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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
102,992 个评分
20,605 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

MH
Jun 29, 2018

Very good course to start Deep learning. But you need to have the basic idea first. I would suggest to do the Stanford Andrew Ng Machine Learning course first and then take this specialization courses

BC
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.

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51 - 神经网络与深度学习 的 75 个评论(共 10,000 个)

创建者 ABIR E

Feb 18, 2021

The course is more than excellent, you will implement all the Artificial Neural Networks Algorithms (step by step), and you will learn all the maths behind these algorithms. The Assignments (especially those of the last module are challenging!). I already obtained the Professional Certificate "TensorFlow Developer": now I understand the behind the scenes of many packages of TensorFlow... really: the course is terrific!

创建者 Tim B

Jul 15, 2020

The course does not have the same quality as the “Machine Learning” course Andrew Ng made with Stanford.


The biggest issue are the programming exercises, that do not require the learner to think at all. Most tasks in them are on the level of “copy and paste this piece of code”, “retrieve a value from a python dictionary” or “use a mathematical formula displayed directly above”. I appreciate the effort to make the course more inclusive to people with a weaker background in Computer Science. It would however make the course much more worthwhile to have challenging exercises with optional hints, instead of giving the solution away in each task description.

“Neural Networks and Deep Learning” hardly teaches anything, that wasn’t already covered my “Machine learning”. The major differences is that it uses Python instead of Octave and arranges features as rows instead of columns. In my eyes, the learners time is better spent, skipping the first course of the Deep Learning specialization entirely and taking the Machine Learning Course instead. To the creators / maintainers of the course I would advise creating a summary, that covers the most fundamental differences between the two courses (different notation, numpy fundamentals) and make a suggestion where someone who has taken Machine Learning should join the Deep Learning specialization.

While the audio quality has improved, the video editing is poor. There are multiple occasions where misspoken content, that was clearly meant to be edited out, remained part of the video. Many videos are preceded by a “Clarification” reading task that corrects some mistake in the video. How hard is it to get an intern to fix this in post?

创建者 Miriam G

May 18, 2018

Really just mathematical background knowledge. Nothing you would ever need, since there is keras. No own thinking during assignments neccessary, either.

创建者 Aratz S

Feb 27, 2018

Easy course if you have coursed the ML course before. I would like to see more explanations in detail. Still some bugs in the assignments... why???

创建者 Thomas M

Jul 16, 2018

Course starts with a lot of math without any context what all those computations and parameters are used for or what they have to do with N

创建者 Loren Y

Feb 5, 2019

The assignments are not good. Too easy and too much handholding. Also lots of technical issues.

创建者 Richard R

Nov 18, 2019

Meh. I don't know why we are spending so much time in Week 2 talking about the math and how to not use FOR loops in week two when he STILL hasn't given any kind of overview about why we do this math, how we're going to use it to identify cats in pictures. Instead, we're just yakking on about math math math math math with NO context whatsoever. If I wanted a math class, I would have taken a deep-in-the-weeds math class. I expected a higher level of instruction for this higher level of abstraction but instead it seems that he just wants to talk about math and how to use vectors in NumPy. Zzzzzzzz.

创建者 Andrew H

Apr 28, 2019

Not enough explanation or support to complete the very vaguely worded assignments in anything like the specified timescales.

I respect the source of this course but as a teaching resource it is really very poor.

创建者 Ali A

Aug 28, 2017

Terrible integration with Jupyter Python framework, end up losing 3 hours of work! Nobody responds from the courser team !

创建者 Kenneth T

Jun 5, 2019

Great course, definitely taught me the basics of Neural Networks and Deep Learning as it's supposed to. Assignments are quite engaging when you try to thoroughly solve them. Even with minimal mathematics, the course will handhold you the whole way. Definitely a great course for anyone with minimal programming to get into. For me, the most challenging part was understanding how Python syntax worked with numpy. If you are taking this course I recommend taking your time with implementing the projects, they can definitely give you an understanding behind the logic of neural networks by following the code. The instructor is quite nice and warm, sometimes a bit dry, but nonetheless, he seems very warm; wanting to teach the next generation of individuals to do ML/AI. The course does have a few downsides such as how buggy the iPython notebook can be. This is the programming environment you will be using. An the video quality isn't always the best with the audio, but overall the content was presented in a great way and prepared in a manner in which you learn one step at a time.

