17 de jan de 2020
Very structured approach to developing a neural network which I believe I can use as foundation for any project regardless its complexity. Thanks professor Andrew Ng and the team for their dedication.
12 de set de 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.
por Tim F•
1 de nov de 2017
Andrew does a fantastic job of making this material accessible. This course is a great introduction to deep learning and won't overwhelm you with the details of the underlying mathematics. If you understand some fairly basic linear algebra and know how to take derivatives you'll be fine. The lectures are incredibly clear, and this is one of the best Coursera classes I've taken. The only critique I have is that the homework could be a little bit more challenging - or (if that would undermine the introductory nature of the class) there could be additional optional problems that push students a little bit harder.
por Kevin C•
28 de out de 2017
A review from a business student with some programming and statistic foundation.
The programming assignments are great, guiding you to build part by part of the model.
Whenever you feel unsure what to do, make sure you read the instruction carefully, as clues/hints are often in there.
It's feels so awesome that I could finally construct deep neural network by myself instead of using packages that I have "some kind of" idea what's happening behind the scene.
Thank you Andrew! Your courses really inspire me, and when I become a master some day I will share my knowledge and experiences to inspire younger generations!
por Jake K•
19 de nov de 2022
I am sincerely and genuinely impressed with the quality of this course. Despite the fact that it's been a VERY long while since I've done any calculus or linear algebra, Andrew Ng's explanations of the derivations and other equations were enough to get me up to speed, and the first principles approach made it easy. The only thing I will say is you will definitely struggle if you're not aware of, or at least familiar with calculus and linear algebra. Having actually studied ML at an undergrad level, I can honestly say I never really understood how NNs actually worked until I started this course. Cracking work!
por Ningchuan W•
9 de jan de 2021
Good introduction to deep learning.
As Andrew said, the hard part is correctly deriving the matrix derivatives. not a easy job because it needs many prerequisites.
When I did programming assignments, I was not familiar with python syntax and had to google sometimes. I hope that Andrew includes more useful tricks/techniques that are commonly used in real life programming. I am not a programming beginner. But after years being in school, I am kind of less sharp than before.
The deadline gave me some pressure too. I am having more responsibilities as my family size getting bigger.
Thanks Andrew's team again.
por Jong H S•
30 de set de 2017
This course is really an essential first step to AI. Using Logistic Regression to kickstart is a great way to demystify Deep Neural Network. One of my greatest weaknesses in learning Deep Neural Network was keeping track of correct dimensions in matrices. This course has a special topic on that, very thoughtful indeed. Having taken Geoffrey Hinton's Neural Networks for Machine Learning, I still consider the programming assignments to be very challenging but there are plenty of materials that helped me getting through it. All in all, this is a timely, thoughtful and extremely effective Deep Learning course.
por Fezan R•
22 de abr de 2019
Andrew NG is the most humble and talented teacher I ever came across. This course is paced right for beginners like me, prior to this course I had taken his Machine Learning course. I had basic ideas of logistic regression and Neural Network before. But this course enhanced my learning and also Python is a big help. (though sometimes i have to look for documentation even for most simple things, like getting a random array of certain dimension, but it aint a big deal). The core of this course is the understanding of forward and backward propagation. Which Andrew did with great details and make it simplified.
por Shubham P•
21 de jan de 2021
The instructor is exceptionally knowledgeable in the field. The way he explained the concepts was superb. All the difficult concepts were made exceptionally easy. I thoroughly enjoyed the course and learned many things throughout. Also the assignments on the way were very helpful for a hands-on session. They were a means to apply the concepts we learned and build confidence. Looking forward to learn more concepts in next course of the series. And I'd like to say thank you to Andrew Ng and to all his colleagues who are directly or indirectly involved in the creation of this beautiful course. Thank you all!
por Chang X•
18 de jun de 2020
Such a great course! I had some basic khnowledge but without a systematic view. This course totally made me more familiar with the foundation and theory of deep learning. I am so grateful.
One thing I think can be improved is the tips and hints in the programming assignments. It appears the instruction are very detailed and I think the team can consider making a harder version of the programming assignments for those experienced students.
Moreover, the Jupyter Notebook is fantastic, but it can be hard to navigate through the window, so maybe an outline view (with all the function names) would be helpful!
por Mo R•
30 de ago de 2019
Amazing course. Andrew has really streamlined the concepts, made the course easy to follow and at the same time leaves room for further analysis and curiosity. It is so well structured that can transfer complex concepts easily to you and therefore maintain the excitement in the student to keep on going at his/her own speed. What I loved most about the course was the fact that for some reason it seems like Andrew knows where to give you further explanation about what just happened or where you might get stuck in the code and in the lecture. Thank you Andrew. Such an amazing experience and great structure.
por Raghuraj M•
1 de jun de 2020
To start speaking, this is a really good course.
