and step-by-step application of what we learn each week. Outputs have "expected" outputs shown below, so you know if you're on the right track or not. Overall very happy with this course. It's a good bit of work, but so worth it.

创建者 Tanmay K

Feb 28, 2020

An excellent that covers the fundamental required for deep learning. Professor Andrew Ng gives an excellent intuition behind the inner workings of deep learning and practical guides for implementation with the help of the assignments. I found the heroes of machine learning section to be the icing on the cake as it gave a broad overview of the latest developments in the field of deep learning. To anyone who wants to get an insight into this wonderful domain, I would definitely recommend this course.

创建者 Robert G

Jun 11, 2019

Terrific intro to neural networks! The instruction was very clear on the steps that made up NN/DL algorithms and very easy to follow. I really liked how the programming examples were explicit in what made up the algorithms, and then there were test cases for each section of the code. This made it easy to step-debug through the code, rather than waiting until the code is complete and running into a bug and having to try and trace back through the entire notebook. Thanks for putting this together.

创建者 Glenn B

May 31, 2018

Great topic, well organized, and very understandable. Tests and assignments are structured very well and are completely doable.

I get the dynamic aspect of writing the lecture notes in the videos, however the lecture notes should be "cleaned up" in the downloadable files (i.e., typos corrected and typed up). Additionally, the notes written in the video could be written and organized more clearly (e.g., uniform directional flow across the page/screen rather than randomly fit wherever on the page.

创建者 weonseok c

Mar 15, 2020

Although there are many pre-written codes, I think this course gave a good and easy image how neural net is confirmed and works to a beginner.

Some more things I also wanted are explanations or texts for how to prepare datasets (image data, in this case), and some other usages, not just distinguishing images but sounds or texts and so on too.

But maybe image is most easy example for a person who really don't know well about math or program. I still want to get next courses for further study.