Retour à Réseau de neurones et deep learning

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13,145 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....

Mar 07, 2019

I understand all those thing which you have discussed in this course and I also like the way first tell story of concet and assign assignment. Now I fall in love with neural network and deep learning.

Nov 27, 2017

Fantastic introduction to deep NNs starting from the shallow case of logistic regression and generalizing across multiple layers. The material is very well structured and Dr. Ng is an amazing teacher.

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par Neeraj B

•Aug 22, 2019

This course is extremely helpful for beginners as well as people with experience. The course goes through a proper structure where first Andrew explains each concepts in detail in his lecture videos and each video covers 1 specific topic allowing you to process the material. This is extremely important if you're new to deep learning. Then there are practice questions to test your knowledge of the material covered in the lectures. Then you get programming assignments to actually implement what you have learned and not to mention for people with little python and calculus knowledge he even has some videos explaining basic python and derivative concepts related to neural networks and enough for completing the programming assignments. This course has been a wonderful refresher for me.

par Alex T

•Jul 21, 2018

A great introduction to deep learning. This course explores topics like binary classification, logistic regression, gradient descent, linear algebra in the context of neural networks, forward and backward propagation, computing cost/loss functions, the function/definition of parameters and hyperparameters in deep learning, coding classifiers in python using shallow and deep neural networks, general industry trends, and what misconceptions about deep learning exist in the media today.

+Python, Jupyter Notebooks, NumPy (and other packages)

I highly recommend this course, and it is do-able even for those without much coding or math experience. Thanks to the the team at deeplearning.ai for developing this course! I am looking forward to the following courses in this specialization!

par Pramod H

•Dec 12, 2017

A very well compiled course indeed. It has the signature style of teaching of Prof Andrew Ng where he dives into the concepts thoroughly without any compromise and bolster them through the coding exercise following it. The course focuses on the basic building blocks of neural network by taking away some of the burden of basic python syntax which is already pre-built and provided to you. That said such pre-built code is limited so i never felt that something major is left out. The programs are also built using smaller functions as building blocks. Some of the sections especially the week 4 exercise was a little longer and tougher but after spending some time to look at it for the second time helped understand it. Overall i have 5 stars with 2 thumbs up for this course.

par Hasan R

•Apr 24, 2018

Neural Networks and Deep Learning was a first ever course which I studied online, and after studying this course, It made my enough interest that want to take other courses as well. The thing which I liked most about this course was that it was beautifully structured. Andrew Ng explained the things in a way that I thought these concepts cannot be better explained. During lectures, Andrew Ng shared his experiences about writing python codes efficiently which helped me to complete the programming assignments in time. Most enjoyable part of this part was doing the programming assignments because every step was explained (what are we going to do and what we will achieve) and expected results were also shown to confirm our results before submitting the assignments.

par Daniel C K

•Aug 29, 2017

Great introduction to Deep Learning for those with no experience in the field. Guides you step by step through the exercises. If you've taken Andrew Ng's Machine Learning class, this course is mostly review with a few updates on Deep Learning notation and slightly more advanced vectorization for neural networks. The use of Python is nice, although Python doesn't come with vector manipulation built in like Matlab does. This leads to slightly more cryptic errors, but if you've used Python before, this shouldn't be problematic. In particular, the use of Jupyter notebooks makes for a clean interface, but debugging in the notebooks is more difficult compared to Matlab or Spyder. Overall an easy course to get you working in the Python Deep Learning environment.

par Christopher C

•Dec 02, 2017

Nicely eases someone with modest numerical Python experience into neural nets. Test-driven Jupyter notebooks (with the test data and tests themselves provided) made the programming exercises pretty easy, almost trivial. But that's how it should be--this course was really to introduce the concepts behind deep learning, and enough implementation so that students have an idea of how the tools they'll use work behind the scenes. Most of us will grab Keras-on-TF or something analogous and never mind the details, but this course nicely forces one to internalize at least some of how the sausage gets made. Andrew Ng is also a great lecturer, and his use of the presentation tools were masterful. The interviews with Names to Know were icing on the cake. No regrets!

par Mark M

•Oct 23, 2017

This was a great introduction of computing neuronal networks. As I came from the programmers site and my active math experience lies years behind it was a challenge to recap all the math behind the ML algorithms for me. But this is perhaps the major strength of this course to really make ist understandable. Honor for Prof. Ng his didactical concept. Also keeping track about the vectorized representation of the formulas together with careful elaboration of dimensionality following the forward and backward propagation chain helps to make the coding of the NN algorithm easy to handle. Think otherwise I would have wasted my energy in managing all the matrice and vector operations. Never thought that it is so easy to implement your own neuronal network class.

par Aaron H

•Sep 01, 2017

Good coverage of the basics of neural networks with hands-on exercises using numpy.

The notation is a little surprising -- most of the time we math people talk about dy/dx as being the derivative of y with respect to x. That is, when I wiggle x a little, what happens to y? The notation in this course assumes that everything is a derivative of the cost function with respect so something else, so the notation only includes the "something else". For example dW is the derivative of the cost function with respect the weights in the matrix W.

If you are not careful, it is easy to lose track of what dZ means.