It guides through the basics of how to build a neural network instead of just importing from sklearn library. It helps one understand what is happening behind the scenes when one imports models from libraries like sklearn, PyTorch, etc. This course has taught how efficiently one can decrease computation time using vectorization as it made programming that enjoyable and exciting, it also reduced the time taken to complete the program exponentially.
I would recommend everyone who wants to learn how a machine learning model works and also build their own model
por Mallikarjun C•
31 de jan de 2019
I found this course to be extremely good. It covers nicely theory, implementation and application of Neural Networks and Deep Learning. Prof. Andrew Ng through his video lectures makes it fun and easy to learn this subject with the right emphasis on key points. The quiz's and program assignments are really good, reinforcing the concepts. In addition I found the Hero's of Deep learning conversation videos towards the end of each week, informative and thought provoking. This is my second course after taking Machine learning on Coursera. I am enjoying learning on Coursera. Thank you Prof Andrew and Team.
por Chatchai J•
27 de nov de 2021
คอสนี้เหมาะกับใคร: ไม่เหมาะกับคนที่ไม่เคยเรียนเลย แบบมาเรียนอันนี้ ตายตายแน่ๆ เพราะเขาไม่ได้สอนแต่เริ่มต้น อารมณ์ประมาณว่าต้องมีความรู้ในตัวด้วยแล้วถึงมาเรียนได้ แนะนำคอส จาก อ. นพดล ช่วยได้เยอะเลย ควรมีความรู้ในการใช้ python -> pandas, google colab เบื้องต้นบ้างเพราะคอสนี้ไม่ได้สอนอะไรมาก มาปุ๊บจับเราโยนทำๆๆ แต่ยังดีเขายังไกด์ว่า search google ด้วยคำนี้นะแล้วลองอ่านดู ก็ถือว่าไม่ต้องไปค้นยันรากเง้า ความรู้แคลคูลัส กับ matrix ก็สำคัญ เพราะในนี้จะคณิตศาตร์พอสมควร (ใครบอกเขียนโปรแกรมไม่จำเป็นต้องเก่งคณิตนี้ไม่ใช่ชัวร์) แต่ถ้าไม่รู้ก็ไม่เป็นไรเพราะในคอสนี้เขาอธิบายพอสมควร แต่รู้ไว้จะสบายกว่า
por Maryllia K•
27 de out de 2020
Excellent step by step introduction to NN and DL. I took the course to brush up my ML/DL skills and strengthen my understanding on the DL basic concepts. I couldn't be more satisfied by the structure and overall layout of the course. The instructors give the right amount of detail for a beginners course without omitting important concepts. Plus, with the code available a practitioner can go ahead and practice the exercises on their own to make sure that they have mastered the concepts or identify the areas they might need to practice more. I really enjoyed the course and I would highly recommend it.
por Carsten W•
28 de dez de 2019
Fantastic course with well structured Jupyter notebooks for your Python programming assignments. The assignments were pretty easy due to extensive explanations and repetition of key formulas from the lectures within the notebook. To be fair to others, maybe it was also a bit easy, because I just recently completed Andrew's older Machine Learning course (with programming in Octave and still highly recommended for a slightly deeper foundation in ML - I think), so I was already well familiar with the key concepts, vectorization etc, which I only had to transfer to Python. In any case, awesome course!
por Heshmat S•
26 de dez de 2017
I've taken Andrew's "machine learning" course before, which I loved so much and learned a lot from it. The only issue with it was the use of "matlab/octave"; fortunately, he switched to "python" in this specialization course. :-)
This first course in the "deep learning specialization" is a very well though-out introduction to deep learning. Starting from logistic regression, Andrew builds upon the materials and masterfully introduces the more sophisticated concepts one after another. The programming assignments make the course even more fun and practical. Loved the course.
Thank you Andrew & Co. :-)
por Obaid S•
6 de jul de 2019
This course is one of the best online course I have taken so far. With basic math knowledge (you just need to know what is a vector and what is a slope) you can complete all the assignments and the course itself. In this course, you get in-depth knowledge of how a neural network works by implementing it yourself. The best thing about this approach is that you will be very confident as you start playing with high-level libraries like tensorflow, since you will know what is going on under the hood. I think this course is a great place to start if you are new to deeplearning before using any library.
por Fabian A•
28 de out de 2017
I really enjoyed the Jupyter Notebook approach as it really suits my experience with Python3 and love of pedagogical and sound presentation of theory. The code can sometimes be a bit too forgiving in that it would be possible to go through it without thorough examinations of dimensions, calculations and the like. I, however, am doing this for learning rather than certification so it was a minor issue.
