Explicaciones matemáticas claras y una ensenanza organizada. No sólo lo considero útil para nuevos sino también para aquellos que quieran ver nuevos puntos de vista. Yo ya programé redes neuronales, y sinceramente, la forma en la que Andrew Ng las ensena y te las hace programar fue útil pese a todo.

La mayoría podréis recurrir de manera directa al código, no obstante, recomiendo encarecidamente hacer una escucha atenta de las explicaciones teóricas.

Easy-to-follow and very informative course with thorough explanations and intuition behind the deep learning approach. The course and assignments are build to make sure one understands the math and main concepts. I am happy with the amount of comments and guidelines in the programming assignments: this helped me understand covered topics step-by-step without having to be stuck in non-relevant issues like wrong shapes in Python many lines of code ago.

This is an extremely helpul course as it takes up everyhting from the start and hence also provides the basic foundation that you need to understand the Regression and ReLU part. Also understanding gradient descent without a firm grip on linear algebra is a tough task in itself but Andrew makes it a lot easier for us through the in depth explanation and the forthcoming assignment practice. Simply put, You learn a great deal with ease in this course.

This is expertly designed to teach you exactly what you need to understand the inner-workings of neural networks. Prof. Andrew Ng knows his stuff and he delivers in a manner that's unmatched. I am now more confident of my skills in building models with frameworks such as Keras, PyTorch, etc because this course taught me how to build them from scratch. I highly recommend this course and my biggest gratitude goes to Prof. Andrew Ng. He's a rockstar!!!

I can say, that I started this course from scratch. Without any knowledges in ML. The only experience that I had was C#, so Python and all those libs was a bit scary at the beginning. But now.. OMG. Thanks to Andrew Ng and all others who participate in this course creation. Very clear explanations, super powerfull comments on the Jupyter notebooks and so on... I feel that now I can give lectures on this topice by myself :) Thank You very much, guys!

I thank Andrew Ng for clear explanation of some non trivial or even difficult math details and well prepared notebooks. Having to say that sometimes I found programming code in notebooks as a difficult to read and I wanted to refactor it many times (like refactoring data structures to store cache, parameters etc.). However at the end I realised that thanks for such unclearness I understand slightly better what is going on under the hood. Thank you!

I think a clear concept in deep learning is as important as the choice of activation function in a neural network. This course is perhaps the best activation function for your deep learning understanding. Andrew Ng is the one the best tutor I've ever seen and his ability to take things from scratch and build it up is just fantastic. The assignments are well designed to make you think about one problem at a time and then build the blocks one by one.

This is my first exposure to data science. I have never programmed nor had much exposure to advanced mathematics. Andrew Ng makes the learning process enjoyable by breaking each part of a neural net into its smaller components, which has given me a much better understanding of the whole.

I have come away from Course 1 with more confidence in my own abilities to understand complex ideas. I am truly looking forward to moving on to Course 2. Thank you!

I really enjoyed learning this course. Not being a programmer and no prior python knowledge , i was able to learn successfully & complete assignment. I may not be a master (yet) but i am confident and can understand very clearly what is involved in deep neural network & how to solve a problem. The course is really recommended as the videos, assignments are well sequenced and made for beginners ! Thank you deeplearning.ai & Andrew Ngyuen and Team.

The first course of the Deep Learning Specialization is pretty much all-rounded, almost all you need is explained during the lessons. The advanced math techniques that normally scary people was explained in simple terms, and in fact as told by Andrew, the students don't really need a deep understanding of them, just how to apply them. Overall, a great introductory course, looking forward to enrolling in the following courses of this specialization.

It is very clear and helpful to learn how to build a neural network , even for me who was not familiar with Python. It is showed by easy examples of the common procedure and structures of typical neural network. And also I learned many useful functions and libs. Especially, I have learned how and why the Backward propagation is working, which I could not understand before.

Thanks a lot!

Now I can't wait to start building my own learning machine! ^_^

This course is the best course i have ever taken in Deep Learning. The detail explanation of the mathematical concepts and the examples are just on point. The programming exercises are also perfect for practicing of the theory. The Instructor, excellent in his explanations i really enjoyed this course. I will on any day recommend this course to anyone interested in learning Neural Networks and Deep learning. I can't wait to begin the Next course.

