Retour à Réseau de neurones et deep learning

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67,274 évaluations

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12,716 avis

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....

Oct 21, 2017

Andrew Ng's presenting style is excellent. Makes the course easy to follow as it gradually moves from the basics to more advanced topics, building gradually. Very good starter course on deep learning.

Sep 13, 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.

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par John L

•Dec 24, 2017

Great foundations. I really like to learn from the bottom up and this class provides exactly that experience - build your own NN from scratch. While I do like using Jupyter notebooks for the class to avoid the need to configure a local dev environment, I also find the "write 2 lines of code" style a bit limiting. At times (especially on the final assignment) it felt like it was more an exercise in book-keeping than exercising my knowledge. But of course, for a robo-graded class I think it would be a lot to expect more free-form assignments.

This is a great first class on deep learning and I will highly recommend it to my colleagues at Microsoft.

par Vincent D W

•Oct 21, 2019

I was implementing convnet using keras for my undergraduate thesis before, and confused with the terminology used (hyperparameter tuning, gradient descent, global minima, etc). Alas, i persevere and finished my thesis with explanations i found online (albeit with much-unanswered questions and uneasy feelings). I decided to take this course to really dig deep into how this so called "brain simulation" works and i'm glad i did. It's giving me the much-needed intuition into how neural network really works. I now understand the mechanism behind gradient descent, and even gained insight into what derivatives really is (it is just a rate of change!)

par Balaji H

•Jan 06, 2018

The course was great. The videos provided very clear explanation and intuitions behind critical components of the Neural Network. The course built beautifully from a single neuron to a multi-layer multi-neuron model, making it clear step by step. The most helpful & interesting part of this course were the quiz and assignments. Assignments gave a great understanding on the implementation of neural network and how to build them in a very modular way. Building this way, will really help anyone define and experiment with different models easily. The sincerely appreciate the time invested by the authors to build this quality course. Thanks a lot.

par Marc A

•Mar 11, 2019

This is a nice follow-up to Andrew Ng's Stanford ML course. This one digs deeper into neural networks specifically, so if that's what you're interested in, this is a great course to take.

Note that the Stanford course used Octave and this course uses Python and NumPy (in Jupyter notebooks), so this is also nice because it gets you accustomed to using technologies that are more similar to what real ML practitioners are using. This course does still have you implement things by hand with NumPy and does not delve into higher-level frameworks like TensorFlow. For that, you will have to wait for the next course in the Deep Learning Specialization.

par Giuseppe

•Nov 03, 2019

This course is amazingly paced and also strikes a very good balance between required knowledge and depth of the topics covered. I cannot imagine how to improve this course except by asking for "more of the same". I had enough background in math and computer programming and I read already some articles and tutorials on Neural Networks. But only after this course I grasped the concept a little better. Andrew Ng is a very good educator: always ready to trade one pound of mathematical rigor for an ounce of intution. And I believe this is the only way to provide good contents here on Coursera. I strongly encourage everyone to take this course.

par sampson w

•Jul 31, 2018

I've tried other introductions to deep learning courses, and they seem to focus too much on math or too much on coding - assuming the student is coming from one discipline or the other. This course nicely addresses both the math behind the algorithms, and the code required to implement it, without delving too deeply into either and focusing on the core of DL. This course uses Python and the libraries commonly seen in Kaggle kernels, and includes interviews with some of the most prominent names in AI, making it very relevant in 2018. I took the machine learning course from the same instructor and enjoy the delivery and organization.

par fheinrichs

•Sep 14, 2017

As always, Andrew Ng's explanations help to grasp the material quickly and effectively. The programming exercises are interesting, yet not too challenging.

The course is, however, a bit light on the theoretical side. So if you are a practitioner looking for "hands-on" experience to get started with deep learning, by all means, this is your course.

If on the other hand, you are looking to understand the theory behind some of the concepts (i.e., you are not to afraid of a bit of math and would like to, e.g., see the derivation of the backpropagation algorithm), this course alone might not satisfy you. But it's a good start nevertheless.

par Maximiliano B

•Oct 06, 2019

This course is excellent and it is a great introduction to deep learning. Every week you learn new techniques and at the end of the course you are able to build a real deep learning application. If you have a solid math background you will gain a better intuition about the details of the algorithms. Finally, Professor Andrew Ng explains the content clearly and shares several best practices as well as useful advices that will make your learning experience very rich. I've loved the heroes of Deep Learning interviews and it is a great plus. I definitely recommend this course and I can’t wait to start the next one of the specialization.