创建者 Sandip G

Mar 21, 2020

The content was very good and intellectually curated, and no complaints about a teacher of such high quality "Andrew Ng". Actually, I took the "Machine Learning" Course long before on Coursera from the same instructor, as I took this course now, which highly helped me to finish this in less than a week, although I never got time to complete the former course. Advice to any new students on this course would be to have a basic understanding of Machine Learning, which includes linear regression, vectorization et.al. , (or simply, "ML" course on Coursera).

One small amendment on this course could be to reshuffle the contents a little, from different weeks as I found the content which was in Week 4, to have high importance to be taught earlier in this course (for eg, getting matrix dimension right ), and there were others sub-topics in week 3 as well. I don't remember all of them, as I took 4 weeks worth of information, in just a single week :)

Very excellently taught, and contents, as well as assignments, were of topmost quality.

创建者 Kenny C

Aug 1, 2020

One might dislike that the derivation of formulas is not talked about in this course, but I think it's the right decision for this course. I took the Coursera Math for Machine Learning Specialization before taking this course, and the derivation for the formulas took at least 4 weeks of background material about linear algebra and multivariable calculus. Thus, this course aims to give you a conceptual understanding of neural networks that will allow you to implement it on your own. While some might argue that the programming assignments are too easy, or that too many hints are given, I think they're necessary for guiding you in the correct direction during the assignments. If you take the time to read the prewritten code, you will be able to get the understanding you get from writing it fully from scratch and possibly taking hours to debug and to read NumPy documentation. Overall, a very solid course for those who want to build a neural network on their own.

创建者 Michael C

Sep 23, 2017

Excellent course. Surpasses Andrew Ng's original Machine Learning course in conceptual depth and ease of implementation. The lecture videos, quizzes, and programming assignments are all targeted towards someone who knows nothing about deep learning or machine learning, yet manages to elaborate on surprisingly advanced topics which you would not expect to make an appearance in an introductory course. It strikes a superb balance between simplicity and depth that is rare even in in-person university courses, and much rarer still in MOOCs. I will be taking all the rest of the courses in the Deep Learning Specialization. Well done.

创建者 Hong X

Oct 2, 2019

I've learned to build the basic binary classification model from conventional logistic regression to a shallow model (with one hidden layer) up to any layers of ANN. One of the most rewarding point for me is that I start using python (other than Matlab with which I have stuck for years until recently most cutting-edge open-source codes are found delivered in Python!). Although there is still a long way to go , I found well warmed up by those delicately designed step-by-step programming exercises in Jupyter notebook. Therefore, I do appreciate the course materials contributed by the lecturer as well as the exercises-designers!

创建者 Chi W C

Sep 13, 2017

Wonderful class. I started out not knowing anything about neural network or deep learning. I was able to follow the class lectures to get a sense of what was going on. The assignments were clearly structured and well organized, and serves as excellent examples in how to build this type of applications (by small building blocks and test each of the block carefully).

At the end, I was able to build my first neural network implementation in recognizing a cat!!

(However, I have uploaded 3 non-cat images, but NN failed by predicting these were cats. On the contrary, logical regression correctly predict the 3 images as non-cat).

创建者 Carl G

May 6, 2018

Andrew Ng is a thorough teacher and shows how online platform can be as engaging as taking a live class. His pace and style of writing slides is perfect for keeping pace taking notes by hand (my preferred way for efficient learning). He takes time to explain in depth how NN's work, and even more important his experience how to use them. Homework is a bit simple, but also appreciate to not be mired in coding details. Nice to be able to focus on how NN's works. Best part is that each piece of code can be fully tested against known output before used further. Illustrates nicely good practice once doing real coding project.

