Retour à Réseau de neurones et deep learning

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81,830 évaluations

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16,117 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....

Feb 12, 2018

I would love some pointers to additional references for each video. Also, the instructor keeps saying that the math behind backprop is hard. What about an optional video with that? Otherwise, awesome!

Jun 26, 2018

Really, really good course. Especially the tips of avoiding possible bugs due to shapes. Also impressed by the heroes' stories. Genuinely inspired and thoughtfully educated by Professor Ng. Thank you!

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par Rob M

•May 13, 2019

I've taken and finished Udacity's Nanodegree, and while it certainly has a lot of its own strengths, I came here to get another perspective on the math involved, especially in backpropogation and numpy operations. Lo and behold, this class (Andrew in particular, of course) delivered exactly what I was looking for. And because the course was supremely self-paced, instead of feeling rushed to hit an official deadline like Udacity's course, I was able to take the time I needed to watch the videos a couple times each, when necessary, and really drill home the concepts.

Lastly, the projects here at Coursera are extremely well thought out, organized, and testable. I *loved* the use of the numpy seed operation, so when I completed a function and tested it, I felt extremely confident that the inputs, operations, and outputs were exactly what I needed. At this point, I definitely like the approach to projects much better than Udacity's (always felt like more of a guessing game there).

I'm excited to start and finish the next course in the Specialization!

par Michael S E

•Feb 28, 2018

Excellent course. Quick introduction to the basics of neural networks. This course has very high overlap with Prof. Ng's course on neural networks at Stanford. This appears to be the updated version on his new DeepLearning.ai platform.

The programming assignments are very user friendly, in that the code is already highly structured with student code just to fill in a few blanks. They also provide built-in test cases. The difficulty level is not high compared to a more open ended problem formulation (let alone a real world task). The assignments do make efficient use of student time in that they focus on the essential aspects of the course material and minimize time spent on extraneous computer programming challenges.

I appreciated the consistent and strategically chosen notation, which makes it easier to translate formulas into code snippets. Ng's notation conventions allow you to make an educated guess at how to vectorize algorithms in numpy simply by capitalizing variable names.

Thank you for sharing your knowledge and expertise with us!

par Manuel G

•Sep 09, 2017

This is a great class to get introduced to deep learning concepts and get some hands on experience with the underlying machine learning aspects. The Jupyter notebooks are great in that you are left with something you can use later as a starting point if you want to do your own implementations. The flip side of that is that, in my view, the coding assignments are made too easy and I feel that after all the hints and given the code you are given, the student's contribution is a tad too trivial at that point. Still, this doesn't change my rating because from the perspective of learning about DL concepts, this is not a crucial point. Since the course is still very new, there remain a few bits of consistency in notation and other little details that haven't yet been 100% fixed, but there's a lot of activity in the forums to help you clarify things and give feedback on what is not working.

As usual, Andrew Ng does a great job of motivating and explaining all the concepts. If you enjoyed his ML class, definitely go with this specialization.

par Sandip G

•Mar 22, 2020

The content was very good and intellectually curated, and no complaints about a teacher of such high quality "Andrew Ng". Actually, I took the "Machine Learning" Course long before on Coursera from the same instructor, as I took this course now, which highly helped me to finish this in less than a week, although I never got time to complete the former course. Advice to any new students on this course would be to have a basic understanding of Machine Learning, which includes linear regression, vectorization et.al. , (or simply, "ML" course on Coursera).

One small amendment on this course could be to reshuffle the contents a little, from different weeks as I found the content which was in Week 4, to have high importance to be taught earlier in this course (for eg, getting matrix dimension right ), and there were others sub-topics in week 3 as well. I don't remember all of them, as I took 4 weeks worth of information, in just a single week :)

Very excellently taught, and contents, as well as assignments, were of topmost quality.

par Tony H

•Aug 16, 2017

Extremely well-taught and well-structured introduction to neural networks and deep learning. I found the explanations of forward and back propagation to be at a level suitable for getting the algorithms to work without swamping one in detailed calculus, but with enough detail to enable productive further study. There is an introduction to computation graphs that will hopefully lead into Tensorflow in the next courses in this specialisation. Professor Ng is a methodical, very knowledgable and interesting teacher and I really enjoyed all his video lectures. The weekly quizzes are reasonably challenging and the programming exercises very well written and enjoyable. If I have one minor criticism it is that there is perhaps a little too much 'hand-holding' in the programming exercises; I felt that some code was supplied that could have been left for the student to fill in, some very basic Python instructions could also have been left for the student. I am greatly looking forward to the next courses in this specialisation.

par Shibhikkiran D

•Jul 08, 2019

First of all, I thank Professor Andrew Ng for offering this high quality "Deep Learning" specialization. This specialization helped me overall to gain a solid fundamentals and strong intuition about building blocks of Neural Networks. I'm looking forward to have a next level course on top of this track. Thanks again, Sir!

