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....

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.

AT

Jun 30, 2020

I think that this course went a little bit too much into needy greedy details of the math behind deep neural networks, but overall I think that it is a great place to start a journey in deep learning!

筛选依据：

创建者 David R

•Oct 01, 2019

(09/2019)

Overall the courses in the specialization are great and provide great introduction to these topics, as well as practical experience. Many topics are explained clearly, with valuable field practitioners insight, and you are given quizzes and code-exercises that help deepen the understanding of how to implement the concepts in the videos. I would recommend to take them after the initial Andrew Ng ML course by Stanford, unless you have prior background in this topic.

There are a few shortbacks:

1 - the video editing is poor and sloppy. Its not too bad, but it’s sometimes can be a bit annoying.

2 - most of the exercises are too easy, and are almost copy-paste. I need to go over them and create variations of them in-order to strengthen my practical skills. Some exercises are quite challenging though (especially in course 4 and 5), and I need to go over them just to really nail them down, as things scale up quickly. Course 3 has no exercises as its more theoretical. Some exercises have bugs - so make sure to look at the discussion board for tips (the final exercise has a huge bug that was super annoying).

3 - there are no summary readings - you have to (re)watch the videos in order to check something, which is annoying. This is partially solved because the exercises themselves usually hold a lot of (textual) summary, with equations.

4 - the 3rd course was a bit less interesting in my opinion, but I did learn some stuff from it. So in the end it’s worth it.

5 - Slide graphics and Andrew handwriting could be improved.

6 - the online Coursera Jupyter notebook environment was a bit slow, and sometimes get stuck.

Again overall - highly recommended

创建者 Halil D

•Aug 24, 2020

Learning from reliable resources is crucial. Andrew Ng is ranked #3 in the field of Deep Learning, in terms of the number of citations, on Google Scholar. Therefore, being able to learn from a person like him is an extremely valuable chance. I learned a lot, but would like to tell the things that should be improved:

• There are lots of redundant repetition. It kills the flow and creates a serious mess

• Assignments are only focused on finding a few missing lines in the cells. Therefore, it cannot evaluate whether you "understand the big picture" and "can build a model on your own" or not

• Sometimes terms/concepts are not clearly explained OR not explained at the right time. Example: A new term "activations" comes up in a video, and you wonder what is that. However, you learn what actually it is, maybe in the next video by your own inference

Advice for learners: Before starting to a programming assignment, download the whole folder of this programming assignment (you cannot download a folder, but you can download it file by file and create the same folder with its original structure) and work on your computer. By this way, you can prevent the "kernel disconnection" risk of the online version, and also replace the notes within the "Markdown" cells with your own summary. When you complete the programming assignment, you will just need to copy the codes within the "Code" cells to the online version, and then submit

创建者 Nowroz I

•May 18, 2020

I loved this course as it explains the intuition behind the methods used in deep learning. As I have no problem with Calculus and Linear Algebra, I was able to calculate the derivatives by myself. People who are not accustomed to working with NumPy may find the assignments overwhelming. Hence, my suggestion will be to learn the NumPy (only the basics will do) before starting this course.

创建者 Samaelí

•Apr 03, 2020

I give four stars because the course is great and the programming assignments too. But I think sometimes the programming assignments were a little condescending and easy. Don't get mi wrong, there were moments that I din't know what to do, but there were also a lot of times that all the procedure was explained.

创建者 fahad

•Aug 25, 2019

This course was really clear my concepts of Deep Learning and how actually neural network works.

创建者 Shravan V

•Feb 02, 2020

The course exercises were very well thought out and well designed. The instructions were not crystal clear, which led me to errors in the notebook. In week 4's last assignment, it wasn't made clear that the function definitions I had written in the preceding assignment should not be cut and pasted into the notebook, but that the grading system would use its own function definitions; this led to my submission leading to grading errors. Took many hours to figure out what was wrong, through the help of one very helpful person (Paul Mielke) on the forum.

Andrew Ng's handwriting is TERRIBLE. He should either practice writing more clearly, or use slides.

I would have appreciated having written down lecture notes; having to take notes on the fly was hard as I was sometimes watching the lectures on the train or during dialysis (one arm is disabled).

Is it really necessary to use up so much of the screen when showing the videos with the logo of deeplearning.ai?

Just a comment on one important shortcoming of online instruction: As a professor who teaches statistics, it is interesting to see the loss in learning that the student experiences through the absences of individualized feedback. One learns way more when one can talk to the teacher(s), and I guess this high volume throughput style of teaching limits what can be taught online.

创建者 Omar A

•Jul 22, 2019

If you have taken this course after ML by Andrew, you will see exactly the same material covered in 1 week expanded in 4 Weeks except using Python instead of octave or Matlab.

If you have calculus background I expect you to get tedious from elementary approaches in the lectures to get rid of Math and calculus.

