Retour à Réseau de neurones et deep learning

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20,273 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....

MZ

12 sept. 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.

NL

3 oct. 2020

This course helps me to understand the basic concept of Deep Learning. However I think this course should include at least 1 week (or 2-3 videos) about math so learners can have a better understanding

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par Justin T

•11 oct. 2018

Fantastic course! As someone who has done several online tutorials that use frameworks like TensorFlow or Keras, even having implemented things like deep reinforcement learning agents and image classifiers with these frameworks, I've never really gone through any formal training on much of the lower-level concepts/mathematics of deep networks. But this course cemented a lot of the fundamentals about deep learning into my brain that I was missing before, organizing topics in a clear and concise sequence of videos/lessons that really helped me keep things organized in my own head. Doing the exercises in a "manual", framework-less way using just standard Numpy was an awesome and enlightening experience too!

par Balachandran S

•16 oct. 2017

One of the best Deep Learning courses (probably 'THE' best) around. Been a fan of Andrew Ng's sessions after I took his Machine Learning course in Coursera.

The course is perfectly designed such that its complexity increases gradually every week. The instructor makes sure that the participants follow completely.

Some points about the course -

1) Assignments are made too easy (too much hand-holding). But maybe the others who are new to programming might appreciate it better.

2) Andrew laboriously reiterated the points that required understanding.

Sometimes, I felt the course topic to be highly repeatative but later after completing the assignments, I felt it was required to be so and it was totally worth it!

par Vishnupriya V

•17 mai 2019

As a beginner who is interested in Deep Learning, this course was very useful an informative. The explanations given by Prof. Andrew were to the point and precise. However, detailed explanations behind the mathematics(calculus) could've been given as optional videos. The quizzes and the programming exercises were also very challenging at a beginner level. They quizzed us not only on the equations but also on the concepts. The only drawback in this course that I faced was trying to submit my programming assignment through Jupyter notebooks. They can very annoying at times. Also, the discussion forms were very active with the mentors and fellow student who would quickly help by replying to your questions.

par Steve S

•3 déc. 2017

This course is a very thorough introductory review of neural networks that doesn't require expert level knowledge in some of the underlying math like calculus, but nevertheless manages not to talk down to you. In fact, the straightforward way the material is presented inspired me to learn calculus on my own to back up the material. Regardless, it gives you (almost) everything you need to start coding neural networks on your own. Where I did have some trouble it was owing more to lack of experience with Python and the Jupyter environment. I also would have liked a little more visibility into the data we were inputting, although I think that may be covered more in classes further on in the specialization.

par Mohit A

•22 août 2020

Hi to all team who had put their Mount Everest's height like efforts for making this a fab course and self explained assignments.And special thanks and love for my dear Andrew sir for teaching too smoothly and always relaxing their students by saying "If you don't get it,don't worry, we will see it after sometime".One more Special credit and mention for team who built these amazing self explained and too easy to understand python notebooks so that even a kindergarten student can get this and one only needs to pay attention on making skills and not on other things like how to fetch data,and how many and which libraries to be added etc.

Thanks and kudos to all team and loads of respect to DEAR ANDREW SIR.

par Arnaud S

•29 oct. 2017

I found this course absolutely excellent. The structure and approach are absolutely great, and I am very happy that you force students to understand the mathematical underpinnings of backpropagation instead of letting a DL framework do the heavy lifting for you. Engineers need to understand what they do deep down.

My only improvement suggestion would be to provide a more detailed explanation of why we do the matrix multiplication & transpose in the computation of dW and of dA[l-1]. It turns out that in the case of dA[l-1] the explanation goes to the heart of reverse-mode differentiation and how to avoid combinatorial explosion. Cfr's Colah's blog excellent paper on backpropagation for details.

par Simranjit S P

•19 janv. 2020

I liked this course very much. I have done coding and trained models in Pytorch and didn't have strong grasp in the math's part i.e Gradient and derivates that is the why i have taken this course at the first place. Though the course doesn't contain everything but it has given me enough knowlegde to start with deep-learning.The quiz and programmning excersice are really good. I have to think enough at some part and have done mistakes many times but got my concepts cleared. And thanks to coursera team for approving my financical aid.And though review may be good or bad depending on the person but i have learnt what i want to learn and it is good enough rahter than youtube or online material.

par Mukund C

•13 sept. 2019

Absolutely Fantastic. I thought the programming assignments were a little too easy, but that's probably because I am familiar with python programming. I must say that the structure of the code really helped me focus on the core algorithms and vectorization (using numpy methods), so, in retrospect, it is probably a good way to make the student focus on the core concepts. I wish, however, there were some (more) optional lectures on the math and some more detailed derivations and some "optional" practice problems on doing partial derivatives etc., just to cement some important concepts such as back propagation. Highly recommend this to students wanting to learn the basics of neural networks.

par Greg A

•5 sept. 2017

Awesome course. I have fairly little previous math experience though I have been working on some calculus and LA immediately before/while taking this, and all the topics were easy enough to understand how they are supposed to work. Much recommend.

