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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
58,287 个评分
11,070 个审阅

课程概述

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....

热门审阅

BC

Dec 04, 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.

XL

Aug 27, 2017

This is a very good course for people who want to get started with neural networks. Andrew did a great job explaining the math behind the scenes. Assignments are well-designed too. Highly recommended.

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1 - 神经网络与深度学习 的 25 个评论(共 10,859 个)

创建者 vatsal m

Sep 14, 2017

I enjoyed the lectures and a few practice quiz. But I don't think the structure of assignments presented here is the correct way to assess learning. The assignments or exercises should be interspersed between lectures and the problems should be more interactive (pushing the student to think). Moreover, the amount of pre-written code was immense and therefore didn't really make me think a lot on my own. This structure of assignment forces the student to focus on matching the expected output instead of really understanding the concept. I am pretty sure most students did not really grasp the concepts at an intellectual level but still passed with decent grades. This is exactly the problem with schools today and I hope that Coursera is working towards rectifying that.

How do we create a learning platform that forces the student to intellectually interact with the problems? Many students that come here have picked up bad habits from their previous learning careers. They bring those bad habits here and it's up to Coursera to somehow try and make them unlearn those habits. This course instead allowed the students to happily use their bad habits and finish it feeling accomplished.

创建者 Jonathan C

Aug 20, 2017

The course expands on the neural network portion of Andrew Ng's original Machine Learning course, but ported over to Python. Even though it is spread out over 4 weeks, it really doesn't cover any additional material. Instead, Ng repetitively goes over the math and coding with vectors in Python, while stressing how hard the calculus derivation would be. This might all be helpful to you if calculus was not your strong suit, but my guess is that if you have any kind of background in computer science or statistics, the math in this course would be almost elementary.

The assignments are done on Python Jupyter notebooks, which has the advantage of a standard environment, but disadvantage in that it hides some abstractions. Specifically, you lose the sense of what the actual code would look like in a Python IDE. Sure, you can download the notebooks as .py files. Much of the code is pre-written, and you only fill in a few lines of code in each assignment. It would take a lot of self-study on what's actually going on in setting up the programs to actually be able to self-write a neural network. Although Python is without question more popular in machine learning than Octave, it is more popular because of its library support, and in a course that requires you to build your own neural network instead of using libraries (besides numpy), that doesn't matter. I preferred doing the assignments in Octave rather than the notebooks.

Since it is impossible to purchase this course on its own, perhaps the bigger question is whether the specialization is worth it. Courses 4 and 5 are not up at the time of this review, but Course 3 is only 2 weeks with 2 quizzes and no programming assignments, and Course 2 is about hyperparameter tuning, arguably the most novel in the 3 courses, but still not something that deserves its own specialization or even its own course.

My suggestion is to watch all the lectures for free. And then use your free week to do the programming assignments, which you can probably finish in a day, across all the courses.

创建者 Nicolás A G

Dec 05, 2018

I'm very dissapointed, all what taught here is also on the Andrew Ng's Machine Learning course. The sole difference is that here python is used and that the exercises are extremely easy, you almost have not to think. And even they give an approx of lines of code you have to write which are no more than 4 and if that threshold is surpassed is because you have to copy & paste same thing with different variables names.

创建者 Martin P

Aug 11, 2018

too easy to pass (the code needed for the assignments is even presented during the lecture)

the lectures itself are like "deep learning for dummies", everything is repeated multiple times

创建者 Mageswaran D

Nov 09, 2017

I felt the assignments are more of a fill in the blanks, than using brain. There was not much of a challenge considering my Scala certification

创建者 oli c

Dec 02, 2018

Lectures a good. The programming assignments are too simple, with most of the code already written for you, so you only have to add in very similar one-line numpy calculations, or calls of previous helper functions. I would learn more if the programming part was harder.