I strongly recommend this specialization for anyone who wish get their hands dirty and wants to understand what really happens under the hood of Neural networks with some curiosity.

Some of the key factors that differentiate this specialization from other specialization course:

1. Concepts are laid from ground up (i.e you to got to build models using basic numpy/pandas/python and then all the way up using tensorflow and keras etc)

2. Programming Assignments at end of each week on every course.

3. Reference to influential research papers on each topics and guidance provided to study those articles.

4. Motivation talks from few great leaders and scientist from Deep Learning field/community.

par Rajneesh S

•Oct 08, 2017

I really enjoyed this course. Andrew really knows this topic very well and his passion shows in his teaching. The course was structured very well and was very easy to follow.

I underestimated the knowledge of math required for deep learning. I was never very good at math and it really has been a while I learned vectors, matrices, calculus etc., but this course gave a nice introduction to the math that is needed. However, for me personally, I still had to go back and learn the basic math concepts. Khan Academy and YouTube videos were very helpful.

I am very good in coding. However this course made me realize that there is not much coding as such for deep learning. Python libraries really makes it easy. You need to understand the mathematics and formulas, and after that, its all about the test data and your hyper parameters.

Unfortunately I have to take a break as I have to travel for business, but I am highly motivated and I will definitely return and complete the other courses for specialization.

par Prof. C H V

•May 25, 2020

Excellent course with hands-on sessions. It is really difficult to learn neural network and deep learning with only theory part. Practice along with theory makes course very much interesting. In this course, Python is used which is open source and freely available. But it is difficult to execute downloaded iPython notebook as dataset "lr_utils" is not available. However I could execute the code with other dataset but it was difficult initially. There should be separate video lecture about explaining how to solve assignment because initially it was difficult for me to solve the assignment. Grader was giving grade 0 even though code was right then later I found that I was removing some of the lines in comment region and hence I was getting 0 grade even though source code was correct. So special session about submission of assignment should be there. This was my first course on neural network and deep learning and it was great learning experience for me.

par Sebastian J

•Sep 10, 2017

Wonderful introduction to deep neural networks and the theory behind them. Programming exerices make for a fun way to try out concepts introduced in this course. Andrew has mastered the delivery of complex concepts and math behind neural networks in a systematic and discrete chunks, which allows for easier absorbsion of the material. One thing in particular that this course really shines at is looking at the computation graph of forward propagation and using it to explain derivatives used in backward propagation. This is one thing I missed in Andrew's Machine Learning course. Another subtle change which I found to have a big impact on the ability to reason about various computations in the choice on how to organize input and parameter matrices used in neural network modeling. I found the choices presented in this course a lot more intuitive than the ones in ML class. Many thanks to Andrew and his assistants for putting together this material.

par Nkululeko N

•Apr 05, 2020

The first course is very good for beginners, however if one has no background skills on how to program in python like myself, then this course is a bit challenging. Implementing all of what I've learned to the Juypiter Notebook using python 3.0 was a bit tricky but understandable as you learn. I feel happy and motivated to continue and finish the whole specialization course. I have a strong background in integration calculus, but because the last time I had to do calculus was years ago, it was also a bit tricky to understand some of the calculus concepts presented in the course. I think for the first time user, it will be highly advisable coming from my own thoughts that the student learn Calculus mathematics first and as well as the python specialization course before delving into this Deep learning course. I know the lecturer mentioned that it is not necessary to know Calculus maths, but personally I feel like people need it a lot.

par Novin S

•Feb 05, 2018

I liked the course very much. The videos and steps to get me to the point that I can really implement the concepts was very much helpful. Although I feel that I need more practice by programming. I think it would have been better if more programming practices provided.

Many of the programming parts that was related to the preparation of the data was provided. Maybe it could be beneficiary to do those parts on our own as well.

The forum is so crowded and hard to find my way around. Maybe something can be done about that as well.

In general I really liked the course, and I think it was the best way to learn the Neural Networks. Now I feel more confident to dive into text books and more mathematics of the NN. I would also like to add that I really loved the "heros" part. Get to know the community, history, and learning about the way that the pioneers and creators of a topic think was very helpful for me.