One small thing I think could have helped a bit is the practical examples do a little bit too much hand holding. It makes it a little hard to know if you are actually grasping the knowledge or just able to tell what to do based on what information has already been made available from the templates and such. Had to step outside of this and try to do some of it on my own to see which pieces weren't fully making sense. But still, awesome course!

par Yaseen L

•7 sept. 2017

Great, just like the first Intro to Machine Learning course Professor Ng distributed. Same style with improvements made in course design. For example, notation is much more consistent this time around probably because it is a more focused course unlike the first one. I would say taking Intro to ML first would help as it is a perfect primer for this course. Also, I'm glad they've decided to use Python which is just much more general purpose than MatLab. I would also say a solid grasp of the language is needed as a lot of boiler-plate code is provided and understanding it could be difficult if not otherwise comfortable with Python. Looking forward to continuing the full specialization.

par Borut H

•16 mars 2019

Amazing course! The creators are very good teachers. Materials have the right mixture of motivation, real world examples, theory and practice. I also like Andrews presentation style - one can really feel that he truly cares about the students being given good information and getting encouraged to learn. The assignments were also very well made - everything works, the code is good and there is so much help in the context/comments (eg. someone could even finish the labs without understanding the subject) - but this basically allows each student to choose how much effort he/she wants to put into the subject (also meaning how much knowledge she/he wants to absorb during this course...)

par Debraj T

•29 avr. 2018

I found this course very helpful in furthering my understanding and clearing a few doubts that I had from the Machine Learning course. I seem to understand back propagation much better now.

This course also helped me give a structure to the steps involved in actually building a Neural Network... gives me more confidence.

My only issue was with the programming exercises. I felt they were very tightly structured, maybe because of the automated grading system. It was almost impossible to go wrong. More flexible and open exercises, I think, will help in learning the real intricacies of building a NN from scratch. Don't really know enough to comment on how this change can be incorporated

par Chitra V

•9 janv. 2019

The course is well structured and the programming exercises are so detailed, I am going to refer to them in future while implementing neural networks. The best part about the course is, Andrew Ng actually taught the math behind the network. Rather than taking his students through a library function for neural networks in python, he taught his students how to code from scratch while also covering nuances such as suitable activation functions for different cases and ideal values for weights. The documentation for programming exercises is very detailed and must have taken plenty of time for those who worked on it. Recommend it for anyone wanting to start. Kudos to the instructors!

par Ritesh A

•12 janv. 2018

The programming assignments (things which the student had to fill in) seemed repetitive and very limited (e.g. mostly needed only mathematical formulas to be filled in using numpy). However, to keep the grading similar and also cater for less advanced users simultaneously, the assignments could be tiered by beginner-intermediate-advanced (by concealing more and more stuff) but still grade based on the current beginner level only. So, one could start with advanced and then reveal more to get to intermediate in case he is not able to solve etc .. May be optional bonus grades for solving it at advanced level etc.

Otherwise a good course overall to get intuition into deep learning.

par Patrick B

•16 janv. 2021

I took the Machine Learning course last year, and my only complaint was that the neural networks part was a bit confusing. (Once the bias unit had to be in the weights matrix, once it had to be removed in order to perform some calculations.) The formulas also didn't work all the time, and you needed to figure out the proper shapes (using transpose operations) on your own.

In this course, the notation and explanation is much clearer. Keeping the bias unit out of the weights matrix in the first place makes everything easier. (Of course, the video quality is also better.)

Thanks Andrew; you really listened to your audience and figured out how to explain those concepts more clearly!

par Abel G

•29 août 2017

Oh My God, my first Coursera course that i have finished to the end.. Supper happy and supper excited till I go to the next one. It is so engaging that even working on a temperature above 30 in no AC room did not slow me down. I also started this course while i was officially in vacation since I could not wait till i get back from vacation. Anyways, Very good content, easy to follow and the fact that I had to implement all the theory right away was just super. I learned not only the power of NNs but also my favorite programming language Python. Any one with a motivation and interest in DP should take this course because it gives the foundation in the best way possible.

par Ripon K S

•3 août 2019

This tutorial was so elaborated. And in each week Andrew Ng tried to recap important findings from previous lessons which were helpful. Sometimes it looks fuzzy to recognize if the instructor is referring some notation as raw or vector form. But mostly it was nicely designed. I love the way programming exercise was designed. It can provide the basis to build a neural net from scratch. Considering all levels of users, he gently represented all the complex term like derivative in a simple way. Maybe for the future suggestion, Besides handwriting, if those calculations of those function can be displayed in animated design, then it's possible to make it simplified enough.

par Gaetano S

•11 avr. 2020

Andrew is an exceptional teacher. Thanks to him, I clearly understood the structure of a neural network and the functioning of the whole network starting from the single neuron.The mathematics behind a neural network, which until recently seemed very difficult to me, is now very clear.