If you are pretty comfortable with vector calculus, it moves pretty slowly at times. If your calculus is rusty, I think the speed is probably perfect.

par Jeff W

•Oct 08, 2017

While I'm good at perl, I wanted to learn python, and as I'm a learn-by-doing kind of person, I thought an ML course in Python would be a good place to start. I was surprised that "Deep Learning" was a bunch of the neural network techniques I'd played with in the past, and was a bit apprehensive about the amount of calculus that would be required.

This class breaks down the ML concepts quite simply, and helps you understand how to actually build and apply logistic regression, and then use that as a building block to deeper neural networks. They also give you an intuitive understanding of the mechanism and underlying math, without requiring endless pages of derivations.

I recommend this course to anyone looking to get a solid overview of ML techniques.

par Shehryar M K K

•Oct 02, 2017

This was my first foray into the field of deep learning. Dr. Andrew is an amazing instructor his humble demeanor made learning really enjoyable. I really like where he went into derivatives and did it step-by-step making me understand the math behind the scenes. The programming assignments were super easy only difficulty was my lack of practice with python. If I would have to improve on this course. I would say add articles for further readings with a short quiz after it related to the article. I would also like to take this opportunity to thank the coursera team who accepted my application for financial aid without which I would have never earned this certificate. Thank you for allowing me to learn something new and for making it easy and enjoyable.

par Tyler K

•Aug 19, 2017

Fantastic as always. I do wish it had a lot more math but I understand the challenge delivering that to a larger audience. My favourite aspect of Andrew Ng's classes is actually the absolute response by the grader system.

I learn very effectively in environments where I receive complete feedback on my problem submissions. Allowing me to correct my understanding of the material and retry. Contrast this with the PGM course where total scores are not returned and there are a limited number of submissions. I felt that my learning was stunted in that environment as there was no opportunity for me to correct my understanding of the material myself and have it re-scored.

Hopefully we'll see more math heavy classes in the future that retain this style :)

par Arpit S

•Dec 09, 2018

Finally, I had to sit down at a stretch and finish the course at a go! I think it was completely worth it and I thank coursera team for providing me financial aid to take this course. I am very grateful to have got this opportunity to learn from this excellent course. Will definitely complete all courses within the deep learning specialisation by a little at-a-stretch effort and i am sure it'll give a sweet boost to my understanding. The course material, Professor Andrew's way of explaining and the assignments are all incredible and i really enjoyed the modules for implementing back-prop the from-the-scratch way! Personally, I also feel the best way to take these courses is at a stretch which completely connects the dots for me. Thank you team :)

par Bryan H

•May 28, 2018

The programming assignments give you the hands-on experience you need to feel comfortable coding your own ANNs from scratch. Andrew's lectures are well-paced, easy to follow, and enjoyable.

Room for improvement:

The Jupyter notebooks, although convenient for the Python programming assignments, are unreliable. I spent 25 % of my time re-writing code because the notebooks wouldn't save, and I had to reload certain assignments multiple times, often at different times throughout the day. If you have made progress and the page doesn't save, then leave the tab open and copy-past your code into a new instance. Nonetheless, I can't fault the Instructors for the lack of fidelity in the intercommunication between Coursera's platform and Jupyter's notebooks.

par Liaw S W

•Sep 17, 2017

It was a great course, very well organized but after doing the programming assignments, I feel that I might not have fully grasped the concepts in lectures. The descriptions in the assignments were great and helpful, but I feel that the pace of the course was too quick, too easy. I feel that I must be missing out on something. Maybe it's because the teacher has done a very good job in explaining hard to understand concepts to the degree that they seem too easy to understand. Since this is only an introductory course in the series, it is understandable that it is supposed to be easier. Don't get me wrong, this course has very much substantial contents to it! Nonetheless, it was a great foundation course for the specialization! Thank you teacher!

par Noelle M V

•Aug 29, 2017

If you took Andrew Ng's original Machine Learning Coursera course in 2012 (as I did), you expect nothing less than an excellent course. Unlike Neural Network or Machine Learning courses at other learning sites, this one is far superior. If you are to ever going to fundamentally understand what is going on inside all those convenient Machine Learning and Neural Network software libraries and frameworks (versus just blindly using them), or perhaps build your own libraries; then you need this course. And, indeed, it is important to understand because not understanding removes all intuition as well as removes knowledge of boundary and limiting cases that you may encounter, which will make things harder for you. I highly recommend this course.

par Keely W

•Mar 12, 2018

I'm LOVING these classes!! The instructor, Andrew, is excellent, and the material is presented in a logical progression so that it's not too overwhelming. It definitely helps to have some background in math, namely Calculus and Linear Algebra. The programming assignments can be a bit tough if you don't truly understanding which Linear Algebra methods to use, i.e. dot product multiplication vs element-wise multiplication, but usually the instructions are good. However, I found myself having to look up a lot of Python and Linear Algebra basics online (Stack Overflow is your friend in this case.)