创建者 Mohammad S B H

Apr 28, 2019

This is a good course with good explanation but the only problem with this course is that it covers so much information all at once during the entire week and then there is just literally one or two programming assignment at the end. There should be exercise questions after every video to apply those skills taught in theory into programming. I now know general concept of deep learning but I still barely have a clue on how to code those concepts. If I wanted to code all that myself I still wouldn't even know where to start, where to get the data etc etc because the programming assignments were just, now write this, now write that. Also there should be a help button where mentors should be available because we have tons of questions after learning a new concept. We cant just type all questions in the discussions forum and then then wait till someone replies and then that question gets lost among the pile of other questions. Especially in programming assignments when we get stuck and then dont have a clue what to do now. For $50 a month, the teaching structure is really poor. Even khan academy has a much better educational structure. and its all free too. I am a college student with a part time job and I am contributing 70% of my earnings towards this course because my future depends on it.

创建者 Alan S

Oct 28, 2017

This course was a hot mess. Andrew Ng seemed to lose his train of thought in some of the lectures, and he would repeat himself and just say nonsense sometimes. There were a bunch of errors in the quizzes and the assignments were confusing at times. On the whole, this was not up the the standard of Andrew Ng's old ML class. I did continue with this series of courses anyway, and I noticed a marked improvement in the quality of the second course, so its possible that they cleaned up the first one in the time since I took it.

创建者 Nikolay B

Oct 26, 2017

Course targets very slow learners. Professor repeats same stuff again and again and again, basically for 4 weeks we learn how to calculate the same things (front-back propagations and cost function). Programmings assignments are incredibly easy, all solutions are made by authors, you just write in code what they described in notes. 1-2 lines here and there.

创建者 Deven P

May 14, 2019

This is really a very good introductory course for people from various background. The assignments are also nicely designed to give an insight to how things works.

But at times, in order to make this course appealing to non-math/engineering background, it at times trivializes some important mathematical concepts and notions, in order to not scare away people who are not very comfortable to mathematics.

创建者 Johan W

Oct 10, 2017

Too slow, a lot of repeating facts, very little contents in total in the course, and nothing new compared to the old machine learning course which was more fun and much faster. Nice environment with python notebooks though!

创建者 John A

Apr 15, 2019

What a great course. I was expecting this to be more of an introduction to using Tensorflow and high level introduction to neural networks. Instead it is an incredibly well explained introduction to how to build your own neural network (in python) and implement it on some sample data. This really gives you a good grounding in what a neural network is doing (at least implementation wise) and a good foundation to build on. I am sure later courses in the specialization cover use of Tensorflow (maybe keras?) but I can see how this course enables you to understand what is going on under the hood of all these toolsets. He has a great ability to explain what could be very complicated ideas simply and layout what could be convoluted coding sequences in a very well organised and concise manner. I will recommenced this course to anyone starting out with either the intention to go into data science (using algorithms) or machine learning (building your own algorithms).

创建者 Md. N H

Jun 30, 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

创建者 nikcojeanian

Dec 02, 2017

Programming assignment is too simple

创建者 Niloufar Y

Jan 12, 2018

not satisfied

创建者 Parth S

Aug 10, 2018

Coding Exercise Were quite simple, a full length assignment would have been better.

创建者 Antoine C

Jun 04, 2018

If you are already used to Python/numpy and you followed the free Machine Learning course from Ng, you really won't learn anything, apart from a new activation function.

创建者 Mohammad G H

Oct 01, 2018

Very basic level

创建者 Ashkan A e A

Nov 13, 2018

Too easy

创建者 Jonathan C

Mar 24, 2019

The lectures and assignments are extremely shallow, unengaging and poorly edited and recorded. Andrew Ng is riding the waves of the popularity of his ML course. I regret every dollar and minute I wasted on this crap. DON'T ENROLL DO YOURSELF A FAVOR GO READ A BOOK!

创建者 Nikhil D K

May 12, 2019

This is a good review of the concepts. It helped even more once I finished the course and reflected on the material by working out the equations for back propagation by my own hand. Looking forward to the next course in the series.

创建者 Leon V

Apr 07, 2019

A bit easy (python wise) but maybe that's just a reflection of personal experience / practice. The contest is easy to digest (week to week) and the intuitions are well thought of in their explanation.

创建者 Giovanni D C

May 31, 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!

创建者 Stanislav T

Jul 07, 2018

I think the course explains the underlying concepts well and even if you are already familiar with deep neural networks it's a great complementary course for any pieces you may have missed previously.

创建者 Mohammad Z

Sep 13, 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.