Really nice videos, a clear structure and a very thoughtful balance between the complexities of math and the "get things done" possibilities that jupyter notebook and Coursera permits. A great course!
por Debmalya M•
17 de mai de 2020
Perhaps this is the first course of this type that does not use any fancy python libraries to do something as complex as deep learning. It just uses numpy. For this reason, if tomorrow the python language gets obsolete, skill transfer would be very easy. The assignments are not too hard If you watch the videos regularly, but the contents are by no means easy to understand, particularly the parts where the instructor teaches matrix dimensions and backpropagation. I think watching the videos is not enough unless you practice the concepts yourself, with datasets downloaded from some other websites.
14 de mai de 2018
This course is friendly to novice because Andrew is adept at making the originally complicated lessons easy to inteprete, and his clear pronounciation and moderate speed help students catch up his pace without extra effort even for non-native English Speaker.More importantly, we all known that Andrew is known as a prominent AI scholar around the world, and his intelligence is sparkling through the course, for example, the systematic course structure reveals his in-depth knowledge, as well as the practical advices on buliding a deep learning model shows his rich experience in actual implemention.
por Sikang B•
4 de dez de 2017
Compare to the machine learning class years ago, this revamped NN and DL class took very modern approach and really take machine learning education to the next level by using new technologies, better programming models and last but not least, Python Notebook for education.
Assignments are helpfully guided, however the guidance felt a bit too excessive at times. Some text could be better delivered as hints rather than instructions.
This course is less demanding and is definitely perfect as an introduction course. The interviews are super relevant and highly engaging. Make sure you don't miss them.
por José A•
15 de set de 2017
It's only my first week in the course, and I'd say it's been good. It can be a little bit tedious to catch up with the terminology if you haven't seen any Data Mining or Machine Learning . Nothing that a good devotion of Google and YouTube-fu can't tackle.
Other than that, I have a very basic knowledge in the topic and I have had to do some good research about it. The 2nd week's lectures goes through each of the steps in building a Neural Network, including the explanation of a Gradient Descent, Logistic Regression, and derivatives.
I'll see if I can update the review after finishing the course.
por Paulo A•
26 de ago de 2017
Andrew Ng is the best! Congratulations to all the team involved in the course. It i at a very good level for everybody to join in. As an experienced programmer I though the programming assignments were on the easy side, but I guess they are at the right level for people coming from other areas. As for the maths, I think is a good idea to leave the deep stuff out of it and get people building the NN as a long as the maths behind it is solid, which it is in this course. People can delve deeper in other sources. I'm quite excited for the next 4 courses! All the best to the team and to the students!
por Mark D•
22 de mar de 2018
I loved the programming assignments. The tasks are nice, the visualization of the neural networks' decision boundaries is very helpful, and the setup with the Jupyter notebooks is just awesome. In other courses it is often required to set up a programming environment first, which sometimes takes more time than the programming exercises themselves. In this course it was possible to dive into Python and Numpy immediately - without worrying about file paths, environment variables, compatibility issues and other nuisances. The lectures were also very good. All the concepts were explained very well.
por Rahul K•
27 de fev de 2018
Beautifully structured course! Feels like a walk in the park if you've already completed the 'Holy Bible of ML', i.e., Andrew Ng's Machine Learning course on Coursera.
Very good programming guidelines, and a gentle introduction to anyone who isn't aware of the core concepts on Machine learning.
If you're wondering whether you should complete the Machine Learning course first, by all means, go ahead. However, I can guarantee that there will be no hardships faced even if you're a beginner in ML and want to dive head-first into Deep Learning.
After all, it's Andrew Ng who we're talking about here! :)
por Omair M•
25 de nov de 2017
Prof. Andrew Ng explains all concepts from a very fundamental level and even nervous students will feel encouraged by his insistence on "don't worry about it" for derivations you don't understand. The assignments have a lot of hand-holding but I needed that to focus on other more important concepts instead of debugging python code which can be learned in a different course. Overall, I have learned how to build a deep neural network using a building-block approach and gained confidence regarding this domain which I had previously taken to be mysterious and cryptic and perhaps for the elite only.