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.
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
par Vishnupriya V•
May 17, 2019
As a beginner who is interested in Deep Learning, this course was very useful an informative. The explanations given by Prof. Andrew were to the point and precise. However, detailed explanations behind the mathematics(calculus) could've been given as optional videos. The quizzes and the programming exercises were also very challenging at a beginner level. They quizzed us not only on the equations but also on the concepts. The only drawback in this course that I faced was trying to submit my programming assignment through Jupyter notebooks. They can very annoying at times. Also, the discussion forms were very active with the mentors and fellow student who would quickly help by replying to your questions.
par Steve S•
Dec 03, 2017
This course is a very thorough introductory review of neural networks that doesn't require expert level knowledge in some of the underlying math like calculus, but nevertheless manages not to talk down to you. In fact, the straightforward way the material is presented inspired me to learn calculus on my own to back up the material. Regardless, it gives you (almost) everything you need to start coding neural networks on your own. Where I did have some trouble it was owing more to lack of experience with Python and the Jupyter environment. I also would have liked a little more visibility into the data we were inputting, although I think that may be covered more in classes further on in the specialization.
par Arnaud S•
Oct 29, 2017
I found this course absolutely excellent. The structure and approach are absolutely great, and I am very happy that you force students to understand the mathematical underpinnings of backpropagation instead of letting a DL framework do the heavy lifting for you. Engineers need to understand what they do deep down.
My only improvement suggestion would be to provide a more detailed explanation of why we do the matrix multiplication & transpose in the computation of dW and of dA[l-1]. It turns out that in the case of dA[l-1] the explanation goes to the heart of reverse-mode differentiation and how to avoid combinatorial explosion. Cfr's Colah's blog excellent paper on backpropagation for details.
par Simranjit S P•
Jan 19, 2020
I liked this course very much. I have done coding and trained models in Pytorch and didn't have strong grasp in the math's part i.e Gradient and derivates that is the why i have taken this course at the first place. Though the course doesn't contain everything but it has given me enough knowlegde to start with deep-learning.The quiz and programmning excersice are really good. I have to think enough at some part and have done mistakes many times but got my concepts cleared. And thanks to coursera team for approving my financical aid.And though review may be good or bad depending on the person but i have learnt what i want to learn and it is good enough rahter than youtube or online material.
par Mukund C•
Sep 13, 2019
Absolutely Fantastic. I thought the programming assignments were a little too easy, but that's probably because I am familiar with python programming. I must say that the structure of the code really helped me focus on the core algorithms and vectorization (using numpy methods), so, in retrospect, it is probably a good way to make the student focus on the core concepts. I wish, however, there were some (more) optional lectures on the math and some more detailed derivations and some "optional" practice problems on doing partial derivatives etc., just to cement some important concepts such as back propagation. Highly recommend this to students wanting to learn the basics of neural networks.
par Greg A•
Sep 06, 2017
Awesome course. I have fairly little previous math experience though I have been working on some calculus and LA immediately before/while taking this, and all the topics were easy enough to understand how they are supposed to work. Much recommend.
One small thing I think could have helped a bit is the practical examples do a little bit too much hand holding. It makes it a little hard to know if you are actually grasping the knowledge or just able to tell what to do based on what information has already been made available from the templates and such. Had to step outside of this and try to do some of it on my own to see which pieces weren't fully making sense. But still, awesome course!
par Yaseen L•
Sep 08, 2017
Great, just like the first Intro to Machine Learning course Professor Ng distributed. Same style with improvements made in course design. For example, notation is much more consistent this time around probably because it is a more focused course unlike the first one. I would say taking Intro to ML first would help as it is a perfect primer for this course. Also, I'm glad they've decided to use Python which is just much more general purpose than MatLab. I would also say a solid grasp of the language is needed as a lot of boiler-plate code is provided and understanding it could be difficult if not otherwise comfortable with Python. Looking forward to continuing the full specialization.
par Borut H•
Mar 16, 2019
Amazing course! The creators are very good teachers. Materials have the right mixture of motivation, real world examples, theory and practice. I also like Andrews presentation style - one can really feel that he truly cares about the students being given good information and getting encouraged to learn. The assignments were also very well made - everything works, the code is good and there is so much help in the context/comments (eg. someone could even finish the labs without understanding the subject) - but this basically allows each student to choose how much effort he/she wants to put into the subject (also meaning how much knowledge she/he wants to absorb during this course...)
par Debraj T•
Apr 29, 2018
I found this course very helpful in furthering my understanding and clearing a few doubts that I had from the Machine Learning course. I seem to understand back propagation much better now.
