Chevron Left
Back to Neural Networks and Deep Learning

Learner Reviews & Feedback for Neural Networks and Deep Learning by DeepLearning.AI

4.9
stars
104,433 ratings
20,867 reviews

About the Course

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....

Top reviews

XL
Aug 26, 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.

RG
Sep 6, 2020

I have learned a lot from this detailed and well-structured course. Programing assignments were very sophisticatedly designed. It was challenging, fun, and most importantly it delivered what is aimed.

Filter by:

376 - 400 of 10,000 Reviews for Neural Networks and Deep Learning

By 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!

By 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.

By 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.

By 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.

By 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.

By Zhengyang L

Jul 12, 2020

This course has provided me the most suitable level of math details. Many other books and tutorials tend to overlook the fact that learners are usually not experts in machine learning and statistics. An example is the explanation of why the cost function of logistic regression should be the form provided. When I first saw the formula in a book, I was confused and could not rationalize it myself, which troubled me a lot because I don't think I can implement an algorithm without knowing the cost function. This course is really good for beginners who cares "why".

By dsp

Aug 23, 2018

Well motivated. Clearly structured. Generalizing from Logistic Regression over shallow Neural Network to Deep Neural Networks was easy to follow and reinforced the structure of the approach. I overall liked the presentation of the maths and assume that it is well suited for an audience of differing affinity to maths. For myself, I will have to do the calculations again on my own to get a real grip on them. [Writing db (=something that should grow with steeper b) for dL/db (=which shrinks with steeper b, given the same change in L) still feels wrong.] Thanks!

By Raimond L

Aug 19, 2017

Nice basic course, gives a clear look at what is happening inside neural networks, all details are explained in quite clear and understandable form with practical tasks of implementing everything, so that you really know what is going on.

After that course you will have a knowledge of how to implement a simple neural network and it's learning algorithm from zero. Also you will get some knowledge about matrices operations, derivatives and python programming.

I do highly recommend this course for novices and for more skilled people. It was a positive experience.

By Nicholas M W

Jan 2, 2018

Excellent presentation of the material. The homework assignments made this approachable by holding my hand as I learned "how to walk" with matrices and multilayer neural networks. I feel like there could have been one more "do everything yourself" assignment, where we had to build another L-layer neural network completely from scratch, but maybe that isn't the point of this course, since I expect I'll be using keras or something in "the real world". An optional quiz involving some of the derivations for some equations might have been a nice stretch, as well.

By Augden S

Oct 11, 2018

A solid introduction into discussing the basics of machine learning. Although I had to research some details on specifics topics which I could not completely understand in the course, that was my own problem, really. The basic steps for creating a neural network and understanding the functions behind initializing parameters, forward propagation, cost and backward propagation are explained well, and since the assignments are in python, I've learned a few packages and helpful coding hacks to better implement efficiency in programs. Overall, I would recommend!

By Akshay B S

Sep 6, 2017

It is a great course to get started on Neural Networks and their practical implementation. The whole course is constructed keeping the end result of building an OPTIMIZED program in python for building a neural network and everything connects together in the final programming assignment. Not only do you learn what are neural networks and how they work but you also learn very importantly how to code in a very optimized manner so that you decrease the training time as much as possible. Definitely a great course, looking forward to complete the specialization.

By Ning D

Jan 26, 2021

I understand so much more about deep learning. Learning about some basic Mathematics of calculus and derivatives really helps in understanding. Professor Andrew also repeated lots of things several time which is good in order to check whether I got that particular part correctly or not. Somehow having no python basic at all will definitely be difficult in order to finish the assignment since the video lesson did not give lots of example on this but eventually after following along and spending quite lots of time on the Lab do help understand how it works.

By Hendra B

Jan 28, 2020

This course is the best course to start learning deep learning. You will enjoy the step-by-step creation of shallow and deep neural networks.

Frankly speaking, I am amazed at the creativity and brilliance of the Andrew Ng's team for preparing the programming assignments and quizzes. Therefore, I am speechless.

Last but not least, I am also really grateful and thankful for Coursera who has given me Financial Aid for this course. In return for this act of kindness, I am able to finish it before any deadlines.

Thank you deeplearning.ai!

Thank you Coursera!

Thank

By Mattias K

Nov 4, 2017

Great intro to deep learning. Although it's a bit repetitive at times, especially coding bits - one is not really forced to understand the components at times but can instead just follow instructions and copy paste bits and pieces. Would for example have appreciated that more time was spent on explaining the details of derivation of backwards propagation especially within "deep domain". The intuition is clear, but either forcing the user to do (or giving a link to) a step by step derivation would have been useful and saved time. Thanks for a great course.

By Pantelis D

Dec 19, 2020

An excellent followup to the ML course of the same professor (Andrew Ng), similar short, on point and clear videos that serve as an introduction to Deep Neural Networks.