This particular course was an introduction for me in neural networks because I'm studying mechanical engineering, and provided me with motivation to attend other similar online courses in the domain of artificial neural nets. Overall it was a compact course with comprehensible teaching process and quick, sufficient and effective online lessons which were necessary and useful for all the assignments and tests. I' m glad that I attended this course.

Neural Networks and Deep Learning turned out to be a really fun and educational course. I always wanted to learn how the commonly used dnn libraries work and there was no better way to do so. Professor Ng's instructions made the concepts rather simple and easy to grasp; building neural networks from scratch couldn't have been any easier. I'm glad I opted for this course and I'll be moving ahead to the next courses in the specialization right away.

A really good course , however the exercises should be made more difficult as they become really easy. Do not comment evefrything that a person has to solve. Especially in the very last coding assignment. Once a person has completed the assignment before it, he.she can easily complete the last one. By not mentioning the availaible functions, a person would be tempted to go back to previous assignment and read once again.(Thus, revising the course)

Excellent intro to the basic principles of neural networks. As a 40+ non-technical, non-coder entrepreneur, I'm probably not the "target audience" for this class. Also, I have no previous experience with Python. Nonetheless, I still found the combination of lectures and assignments perfect for my learning. As the "big picture" became clearer throughout the weeks, the class triggered new venture ideas and new ways to apply NN in my current arena.

Great course, explain the basis of deep nn perfectly just as the old machine learning mooc. It's of introductory difficulty. The programing homework in ipython notebook taught you coding step by step with through guildline. If you have done the old machine learning mooc or the NN for DL by Hinton, you can skip this one or take it as a review if you are not familiar with python ML coding since both the old ML and NN for DL are offered with Octave.

If you want to learn Deep Learning, I think you should register prerequisite like data science or machine learning.

Neural Networks and Deep learning is the beginning course of Deep learning Specialization but you will start logistic regression first.

I have studied this in my university class and it was a little bit easy to understand.

The course quality is quite nice because you will handle logistic regression and neural network without libraries.

Highly recommend this course to understand neural networks at an intermediate level. You won't have to understand heavy vector/matrix calculus but you do use the resulting equations (given in the course) to implement an N-layer neural network in Python. If you are just starting I recommend the Stanford Machine Learning Specialization courses. Thanks to Andrew and the staff for this wonderful offering - all of these courses are a pleasure to take

This course is super useful , the assignments have added a lot of information and intuition to me . thanks alot for your effort to provide the information in a very easy and systematic way .

Proud alot that I've achieved 99% grade in that course , of course I am looking forward to achieve 100% in the next course , I'm sure that it will be a very useful one like that one , I hope I can complete this super fantastic specialization thanks alot !!

Now I understand why this course is one of the top rated courses on Coursera. The lessons of Prof. Andrew Ng are really great, starting from the basics, he guides you to implement a real neural network by yourself, and I can say that, in the end, you feel rewarded. I encourage every person with interest or curiosity in Deep Learning that take this course, you will not regret of doing it. A basic knowledge of Python is required, but no very much.

-Each and everything is explained in detail with simple examples.

-Helpful things:

-Quizzes

-Videos

-Programming assignment in python using Jupyter Notebook is exciting.

-I recommend this course before doing Machine Learning by Prof. Andrew because in that course only high level usage is given. But in this course actually nut and bolts are discussed.And Prof. tells the intuition behind almost everything. In my view for beginners this course is good.

Très bon cours car :

cela énumère les domaines d'application des modèles

cela donne les intuitions sur les couches cachées

cela rentre dans le détail de l'estimation du modèle (de A à Z)

cela présente la vectorisation et une méthode de programmation qui essaie de minimiser le temps de calcul

Par contre la partie évaluation est peut être trop simple et je conseille d'essayer de lire le code en détail ligne par ligne avant de passer au cours suivant.

Pretty love the step-by-step teaching style. Good for both beginners and people who have taken one/two ML course before. If one is very new to python/ML, I feel it would be better you do things from scratch and compare your answer with the provided notebook, which is very clear and well organized but may be too instructive. Anyway, it is a very good course and definitely worth the time. And I would go for the next course of the specialization.

Programming assignments are really good. In general, I got to know from people who code that you don't get to know what's happening in the internal neural network structure if you're using a python library, but here, I got to learn each and every step/block in the code as well as in the lecture videos. I thank Dr. Andrew for his great explanation of mathematics associated with neural networks. Looking forward to catch up with the other courses.