par N Z

•Jan 18, 2019

Amazing course! I have tried learning concepts of neural networks by creating a syllabus for myself which consisted of different resources over the net. However at some point or another I would always reach a big obstacle which would prove to be extremely difficult to surmount and I would always inevitably give up. This course is structured in such a way that respects the current level of the learner and guides the learner through all the concepts without it being impossibly difficult or too easy. This course is only the beginning and I would gladly continue pursuing the other courses to strengthen my deep learning foundations!

par Michael C

•Sep 23, 2017

Excellent course. Surpasses Andrew Ng's original Machine Learning course in conceptual depth and ease of implementation. The lecture videos, quizzes, and programming assignments are all targeted towards someone who knows nothing about deep learning or machine learning, yet manages to elaborate on surprisingly advanced topics which you would not expect to make an appearance in an introductory course. It strikes a superb balance between simplicity and depth that is rare even in in-person university courses, and much rarer still in MOOCs. I will be taking all the rest of the courses in the Deep Learning Specialization. Well done.

par Hong X

•Oct 02, 2019

I've learned to build the basic binary classification model from conventional logistic regression to a shallow model (with one hidden layer) up to any layers of ANN. One of the most rewarding point for me is that I start using python (other than Matlab with which I have stuck for years until recently most cutting-edge open-source codes are found delivered in Python!). Although there is still a long way to go , I found well warmed up by those delicately designed step-by-step programming exercises in Jupyter notebook. Therefore, I do appreciate the course materials contributed by the lecturer as well as the exercises-designers!

par Chi W C

•Sep 13, 2017

Wonderful class. I started out not knowing anything about neural network or deep learning. I was able to follow the class lectures to get a sense of what was going on. The assignments were clearly structured and well organized, and serves as excellent examples in how to build this type of applications (by small building blocks and test each of the block carefully).

At the end, I was able to build my first neural network implementation in recognizing a cat!!

(However, I have uploaded 3 non-cat images, but NN failed by predicting these were cats. On the contrary, logical regression correctly predict the 3 images as non-cat).

par Carl G

•May 06, 2018

Andrew Ng is a thorough teacher and shows how online platform can be as engaging as taking a live class. His pace and style of writing slides is perfect for keeping pace taking notes by hand (my preferred way for efficient learning). He takes time to explain in depth how NN's work, and even more important his experience how to use them. Homework is a bit simple, but also appreciate to not be mired in coding details. Nice to be able to focus on how NN's works. Best part is that each piece of code can be fully tested against known output before used further. Illustrates nicely good practice once doing real coding project.

par LIM W X

•Jan 13, 2018

Through the Neural Networks and Deep Learning course, I have learned the fundamentals of neural networks and deep learning. The lectures are simple and easy to understand. The assessments have designed to test students in the fundamental knowledge of neural networks and deep learning. The assignments are designed to guide students on how to design and implement a shallow and deep neural networks, by applying what have been taught in the lecture. In conclusion, I enjoyed this course and I will definitely continue the deep learning specialisation courses to achieve my career goals. Thank you Prof. Andrew Ng and Coursera.

par Michael B

•Sep 18, 2017

Andrew, like no other instructor, manages to convey difficult material in a clear and concise manner. Even after many years experience in machine learning/deep learning, this course lead to many "aha" moments where many things I learned about the topic came together! The only criticism that I have for this excellent course is that I wish it would contain some, maybe optional, videos that go deeper into the math of for example backprop. I think this is a difficult concept to grasp and I imagine that if Andrew would sketch the proof with is clear and concise style, a lot more people had a much better understanding of it.

par David R T F

•Nov 01, 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.

par HUA E C

•Oct 28, 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!

par William L K

•Sep 06, 2017

Excellent course. Lectures are clear and concise. Professor Ng makes it seem so understandable despite the complexity of actual programming implementation! Assignments are both relatively straightforward (overall concepts) and tricky (keeping track of the matrix manipulations in Python). I don't know how many times I started a programming assignment, hit a wall in terms of programming errors, and came back to it after a time and getting through that error. Persistence, at least for me, was definitely a major component. Well worth the time put in. Looking forward to taking the next class in the sequence.

par Laith M A R

•Aug 18, 2018

I am so proud and confident of the things i learned. i never expected to learn this much from an online machine learning course, so many concepts that were vague to me in the past are now Crystal clear, and prof. Andrew does an outstanding explanation for each concept, not to mention that the programming assignments are extremely beneficial and cover every concept explained throughout the videos in a really cool, professional way. This has been only the first course in the deep learning specialization i am currently pursuing, and it made me so much more excited for the upcoming courses! thank you coursera :)

par Jong H S

•Sep 30, 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.

par Fezan R

•Apr 22, 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.

par Mohammad H R

•Aug 30, 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.

par Mallikarjun C

•Jan 31, 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.

par Carsten W

•Dec 28, 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!

par Heshmat S

•Dec 27, 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. :-)

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