This course also helped me give a structure to the steps involved in actually building a Neural Network... gives me more confidence.
My only issue was with the programming exercises. I felt they were very tightly structured, maybe because of the automated grading system. It was almost impossible to go wrong. More flexible and open exercises, I think, will help in learning the real intricacies of building a NN from scratch. Don't really know enough to comment on how this change can be incorporated
par Chitra V•
Jan 09, 2019
The course is well structured and the programming exercises are so detailed, I am going to refer to them in future while implementing neural networks. The best part about the course is, Andrew Ng actually taught the math behind the network. Rather than taking his students through a library function for neural networks in python, he taught his students how to code from scratch while also covering nuances such as suitable activation functions for different cases and ideal values for weights. The documentation for programming exercises is very detailed and must have taken plenty of time for those who worked on it. Recommend it for anyone wanting to start. Kudos to the instructors!
par Ritesh A•
Jan 13, 2018
The programming assignments (things which the student had to fill in) seemed repetitive and very limited (e.g. mostly needed only mathematical formulas to be filled in using numpy). However, to keep the grading similar and also cater for less advanced users simultaneously, the assignments could be tiered by beginner-intermediate-advanced (by concealing more and more stuff) but still grade based on the current beginner level only. So, one could start with advanced and then reveal more to get to intermediate in case he is not able to solve etc .. May be optional bonus grades for solving it at advanced level etc.
Otherwise a good course overall to get intuition into deep learning.
par ABEL G G•
Aug 29, 2017
Oh My God, my first Coursera course that i have finished to the end.. Supper happy and supper excited till I go to the next one. It is so engaging that even working on a temperature above 30 in no AC room did not slow me down. I also started this course while i was officially in vacation since I could not wait till i get back from vacation. Anyways, Very good content, easy to follow and the fact that I had to implement all the theory right away was just super. I learned not only the power of NNs but also my favorite programming language Python. Any one with a motivation and interest in DP should take this course because it gives the foundation in the best way possible.
par Ripon K S•
Aug 03, 2019
This tutorial was so elaborated. And in each week Andrew Ng tried to recap important findings from previous lessons which were helpful. Sometimes it looks fuzzy to recognize if the instructor is referring some notation as raw or vector form. But mostly it was nicely designed. I love the way programming exercise was designed. It can provide the basis to build a neural net from scratch. Considering all levels of users, he gently represented all the complex term like derivative in a simple way. Maybe for the future suggestion, Besides handwriting, if those calculations of those function can be displayed in animated design, then it's possible to make it simplified enough.
par Ekaterina B•
Jan 10, 2019
Andrew Ng is a fantastic intructor. I admire his teaching style. He pays so much attention to the fundamentals instead of rushing through the material, that I feel like I learned something that will actually stay with me. The homework codes are written beautifully. Introduction of broadcasting and vectorization was an eye opener - turns out I've been programming very inefficiently for years without knowing. This course on it's own is not enough for me to go and architect NNs on my own, but it definitely helps with general understanding of the process, I feel more confident now talking about it and reading papers. Will continue on to other courses in Specialization.
par ANGIRA S•
Mar 31, 2018
A must for anyone in deep learning research. This course aims to build the foundation of deep learning operations by not using the built-in functions but writing code yourself, which help tremendously later. It gives you the microscopic view of what calculations are carried at each neuron, layer, forward pass & backprop.
The interviews provide the right kind of motivation for aspiring researchers. They're like the cherry over the cake! The syllabus describes the course material but whats a plus in this course is Prof. Andrew Ng's tips when it comes to applying techniques and information about the latest (and probably near future) trends of the academia and industry.
Nov 16, 2017
After studying Lesson 1, I learned a lot and solved many problems I've been puzzled before. Andrew-NG's depth explanation and detailed writing move me deeply. Teacher's profound knowledge and responsible attitude is my learning example .The teacher can make the complex knowledge lively and interesting, but without losing its own contagion. After-class exercises design is also distinctive, providing great convenience for our beginners . After class, the active discussion and exchange provide a wide range of ideas and rich ways to me. Thank you, deep leaning team. we thank coursera for offering rich courses, thanks to Miss Wu's team for doing so excellent course.
par Dmitry T•
May 03, 2018
Considering how clear and thorough lectures by Andrew Ng were and overall how hard things were made simple in this specialization I can't give it anything but 5 stars. Thank you very much for your hard job on it!