创建者 LIM W X

Jan 13, 2018

Through the Neural Networks and Deep Learning course, I have learned the fundamentals of neural networks and deep learning. The lectures are simple and easy to understand. The assessments have designed to test students in the fundamental knowledge of neural networks and deep learning. The assignments are designed to guide students on how to design and implement a shallow and deep neural networks, by applying what have been taught in the lecture. In conclusion, I enjoyed this course and I will definitely continue the deep learning specialisation courses to achieve my career goals. Thank you Prof. Andrew Ng and Coursera.

创建者 Michael B

Sep 18, 2017

Andrew, like no other instructor, manages to convey difficult material in a clear and concise manner. Even after many years experience in machine learning/deep learning, this course lead to many "aha" moments where many things I learned about the topic came together! The only criticism that I have for this excellent course is that I wish it would contain some, maybe optional, videos that go deeper into the math of for example backprop. I think this is a difficult concept to grasp and I imagine that if Andrew would sketch the proof with is clear and concise style, a lot more people had a much better understanding of it.

创建者 William L K

Sep 6, 2017

Excellent course. Lectures are clear and concise. Professor Ng makes it seem so understandable despite the complexity of actual programming implementation! Assignments are both relatively straightforward (overall concepts) and tricky (keeping track of the matrix manipulations in Python). I don't know how many times I started a programming assignment, hit a wall in terms of programming errors, and came back to it after a time and getting through that error. Persistence, at least for me, was definitely a major component. Well worth the time put in. Looking forward to taking the next class in the sequence.

创建者 Laith M A R

Aug 17, 2018

I am so proud and confident of the things i learned. i never expected to learn this much from an online machine learning course, so many concepts that were vague to me in the past are now Crystal clear, and prof. Andrew does an outstanding explanation for each concept, not to mention that the programming assignments are extremely beneficial and cover every concept explained throughout the videos in a really cool, professional way. This has been only the first course in the deep learning specialization i am currently pursuing, and it made me so much more excited for the upcoming courses! thank you coursera :)

创建者 William M

Sep 4, 2017

I really enjoyed taking this course. I have taken one of Andrew's courses before, and they keep getting better. I have a background in development, and appreciated the use of python over octave. Andrew consistently strives to provide an intuitive feel for the topics he is presenting. The fact that he is able to provide a complex subject in a simple manner speaks to his mastery of the subject.

The course contained a great mix of theory and practical application of those theories. I'm looking forward to the next course.

创建者 John G

Mar 28, 2020

What an amazing course. To be fair, I had completed Dr. Ng's course "Machine Learning" before taking this particular course, so some of the concepts, I was already familiar with. This course, delved deeper into the mathematics of Neural Networks and followed it up with coding assignments in Python. This course has provided a strong foundation for me to continue to build my knowledge base. To anyone interested in Deep Learning, take this course!!!

创建者 Malte B

Apr 8, 2019

Great course to get a practical understanding of (Deep) Neural Networks. I would recommend to take Andrew Ngs "Machine Learning" course (also available on Coursera) beforehand, because the latter is much more rigorous when it comes to matrices operations. Thus it is unfortunately possible to just fill in the provided code in this course but don't really understand what it does.

创建者 Muhammad T

May 10, 2020

This field of deep learning has always intrigued me and I wanted to study it. My university offered a course, but sadly I couldn't enroll in it. However, thankfully, I got access to this masterpiece and now I can say that by completing course 1, I am pretty confident about neural networks and how to construct one.

Great Couse, and Great Instructor. Would Definitely RECOMMEND!!!

创建者 WALEED E

Dec 16, 2018

This course formed a concrete background in building multi-layers neural network from scratch. The best advantage of this course is I was able to immediately apply the knowledge I gained into real world problem like humanoid navigation towards known targets. Illustration is great in terms of mathematical explanation and coding in a step by step walk through.