This course is even better than the one on Machine Learning of Andrew Ng because here you can directly use Python with the Numpy library and all the part of the exercises and practice is, in my opinion, much better structured and clearer than the other course. I recommend it to anyone with an interest in Artificial Intelligence. I can't wait to continue my Deep Learning Specialization.

par Ekaterina B

•10 janv. 2019

Andrew Ng is a fantastic intructor. I admire his teaching style. He pays so much attention to the fundamentals instead of rushing through the material, that I feel like I learned something that will actually stay with me. The homework codes are written beautifully. Introduction of broadcasting and vectorization was an eye opener - turns out I've been programming very inefficiently for years without knowing. This course on it's own is not enough for me to go and architect NNs on my own, but it definitely helps with general understanding of the process, I feel more confident now talking about it and reading papers. Will continue on to other courses in Specialization.

par ANGIRA S

•31 mars 2018

A must for anyone in deep learning research. This course aims to build the foundation of deep learning operations by not using the built-in functions but writing code yourself, which help tremendously later. It gives you the microscopic view of what calculations are carried at each neuron, layer, forward pass & backprop.

The interviews provide the right kind of motivation for aspiring researchers. They're like the cherry over the cake! The syllabus describes the course material but whats a plus in this course is Prof. Andrew Ng's tips when it comes to applying techniques and information about the latest (and probably near future) trends of the academia and industry.

par donglingwang

•15 nov. 2017

After studying Lesson 1, I learned a lot and solved many problems I've been puzzled before. Andrew-NG's depth explanation and detailed writing move me deeply. Teacher's profound knowledge and responsible attitude is my learning example .The teacher can make the complex knowledge lively and interesting, but without losing its own contagion. After-class exercises design is also distinctive, providing great convenience for our beginners . After class, the active discussion and exchange provide a wide range of ideas and rich ways to me. Thank you, deep leaning team. we thank coursera for offering rich courses, thanks to Miss Wu's team for doing so excellent course.

par Dmitry T

•3 mai 2018

Considering how clear and thorough lectures by Andrew Ng were and overall how hard things were made simple in this specialization I can't give it anything but 5 stars. Thank you very much for your hard job on it!

However, I would prefer a bit harder and more theoretical course, personally. This one was adapted for a very broad range of listeners, which is a good thing generally. But it is absolutely not challenging to pass it: for instance, the programming excersices are great notebooks, but they mostly are already solved for you and you only need to fill the right lines into the right places. Only the last course on sequential models probably was a bit harder.

par Nishant K G

•4 juin 2019

Very well designed and thought through course - Highly recommended for those who want to learn neural networks from scratch even extending it to deep learning.

This course will empower you to understand, create, and tune a neural network. Clearly describes about Parameters, Hyper-parameters tuning, Forward Propagation, Activation Functions, Backward Propagation, Updating Parameters and Predicting Labels.

On a side note :: Before this course I was only aware about analogy of human brain's neurons and neural network and after this course I am able to understand that no one knows (even neuro scientists) that what a single brain neuron does.

HaPpY Learning Guys !

par Jagdeep S

•10 sept. 2017

Good introduction to Neural Networks. Professor Ing does a great job of simplifying the ideas for folks like me who did Masters in Operations Research more than 2 decades ago. This course brought back the happiest memories of my graduate school days on how gradient descent works. The course also took away the mystery I felt about what I am familiar with i.e. optimization vs how the human mind works. I have not gotten a clue on how the human mind works. I have no idea on how the neurons in the brain fire. I just know that neurons form a giant network and I have always enjoyed network flow algorithms thanks to Professor Dijkstra. This is a really good course.

par Juan S D

•27 oct. 2019

Excellent introduction to neural networks and deep learning! The course is very well structured, coming from the basic concepts of neural networks, up to building a modular deep layered network. Andrew does an amazing job at concentrating in the underlying and most important principles of deep learning, without spending too much time into the nitty-gritty mathematical and technical aspects of the topic. The lab programming exercises are insanely well written, and the ML interviews at the end of each week gave me a lot of perspective into the field and motivation to keep learning. Thanks to the deeplearning.ai team, you made an amazing job with this course!

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