Retour à Réseau de neurones et deep learning

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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....

AA

1 sept. 2019

I highly appreciated the interviews at the end of some weeks. I am currently trying to transition from a research background in Systems/Computational Biology to work professionally in deep learning :)

AA

2 juil. 2020

Excellent course !!!\n\nThe flow is perfect and is very easy to understand and follow the course\n\nI loved the simplicity with which Andrew explained the concepts. Great contribution to the community

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par Miriam G

•18 mai 2018

Really just mathematical background knowledge. Nothing you would ever need, since there is keras. No own thinking during assignments neccessary, either.

par Aratz S

•27 févr. 2018

Easy course if you have coursed the ML course before. I would like to see more explanations in detail. Still some bugs in the assignments... why???

par Thomas M

•16 juil. 2018

Course starts with a lot of math without any context what all those computations and parameters are used for or what they have to do with N

par Loren Y

•5 févr. 2019

The assignments are not good. Too easy and too much handholding. Also lots of technical issues.

par David W

•16 oct. 2017

Great Presenter in Andrew Ng, on a topic of tremendous interest to very many.

However, unfortunately the grader seems to work only rarely in accepting submissions. Code that runs perfectly in the Notebook is repeatedly rejected by the Grader. Dozens of comments on these problems when the course opened two months ago. But still the problems have not been fixed!

And if you want to reset your Notebook for a fresh start , that may take hours or even days .

A pdf addressing exactly what one needs to do would be sensible. Instead one spends dozens of hours trawling round Forum discussions to guess what might actually work for the Grader. A most disappointing experience. Why is this considered in any way acceptable?

par Andrew H

•28 avr. 2019

Not enough explanation or support to complete the very vaguely worded assignments in anything like the specified timescales.

I respect the source of this course but as a teaching resource it is really very poor.

par Ali A

•28 août 2017

Terrible integration with Jupyter Python framework, end up losing 3 hours of work! Nobody responds from the courser team !

par Kenneth T

•4 juin 2019

Great course, definitely taught me the basics of Neural Networks and Deep Learning as it's supposed to. Assignments are quite engaging when you try to thoroughly solve them. Even with minimal mathematics, the course will handhold you the whole way. Definitely a great course for anyone with minimal programming to get into. For me, the most challenging part was understanding how Python syntax worked with numpy. If you are taking this course I recommend taking your time with implementing the projects, they can definitely give you an understanding behind the logic of neural networks by following the code. The instructor is quite nice and warm, sometimes a bit dry, but nonetheless, he seems very warm; wanting to teach the next generation of individuals to do ML/AI. The course does have a few downsides such as how buggy the iPython notebook can be. This is the programming environment you will be using. An the video quality isn't always the best with the audio, but overall the content was presented in a great way and prepared in a manner in which you learn one step at a time.

par Sandip G

•21 mars 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 Kenny C

•1 août 2020

One might dislike that the derivation of formulas is not talked about in this course, but I think it's the right decision for this course. I took the Coursera Math for Machine Learning Specialization before taking this course, and the derivation for the formulas took at least 4 weeks of background material about linear algebra and multivariable calculus. Thus, this course aims to give you a conceptual understanding of neural networks that will allow you to implement it on your own. While some might argue that the programming assignments are too easy, or that too many hints are given, I think they're necessary for guiding you in the correct direction during the assignments. If you take the time to read the prewritten code, you will be able to get the understanding you get from writing it fully from scratch and possibly taking hours to debug and to read NumPy documentation. Overall, a very solid course for those who want to build a neural network on their own.

par Michael C

•23 sept. 2017

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

par Hong X

•2 oct. 2019

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

par Chi W C

•13 sept. 2017

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

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

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

par Carl G

•6 mai 2018

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

par LIM W X

•13 janv. 2018

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

par Michael B

•18 sept. 2017

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

par William L K

•6 sept. 2017

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

par Laith M A R

•17 août 2018

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

par William M

•4 sept. 2017

I really enjoyed taking this course. I have taken one of Andrew's courses before, and they keep getting better. I have a background in development, and appreciated the use of python over octave. Andrew consistently strives to provide an intuitive feel for the topics he is presenting. The fact that he is able to provide a complex subject in a simple manner speaks to his mastery of the subject.

The course contained a great mix of theory and practical application of those theories. I'm looking forward to the next course.

par John G

•28 mars 2020

What an amazing course. To be fair, I had completed Dr. Ng's course "Machine Learning" before taking this particular course, so some of the concepts, I was already familiar with. This course, delved deeper into the mathematics of Neural Networks and followed it up with coding assignments in Python. This course has provided a strong foundation for me to continue to build my knowledge base. To anyone interested in Deep Learning, take this course!!!

par Malte B

•8 avr. 2019

Great course to get a practical understanding of (Deep) Neural Networks. I would recommend to take Andrew Ngs "Machine Learning" course (also available on Coursera) beforehand, because the latter is much more rigorous when it comes to matrices operations. Thus it is unfortunately possible to just fill in the provided code in this course but don't really understand what it does.

par Muhammad T

•10 mai 2020

This field of deep learning has always intrigued me and I wanted to study it. My university offered a course, but sadly I couldn't enroll in it. However, thankfully, I got access to this masterpiece and now I can say that by completing course 1, I am pretty confident about neural networks and how to construct one.

Great Couse, and Great Instructor. Would Definitely RECOMMEND!!!

par WALEED E

•16 déc. 2018

This course formed a concrete background in building multi-layers neural network from scratch. The best advantage of this course is I was able to immediately apply the knowledge I gained into real world problem like humanoid navigation towards known targets. Illustration is great in terms of mathematical explanation and coding in a step by step walk through.

par Abdessalem H

•3 déc. 2017

This is one of the courses I enjoyed the most. For someone who has little to no knowledge in calculus and programming, I found the course is well tailored for all kinds of background. The pace is not so fast and Andrew is making it so easy even for beginners to grasp the new jargon and formulae. Thank you Coursera. Thank you Andrew.

par sai d k

•18 juin 2019

The course gives you very deep intuitions about neural networks and glimpse of deep learning .NO special mathematics course is not required formal understanding of high school calculus is enough .The programming assignment are too good actually they multiply your understanding, you get a feeling of real world application .

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