Retour à Réseau de neurones et deep learning

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In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning.
By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to your own applications.
The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI....

AA

2 juil. 2020

Excellent course !!!

The flow is perfect and is very easy to understand and follow the course

I loved the simplicity with which Andrew explained the concepts. Great contribution to the community

SZ

7 juin 2020

Amazing course for anyone wanting to jump in the field of deep learning. Andrew explains the details very well. The assignments were structured very good that provided detailed instructions. Thank you

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par Shibhikkiran D

•7 juil. 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 muluken m

•30 janv. 2023

The Neural Networks and Deep Learning course offered through Coursera and authorized by DeepLearning.AI was a comprehensive and well-structured introduction to the field of deep learning. The course covered a wide range of topics from basic neural networks to more advanced concepts such as convolutional and recurrent neural networks. The instructors were knowledgeable and approachable, and the content was presented in a clear and engaging manner.

The course assignments provided a hands-on experience for applying the concepts learned, and the discussion forums allowed for peer-to-peer learning and exchange of ideas. The use of real-life examples and applications helped to better understand the practical applications of deep learning.

Overall, I would highly recommend this course to anyone looking to gain a solid understanding of neural networks and deep learning. It provides a strong foundation for further studies in the field and prepares individuals for real-world applications of deep learning.

par Rajneesh S

•8 oct. 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

•25 mai 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

•9 sept. 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 Narayan S

•7 oct. 2020

Andrew Ng is the simplest, most genuine teacher available online. 3 years ago when I first did his course on ML, I was enthralled just by the way he 'spoke' and 'drew' Maths. However, it was still one of my first MOOCs. I really didn't have much to compare. Moving ahead in time, I did plenty of online courses, saw plenty of instructors and came across a hundred fancy techniques to make us learn. Yet, I could barely find the will to complete things in time. Lately, I thought of trying my hand at core DL and I returned to NG, except this time I was deeply apprehensive and mostly half-hearted.What followed was pure magic. With just a digital pen, a writing pad and plain-old slides, NG explained some of the most intricate nuances of Adv. Maths in minutes. His ways were older than the whole damn digital age but still more effective than all the jabber around. I couldn't get up for hours at a stretch.Nothing, nobody, comes close to him.

par Tim G

•3 mars 2022

An excellent introduction of the basic building blocks.

In terms of constructive feedback / areas for improvement. I found Python/numpy matrix & vectors still caused a little frustration in the first coursework and whilst I appreciated the extra section on gotchas with vectors / rank 1 arrays and keeping things as columar/row matricies - it might be worth bringing forward the notes on cardinality and matrix multiplication earlier in the series, as I personally missed the transpose being required for the cost function in the logitsical regression coursework of week 1; ending up writing as: # ensure vector dot products - otherwise it seems we need to do a transpose (?!) positive_diff = np.dot(Y[0], np.log(A[0])) negative_diff = np.dot(1 - Y[0], np.log(1 - A[0])) cumulative_diff = positive_diff + negative_diff cost = -cumulative_diff / m rather than: cost = -(np.dot(Y, np.log(A.T)) + np.dot(1-Y, np.log(1-A.T))) / m

par Humberto F F

•5 nov. 2022

This course is introductory. It gives a broad overview of deep learning using a hands-on approach, i.e., it instructs you how regular and deep neural networks work in practice. As such, it is a good option for both novices in AI and IT professionals (my case) to catch up fast with the fundamental concepts of connectionist models. Andrew Ng has a lot of fluence in the field and is pretty charismatic, so that the classes flow naturally and get the attention of the learner. I can honestly say the course made me a better computing professional (because I learned I bunch of new things) and a better teacher (I teach computing at a technical school in Brazil). A piece of advice: if you intendo to apply for this course, you do have to have some mathematical skills (a bit of algebra and calculus is mandatory, basic statistics aids a lot) and mid to high level programming knowledge (if you know Python, the course will be a bit easier for you).

par Nkululeko N

•5 avr. 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

•5 févr. 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

•26 janv. 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

•20 oct. 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

•3 janv. 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

•5 avr. 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

•27 août 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 Mohammad A Q

•13 juin 2020

This course was phenomenal!

First I want to thank Professor Ng and the teaching staff as well as the Coursera team for providing such a great quality course.

I had taken the Machine Learning course by professor Ng before which was a great course itself but I had still some issues with backpropagation. (it was a little bit complicated) In this course, on the other hand, the professor explains backpropagation and the math behind it in a lucid, simple way.

Using python as the course's programming language was excellent. It is in my opinion what makes this specialization an absolute winner. The course's assignments and quizzes would make the concepts of the course even more clear.

The interview with heroes of the deep learning section was a great idea, professional people talking about how they got where they are and advising beginners on how to thrive in this path is really helpful.

par Vaibhav M

•14 oct. 2022

Amazing courses that go into detailed explanations about the math and intuitions behind the algorithms without getting too convoluted or making things unnecessarily complicated just for the sake of it.

Prof. Andrew doesn’t just tell you the name of a function for a library (like scikit

learn or tensorflow) and give you magic numbers for parameters. You actually build the model yourself and learn what the parameters stand for and what is the purpose of those parameters and hyper-parameters.

The specialization is well divided into meaningful courses and each course is well structured so that you know exactly what you are going to learn and what key specific skills you will get after completion of a course. Because of the quizzes and practical labs, after completing a course you actually gain confidence that you can design optimized solutions for that particular set of problems.

par Dmitry R

•15 avr. 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

•7 juil. 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 Фёдор О

•27 oct. 2020

Отличный курс, который позволяет довольно легко начать заниматься машинным обучением. По моему мнению он довольно простой (код в заданиях по программированию во многом написан за вас), но я бы сказал, что это скорее преимущество, так как из-за этого вы можете не распылять свои силы на технические детали и разбираться в алгоритмах. Курс отлично подойдёт для тех, кому нужно быстро в общих чертах понять основы машинного обучения. Спасибо, Andrew Ng

Perfect course, which help quite eazy start learn machine learning. In my opinion this course is pretty eazi (code in programming task largely written for you), but i think, that this is more of an advantage, because you don't waste your energy on technical details and better understand algorithms. This course is extremely well suited for those who need to quickly understand the basics of machine learning. Thank you, Andrew Ng

par Linda R

•4 sept. 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

•24 août 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

•31 août 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

•16 août 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

•1 juil. 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.

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