However, I would prefer a bit harder and more theoretical course, personally. This one was adapted for a very broad range of listeners, which is a good thing generally. But it is absolutely not challenging to pass it: for instance, the programming excersices are great notebooks, but they mostly are already solved for you and you only need to fill the right lines into the right places. Only the last course on sequential models probably was a bit harder.
par Nishant K G•
Jun 04, 2019
Very well designed and thought through course - Highly recommended for those who want to learn neural networks from scratch even extending it to deep learning.
This course will empower you to understand, create, and tune a neural network. Clearly describes about Parameters, Hyper-parameters tuning, Forward Propagation, Activation Functions, Backward Propagation, Updating Parameters and Predicting Labels.
On a side note :: Before this course I was only aware about analogy of human brain's neurons and neural network and after this course I am able to understand that no one knows (even neuro scientists) that what a single brain neuron does.
HaPpY Learning Guys !
Sep 11, 2017
Good introduction to Neural Networks. Professor Ing does a great job of simplifying the ideas for folks like me who did Masters in Operations Research more than 2 decades ago. This course brought back the happiest memories of my graduate school days on how gradient descent works. The course also took away the mystery I felt about what I am familiar with i.e. optimization vs how the human mind works. I have not gotten a clue on how the human mind works. I have no idea on how the neurons in the brain fire. I just know that neurons form a giant network and I have always enjoyed network flow algorithms thanks to Professor Dijkstra. This is a really good course.
par Juan S D•
Oct 27, 2019
Excellent introduction to neural networks and deep learning! The course is very well structured, coming from the basic concepts of neural networks, up to building a modular deep layered network. Andrew does an amazing job at concentrating in the underlying and most important principles of deep learning, without spending too much time into the nitty-gritty mathematical and technical aspects of the topic. The lab programming exercises are insanely well written, and the ML interviews at the end of each week gave me a lot of perspective into the field and motivation to keep learning. Thanks to the deeplearning.ai team, you made an amazing job with this course!
par André M•
Oct 22, 2019
Fantastic course, even better than the ML course by Andrew Ng. I love the Jupyter notebooks and have found them such an improvement over the ML's (already good) approach with MatLab. I've learnt tons not just from the course content, but basically from dissecting in my own Jupyter notebook what is going on in each lecture and programming assignment.
This course/specialisation is worth every penny. The interviews with heroes of DL have been very interesting and add a lot of value too. I love that Andrew always asks them about career advice and found Ian Goodfellow's interview particularly inspiring. Thank you Andrew and to all the team making this possible!
par Harley J•
Oct 14, 2017
This course is excellent for both total beginners and people with a little experience in deep learning. I've implemented a few DL networks before, setting hyperparameters based on best practices. However, in taking this course, I came to understand the reasons behind some of the best practices I've used in the past. Dr. Ng does a great job of training and scaffolding for each lesson, building on the previous materials and leading to the next lessons. I'm also glad that he included interviews with big names in Deep Learning, so that I could see what's going on in the cutting edge of DL research, as well as finding more resources for learning even more.
par Sanjit k•
Jun 23, 2018
I had previously gone through the popular course on Machine learning by Andrew and that course was quite exhaustive for starters. In this course we learn about how to build deep networks through python programming language. My one complaint is that the programming exercises were easy compared to his previous course. I think starters also wont find the programming exercises very difficult.I found the python implementations very good. The way you build helper functions first and then go on to program higher Layer neural nets. Through this course you will learn not only the basics of deep learning but also how to structure your code in an efficient manner.
par Marta B G•
May 23, 2019
Really a nice course to take. I´m deeply thanked to Andrew because of his large capacity to simplify complexity - he's really didactic. I loved the way he build concepts from the very simple to the most complex, so that one thinks -- got it!. I like the interplay Adnrew uses between building blocks conceptualization (practical) and algebra & analysis foundations beyond (theoretical background). The assignments are very practical to follow , though after the course one probably couldn´t code from scratch unless she has a large practice on Python, the course is enough to settle the main concepts and learn a good collection of nice tricks in Python.
par Jay P G•
Dec 24, 2019
Well , this has to be the best course for intro to Neural Networks and Deep learning . This course dealt with the basics and mathematics behind Neural Networks and the coding part was well covered in the assignments . If you pay proper attention during the lecture and make notes (I wrote in notebook) , it will help you later while revising all the concepts .
And while doing the assignment be honest and if you're not able to get any answer , just think for some time , pay attention to the small mistake you may have done , revise the concepts and you'll definitely get the answer .
Thanks and Congrats Andrew and his team for making such a great course