In this course the programming assignments are coded and submitted in the browser using Jupyter notebooks, the coding language used is python and for the math the python library "numpy".

It is worth mentioning that some interviews with influential people on the field of DL are included and make the student fall in love with DL even more. Excited to see what's next in this specialization.

By Ged R

Sep 7, 2017

I completed the original ML course earlier this year which gave the fundamentals of the practice. What I got out of this course was a reinforcement of the practices and ways of collecting my thoughts. There was enough difference in the approach and especially in the back prop areas to help clarify the understanding from what was a bit of magic, to a clear and more structured set of calculations. The platform of using the notebook is very solid, and of course there is the usual outstanding support from the community with respect to answering questions

By Christos M

Aug 12, 2019

Andrew NG's approach is one of its kind. Previously, I had taken several courses with other reputable online providers, and also did a lot of reading in tandem. What amazed me about Andrew's approach was the fact that crucial concepts were explained in much detail, one-by-one; this helped me complete the overall puzzle and/or fill in any missing links. I'm not sure if I could've followed the course without any previous experience, but if you're familiar with Python, NumPy and basic ML concepts, then this course will help you understand DL a lot better.

By Basil A

Aug 22, 2017

I think this course is very accessible, and gives you enough know how to hit the ground running. My only caution is that there is a bit of hand-holding involved (because of the limited background they assume), and that if you want a more rigorous foundation, you'll have to supplement this course with other materials. This doesn't detract from the quality of the course though, rather, it's amazing how much you can do with Deep Learning without fully understanding all of the finer details, and this is a good place to springboard into more advanced study.

By Eduard L

Oct 31, 2018

After a full course of Machine Learning, of course, this one is rather weak. The feeling that all 4 weeks we are talking about the same thing. This is probably done for those who are not at all in the subject. I see this course as an introduction to the specialization. I hope the continuation will be stronger. It's great that practical work is done in Jupiter on Python. Program exercises are easy, but it takes a lot of time to figure them out if we don't know Python very well. This is not a plus or a minus, just a statement of fact. Thank you Andrew!

By Bernard O

Oct 21, 2018

This was an amazing course for me. I've always wanted to get to the bottom of deep learning fundamentals and this course did not disappoint. It walks me through the basics to the more deeper concepts in incremental steps without overwhelming me with too much derivatives (but just enough to carry the point across). Just the right mix of theory and practice. Highly recommended as a starting point for deep learning, or if you're like me, developing more intuition towards the practice that I am already doing. Fills in the gaps in my understanding nicely.

By Steven K

Jul 8, 2018

A very nice introduction to deep learning. Covers the basics and builds up slowly. There is some prerequisite knowledge of Python programming and calculus to have success with the course. Professor Ng's explanation of the topic is focused on practical applications, and builds on years of experience gained in academia and industry. The exercises are focused on mastering core concepts. The notation takes a little bit of time to get accustomed to, but you begin to understand why the notation is the way it is. Very good course; I definitely recommend it.

By Romina

Jan 8, 2018

A really good intuition and introduction to neural network and deep learning. What I enjoyed the most was the fact that we needed to implement the learning algorithm step by step through the guided programming assignments as opposed to calling an in built function in libraries ( such as tensorflow etc). I felt the programming exercises were quite very successful in an attempt to draw and maintain the learner focus on the algorithm itself as opposed to other programming aspects, which can be learnt elsewhere/improved elsewhere. Great course. Thank you

By Juan

Jun 27, 2020

I like the practical focus of this course, it allows you to build the fundamental parts of simple tools that are gratifying for us beginners.

The instructor focuses on making sure he teaches only the core concepts and sometimes he does only explain some concepts at a very surface level, but I see this as more of a feature than a bug. Linear algebra and calculus concepts that are only briefly discussed in this course, deserve their own class or course; I like that is up to the student to decide whether to deeply research these subjects on his/her own.

By Ankur G

Nov 12, 2017

This course makes you implement your own neural network without using Tensorflow or Torch. As a result, the student gets to learn what neural networks are implemented internally instead of only learning how to use a particular software package. The course is full of small, practical, and highly useful information such as why we use a cross-entropy loss instead of sum of squared errors loss and why do we need to initialize parameters using not-too-large random weights. This information is very useful in implementing NNs at work or for job interviews.

By Wei L

Aug 17, 2017

It's a very good course. It illustrates the idea of neural network and deep learning in an intuitive way. I think this time I fully understand the idea and details behind them. Also, the python programming is very friendly. I have used R for years but not so familiar with python. However, folloing the instructions I can do the coding very efficiently. I think i just spent less than 1 week on this course but get 100% score on it. So it's not so challening compared to Machine Learning and PGM. I think PGM is the most difficult one among these courses.