Thank you and good job

Novin

par Maxim S

•Jan 26, 2018

Dr Ng is an outstanding teacher. I like that the material was presented gradually and incrementally, without large gaps. I never felt like I was thrown into the deep end and forced to fend for myself, like I did in courses from at least one Coursera competitive. On the few occasions that I ran into problems with the assignments, browsing the forums was really helpful. With so many people in the class, there was always someone else who has run into the same issue I had experienced. Mentors are pretty diligent about responding to questions. I still struggle a bit with the math since it's been 20 years since I've had it in college. Wish I were still able to derive the equations Dr Ng used. It is great that Dr Ng provided derivations as optional lectures. One issue I have is that the choice of layer sizes hasn't been covered. Perhaps, it'll be covered in future courses in the specialization. Thanks.

par Rameses

•Oct 20, 2019

I have taken a couple of Neural Network classes at university level for my master's. I did learn a lot but this course on Deep Learning introduced me to concepts I had never had the chance to encounter in those classes. I enjoyed taking this class as well working on the assignments. The assignments are excellent even if most of the coding has been done for you. It is up to the student to understand the underlying code and to pick up Python if she/he has not encountered Python before. In this course, it is important to understand the core concepts before progressing to more complex concepts. I found myself frequently getting lost and having to revert to earlier topics to understand later topics.

It was a pleasant experience working with Jupyter notebooks, something I did not have the familiarity with.

Kudos to Andrew and team for making this course an enjoyable and rewarding learning experience.

par shunjie l

•Jan 03, 2019

Have you taken a course and has no idea what the lecturer is talking about ? If yes, I am happy to report that it is not the case with this course.

The materials are easy to follow and the video lectures's pacing is perfect for anyone with no experience with neural networks. They are well designed to help students to understand the basics of Neural networks by keeping materials focused but yet detailed enough.

Also, I have to applaud to Dr A. Ng's lecture delivery. Never once would he make students feel lost or discouraged, and he drop little encouragements along the way. It is like preventive-medicine, in the sense that he anticipated and took measures, to allow students to stay engaged and interested. Kudos !

TLDR: For anyone who has little to no background in Machine Learning and is interested in understanding rather than just knowing the basics with Neural Network, this course is for you.

par Yogesh G

•Apr 06, 2020

The prospects of deep learning is exciting in every field from science, engineering, medicine, economics and many more. If you have any interest in Neural networks and Deep learning irrespective of your academic background, then this specialization will be a great opportunity to you for learning and harnessing the power of deep learning in your field.

The best part of the specialization are the programming assignments which are based on building and implementing popular real life applications of deep learning. Even though this may seem tough, you will have to fill only the important snippets of the code(the rest is already there for you), which makes it intuitive and easy. I used python for first time in this course so the course also became way to learn python. Very well designed course structure through out the specialization! It's a great way to introduce yourself to Deep Learning.

par Ben T

•Aug 28, 2017

This was really good. Well paced and thought out. Paid attention to explaining the underlying fundamentals of math as well as the required Python programming elements. Important intuitions on how things work were useful for understanding the greater scheme of things. Also enjoyed the weekly "Heroes of Deep Learning" videos.

I completed the inaugural cohort of another online deep learning course and whilst it covered a lot of great material and current research in a short time the pacing was often too fast and as a complete beginner I was a little overwhelmed; feeling like I was always missing key concepts. I also found that Andrew Ng's videos contained less about personality and hype and felt like they were on a more personal level than some kind of mass market video.

I definitely feel like I've learned something useful and I look forward to the other courses in this specialisation.

par Dmitry R

•Apr 15, 2020

In the deep learning specialization provided on Coursera, you are taught the theory by professor Andrew Ng, who is the Co-Founder of Coursera and has headed the Google Brain Project and Baidu AI group in the past. Professor Ng teaches in a very relaxed and patient tone and the explanations are clear and well formulated. One of the major upsides I liked is that the notation used is carefully chosen and very clear. Professor Ng makes sure to reference the most important scientific papers that contributed to each idea, which is great if you want to dive a little more into details. To progress in the course, at the end of each major chapter you will have to submit a multiple-choice quiz and one or two programming assignments in python. The programming assignments require you to complete a 3/4 finished code, and the focus is on understanding the concept and not on programming.

par Anders N

•Jul 07, 2019

Easy to follow. My previous knowledge of calculus enabled me to verify some of the statements on my own which gave me a deeper understanding of the limitations and opportunities in the neural networks. However the training was designed so that I believe a person with zero calculus experience would learn how to write and run the code and feel they understood a lot more about deep learning.