Programming exercises in this course are very easy and below the level of first excellent experience with ML course.

There is no easy way to get lectures slides, No reading sections in this course. Like this course made to make systematic approaches to get things done without actual care about understanding the theories and concepts.

The good news comes when you have no previous knowledge about NN and elementary python skills, then this course is an excellent way for you to start.

创建者 Alessandro

•Sep 09, 2017

The content is great and I learned a lot. Certainly there could be a lot more feedback by the instructor in the forum. My feeling is that the students are really left on their own. Good from one point of view (cause you really have no choice than crush your head on the problem for days until you understand or give up), bad from another (it takes a lot longer to clarify difficult points). Fortunately the forum is populated by very clever students that take the time to answer questions. As a beginner I learned the broad strokes and intuitions for NN in this course, but the details about certain formulas are still very obscure and I was hoping for a better explanation of those.

创建者 veit s

•Apr 27, 2020

Programming assignments are too easy, mostly copy and paste.

创建者 Anne R

•Sep 09, 2019

The programming assignments provided a good framework in order to practice coding the main functions in a neural network. This was helpful to understand the matrix operations underlying the forward and backward processing in a general L layer network. Without a previous background in linear algebra and in neural networks however this course would be challenging and maybe very frustrating due to the limited debug information available.

The course videos need to be a lot more focused on the details being conveyed. The verbal and visual discussion and explanation provided is in my opinion not effective. The slides are cluttered and contain many errors, the verbal portion is like a casual conversation that repeats quite a bit, and the script provided for those that get tired of the repetition contains many transcription errors. I would recommend that someone be paid to correct the scripts to help those that prefer this way of working through the course material.

创建者 Tracy B

•Sep 29, 2019

The notation used in the course was horrible and correct math notation should be used even if the course is not intended for math students.

I also feel this course should not be labeled as intermediate skill level. This was a very beginner level course. I have a PhD in applied math and was simply looking for knowledge in deep learning since my doctoral work was in a different field. It was very clear that I am WAY behind the target audience of this course. That's not necessarily a negative reflection on the course, but I still didn't find it very useful and feel like it should be labeled as a beginner level course.

创建者 Jérôme B

•Nov 16, 2017

To me, this is a failed attempt at simplifying those concepts. After spending hours trying to figure it out, now I find the algorithm behind the Neural Network very simple, and I can easily explain it to someone. But in this course I had to figure out by myself what was the point of those hundreds of lines of maths. So, very interesting concepts, but the "transmitting style" wasn't for me.

创建者 Ofer B

•May 01, 2018

Very abstract, and the examples are not as concrete as they could be. I'd use better visuals to ensure that the concepts in each video are understood 100% visually.

创建者 Muhammad A

•Aug 20, 2018

Great attempt but it failed to provide complete details. Specifically the project files and their loading mechanism

创建者 Francis J

•Dec 29, 2017

too easy, suitable as an entry level class

创建者 Tobias G

•Feb 21, 2018

Few Detail. Mathematics missing.

创建者 David B

•Feb 17, 2020

This course is really quite bad. I'm not sure why the rating is so high. Probably because they are only prompting people who completed the course to rate it.

The main problem with the course is that It spends the majority of its time describing a byzantine set of notation while avoiding actually helping you understand how to apply the concepts you're learning. So you learn that a^[l](i) is the activation vector for layer "l" and example "i" but then you get to the python portion and, big surprise, none of that information is even slightly useful.

Even worse, the course hasn't chosen its audience. If you're good at math you'll be annoyed about the math explanations. If you're good at programming you'll be annoyed by the programming explanations. Rather than isolate that material in a way that lets people skip parts which they already understand, you get a really basic explanation of everything all globbed together.

Anyway, I'll still try to hack through this thing to finish it, I'm just letting you know that if you're underwhelmed, you're not alone.

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 Domagoj K

•Aug 18, 2017

I am very disappointed with this new course concept where you have to pay 43$ a month to be able to solve a quiz. Coursera used to be famous for its free courses and now it just removes free features over the time. It has become another site with expensive courses. I watched first week lectures and this is probably my last time to enroll in Coursera course.

创建者 Bedrich P

•May 01, 2020

Course teaches bad programming practices, such as naming variables dZ and b. Also it is little outdated - neural networks are not written in numpy anymore.

创建者 Manish S

•Dec 31, 2019

This course is more of spoon feeding, I liked the introduction to neural network in "Introduction to Machine learning" course better.

创建者 Maxence A

•Oct 29, 2017

The programmation exercice are nice, but the courses are mainly about very basic linear algebra.

创建者 Zaheer

•Apr 10, 2019

This course is really good but assignment given to solve is not understandable.

创建者 Joseph K

•May 20, 2018

It will be a good course when you dump jupyter note books.