Definitely a challenging set of courses in the Deep Learning Specialization, but very well presented, and extremely interesting (at least to me.)

par Gary N

•Feb 29, 2020

This course allows you to quickly catch up to the fundamentals of building multi-layer NN models, by viewing it as stacks and layers of logistic regression units. You will sail through this course if you already know logistic regression. Even though nowadays most people don't even need to understand how the calculus actually works beyond a basic intuition, the calculus required for back propagation are well explained; detailed yet presented well for people with high school calculus to understand. The exercises are very simple with an objective not to test your ability to write code, but your understanding of how the steps are put together. The answers are practically given to you, you just have to put them together in the right way.

par Akhil C V

•Aug 09, 2018

This course is phenomenal. Even as someone who's spent almost a year working as a deep learning engineer, there were still many lectures I found incredibly useful. I believe the matrix dimension lecture will permanently change the way I structure the code for my neural networks in the future. If I had one criticism it'd be that it could perhaps get progressively harder. I love the ultimate task (of a logistic classifier), but as we go from week to week, I think there could have been less hints. Even by the end of the course, I felt like I was being spoon fed through the programming assignments. This is a problem, because I'm less confident than I would have been if I'd figured out the Lmodel forward propagation (for example) myself.

par Gokula K R

•Oct 19, 2019

Well, the concepts were crystal clear. To be honest, I got them theoretically but when i began coding, I could see that I could not connect few pieces here and there as there was the template given and I just had to fill in the blanks with whatever is given at the beginning of the module. I would suggest to let the learners code few functions from scratch, so that we could know what parameters to input, and what to return in the end. Also I think suggesting learners to visit documentations of few important modules like numpy, pandas, matplotlib etc. and instruct the learners to import them by themselves than importing them straight away at the beginning. Hope this would inculcate the developer culture and practice to beginners

par Anton V

•May 31, 2018

This is a great introductory course to deep learning and neural networks in general. The lectures are brilliant and so are the assignments. Best experience I've had with an online course. This one actually makes you want to complete it. I had some Python experience and a very foggy idea of how neural networks work after watching some youtube videos, however this course gives some really nice foundation for future development in the area. The assignments are easy to follow and give you code to use with things to fill in based on your understanding. This is a good way to get you started, I can now use those ideas to play around with a personal project and learn more. Looking forward to the rest of the courses in the series.

par Diego V R

•Aug 26, 2018

After Prof. Ng's Machine Learning Course, this new course appears at our Coursera's dashboard. I, again, enjoyed listening to Andrew's lectures. My personal recommendation is to first tackle Stanford's ML course and then this one. However, if you can only pick one and you are doubting between Stanford's Machine Learning Course and this one, pick this one, since it covers neural networks with way more detail than the original one. However, take into account that other machine learning related topics do not appear here such as dimensionality reduction techniques like PCA. Amazing treasure to the Deep Learning beginners out there. Thank you Prof. Ng and every one else who made this possible (The whole deeplearning.ai team).

par Nicholas B

•Sep 03, 2017

Definitely one of the best online classes I've taken. Even having studied this material a little bit before hand, I learned a lot--mainly in ways of building an intuition of why certain functions are chosen, or work the way they do. In this respect, I think there are few better at getting to the core of teaching: simple is better.

I appreciate that the course is designed to widen the reach of deep learning, but for those perhaps either more mathematically inclined or just extra curious, I highly recommend Lazy Programmer's Data Science: Deep Learning in Python class on Udemy for only $10. Gives a little more mathematical rigor...and hey extra practice in coding up a basic NN all in one script doesn't hurt. Thumbs up Ng!

par Vivek R

•Aug 25, 2017

Excellent course for beginners who are ready to put in extra effort to understand the material. By extra effort I mean repeatedly viewing the lecture videos and persisting with the programming assignment until the techniques are clear. That said, I am personally slightly disappointed with this course. Having completed Andrew Ng's original 2012 Machine learning course, I don't think there is anything new here for users like me. However it served as a good refresher. The other complaint I have is that the programming assignments are too simple. It's basically paint by numbers. If you really want to understand the material, you should write the programs from scratch and use any of the data sets available on the internet.

par Abhimanyu A

•Mar 03, 2018

What a marvellous roller coster ride it was! Thank you so much Andrew Sir and the entire team for putting up these efforts to provide such high quality material accessible to everyone! Cheers to all of you! I wish one day I would be able to share my knowledge like this! I wish!

One thing that I would like to add/suggest that, please provide some reference links, book recommendations and other useful information in the end of the program for anyone who wants to do some more research on the roots of the algorithms. Doing this, it will continue the learning path for the student and would keep the fire burning in him/her.

Please let me know if you have any questions regarding this review.

Thank you once again. :)

You rock!

par José A V M

•Sep 26, 2017

Amazing!! I've took part of the Udacity Deep Learning Nanodegree, but the math there was just not enough to my taste, here the thing is different, I love all the notation and the math behind. Also there are more focus in build the model step-by-step. As recommendation, I would like to include an activity to sketch the functions of the deep learning layers. For example, if I would like to build from scratch a deep learning model, what will be the functions I will need (in the assignments I could deduce them, but I would have like to have an activity related to this). Also I would like to have a little more focus in visualizing a small neural network, and write the values from the matrix to the visual representation.

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