Its incredibly rewarding to learn a skill that take you over the buzz-word level. This training gave me enough to have an intelligent discussion with industry experts, and even propose changes in algorithms that they had not considered them selves. This is more value than I expected. Granted, I spend quite a lot of time revisiting the material presented and making my own analysis during the course, but it would never have gotten to this level without Andrew Ng. I am totally impressed!

par Linda R

•Sep 04, 2017

This course is excellent! Andrew Ng is a man on a mission. He believes that Deep Learning will change the world, and this sequence of courses is his way of bringing Deep Learning everyone with a little background in programming and machine learning. This first course in the sequence meets the goal of explaining both the theory and implementation of forward and backward propagation with a clarity I had not seen before. As expected by anyone who has seen Ng’s previous course on Machine Learning, Ng’s lectures are well prepared and presented. He has paid special attention to using the appropriate notation, a real challenge in a subject plagued with so many indices. The practice questions give a good review of the lectures, and the programming exercises are very well done. The $49 charge for grading is well worthwhile, even if one is not aiming for a certificate.

par VLADIMIR G

•Aug 24, 2017

Finally Neural Networks & Deep Learning course explained extremely well! I can say this after completing Hinton's one and looking for a lot of articles, books and videos online - nothing is in comparison! I stand up and applaud to Andrew Ng (and people involved) with this course.

Every single detail I wanted to know is explained here in a very clear and simple way with a lot of carefully made examples and practical tasks provided for you to understand all required concepts even better!

After completing this part of Deep Learning specialization i feel confident about fundamentals and core NN/DL concepts and will move further with specialization completion & into AI world!

BONUS suggestion: I used space ambient music all the time on the journey throughout this course. It gave me some Star Wars feeling and made the experience so much more fun and interesting! Try it! :)

par David M

•Sep 01, 2017

Good summary of the basics of machine learning with neural networks. This course takes you by the hand and does not rush things. If you are new to the field and/or you are not comfortable with math and programming, it will be an enlightening experience. If you find the algebra and programming parts trivial, you can always fast-forward them and still get a useful (and entertaining) bird's-eye view of how artificial neural networks work.

All the algebra involved is laid out with extreme detail and the programming assignments manage to be very guided while being interesting and engaging.

Andrew's previous course used Matlab/Octave but this time everything is in python, and the assignments are done online in Jupyter notebooks. This is a great improvement both in terms of the course experience and in the skills learned (as today python is much more useful than matlab).

par Atul A

•Aug 17, 2017

Excellent course! 👍 I finished the course in under 24 hours. 💪

This course dives right into practical implementations after the initial theory of machine learning and neural networks. Andrew Ng's explanations of core theoretical concepts are both superb and solid. He gives a brief overview of important concepts (such as gradient descent, forward prop, back prop, learning, etc) and then jumps into implementation.

I loved the Jupyter Notebook assignments! They are great in understanding how to implement NN from scratch, going from basic to more advanced.

I did feel that some people might find the math notation a bit heavy or tedious (I did); however, it is important. I would have liked to see perhaps a simpler notation first, then a more complicated one.

Overall, highly recommend this course to anyone looking to get into this exciting journey of Deep Learning!

par Rohan G

•Jul 01, 2019

I *almost* didn't take this course as the specialization mentioned Tensorflow as designated deep learning framework for all assignments. I was more inclined towards PyTorch. My big mistake. The course has 3 programming assignments and none of them require the use of any framework. You implement everything (gradient descent, cost function, back prop etc) from scratch, using just Python & NumPy. And that's a great thing. Trust me. I would watch weekly videos and when the time came for implementation, I was forced to re-watch them multiple times to fully grok key concepts. All frameworks (Keras, PyTorch, Tensorflow) abstract and hide lots of complexities, and I believe when you are just starting to learn Deep Learning, wrestling with complexities and stitching things by hand is the correct way forward. This *has* to be everybody's first deep learning course.

par Björn K

•Jan 21, 2018

It's very clearly laid out and since it's not too long (4 weeks) it feels like you accomplish a lot as you finish each week. The programming assignments are well laid out with a lot of boilerplate already programmed for you. The course also gives you some basics in how a utility library "NumPy" works which is valuable in itself. Personally I don't care about Python, but the knowledge I've gained I can apply in more modern languages like Haskell and F# without any problems.

If I have any criticism it is that it's almost a little too easy to pass the course without completely understanding it. So there's some responsibility on your part to study extra on the side if you want a deeper understanding. But to be honest, I think difficulty level of this course is very reasonable, I don't have any university degree in mathematics or anything and I had no problems.

par Kryštof C

•Oct 31, 2018

This learning course helped me to sort my previous knowledge about neural networks (NN). It is very good for beginners and intermediate students of computer science. Students (anyone taking the course) with mid-advanced knowledge of calculus can see the math behind, which can help them understand the topics more deeply. For students without this knowledge - the teacher has explained the NN topics from enough-high perspective, so the architecture is clear, but some calculations might not be 100% clear then. On the other hand for basic fun with NN, it is sufficient. I would recommend this course to everyone who is starting with NN. One more recommendation for the creators of the course: Adding one lecture on feature extraction (just high-level one) might significantly help the students to understand the complexity of the deep/machine learning problematic.

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