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Avis et commentaires pour d'étudiants pour Réseau de neurones et deep learning par deeplearning.ai

4.9
stars
67,346 évaluations
12,729 avis

À propos du cours

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....

Meilleurs avis

ZR

Jan 04, 2020

At first, I want to thank the course teacher and all the others for providing us such a wonderful course. The way the professor teaches is really very very helpful. Thank you all again and keep it up.

MH

Jun 30, 2018

Very good course to start Deep learning. But you need to have the basic idea first. I would suggest to do the Stanford Andrew Ng Machine Learning course first and then take this specialization courses

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51 - 75 sur 10,000 Avis pour Réseau de neurones et deep learning

par KOTHAPALLI V A S S

Jun 19, 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 .

par Sreenivas N M

Dec 17, 2019

Excellent course to start learning about the basics of deep learning. Not just a simple copy paste cat vs dog classification course. But rather, a proper mathematical understanding of logistic regression, how it can be used as a single layer network to building one hidden layer network to multi layer hidden neural networks.

par Nikhil S

Jan 16, 2020

Neural Networks and deep learning is absolutely a great course for beginners. Those who have interest in this field can go for this course. It will clear all your doubts and you will enjoy this course. It was absolutely helpful for me . It helped me in gaining new skills and expand my knowledge.

par SAGAR B

Sep 10, 2017

A great course to understand basic concepts of Deep Learning. If you are a beginner in Deep Learning and thinking if you should invest your time and money here, don't give a second thought and join right away. Andrew Ng never disappoints!

par Mihai C

Jul 15, 2019

Very well structured, the code is much better than in the Machine Learning course that was initially posted on Coursera, and the use of Python instead of Matlab makes things much easier and friendly for everyone. I really enjoyed it.

par Anjan D

Oct 01, 2017

Excellent course with great assignments. I have learnt from the beginner level in DL. It also helps one to brush up the calculus and linear algebra knowledge very much.

par Kieran S

Oct 22, 2017

Extremely well structured course that gives you good intuition about how deep learning works by starting with simply examples and adding layers of complexity.

par James G

Jan 09, 2019

Great content and pace was more than manageable.

(Unrelated but worth mentioning is that I have found Coursera the platform to be incredibly buggy)

par Gurudutt N

Nov 29, 2019

Such a complex subject made look like so simple. Every concept is covered in detail. Thank you Andrew Ng.

par Benito C

Sep 02, 2017

Very hard work in designing the notebooks so the pupils's learning processing is maximized.

par Michelle

Dec 20, 2019

very clearly explained and can't find anything better, loved the intuition part the most.

par Aman K S

Jul 10, 2019

The most comprehensive and illustrative Machine learning course I could get through.

par Suddhaswatta M

Apr 26, 2019

Converting Mathematical equation to Python code are very well explained !!!

par Lakshya K

Dec 20, 2019

Lovely course and it will surely boost my career. Everyone should do this.

par Md. S R

Dec 20, 2019

An excellent course to start your journey on A.I. and Deep Learning.

par Anastasiya L

Jan 28, 2019

Easy to follow class, breaks everything down to small simple steps.

par Chinmay H

Dec 20, 2019

Andrew Ng is an awesome instructor!

par 华德禹

Aug 23, 2017

greate

par Stephen K

Nov 07, 2019

Tying your shoelaces is easy...if you have two hands. Some reviewers say this course is easy too. But you will be confronted with multiplying matrices and some differentiation. More than anything, I found it difficult to keep track of the different matrices, and particularly their dimensions, which if you do this course you will see is vital. There's also a lot of notation to overcome. You will need to understand some python, particularly how to extract values from tuples or dictionaries, and being familiar with user-defined functions will also help. So, easy?

The course starts with a 0-level neural network and builds up to a deep neural network. It's a nice way to easy yourself into what is clearly a complicated subject. The downside (at least for me) was that each week I was hit by yet more new notation, and I felt that some of what I'd been taught in the previous week (and was clinging on to by my fingertips) was almost redundant. It made my head spin. Nonetheless, I persevered and passed the course.

So, I've gained an appreciation of approximately how a neural network works. I could not build a neural network from scratch without massive recourse to my notes and assignments, and plenty of time. Is this how people build neural networks, or are they using libraries to make the job much easier (Tensorflow, Keras, etc.?) Or, can I use the final assignment as a template and apply this to many problems? I don't know.

I thought the notes were quite poor. There is a mountain of writing on most slides at the end. I scribbled more notes to explain Andrew's notes, otherwise a week later it'll be clear as Aramaic. However, Andrew repeats and explains well what's happening. He has a calm and reassuring manner, which I really liked.

People have complained about assignments being too easy. Not for me. I thought they were a good way to reinforce the lectures, and provided a means to see how a neural network could be built in practice. The assignments are more like lectures with your participation than traditional assignments. This is a plus point, in my view.

Finally, I'm still blown away how just a 'simple' logistic regression with sigmoid activation function can predict cats from random images so well. I've done the course, but it's like magic!

par David R

Oct 01, 2019

(09/2019)

Overall the courses in the specialization are great and provide great introduction to these topics, as well as practical experience. Many topics are explained clearly, with valuable field practitioners insight, and you are given quizzes and code-exercises that help deepen the understanding of how to implement the concepts in the videos. I would recommend to take them after the initial Andrew Ng ML course by Stanford, unless you have prior background in this topic.

There are a few shortbacks:

1 - the video editing is poor and sloppy. Its not too bad, but it’s sometimes can be a bit annoying.

2 - most of the exercises are too easy, and are almost copy-paste. I need to go over them and create variations of them in-order to strengthen my practical skills. Some exercises are quite challenging though (especially in course 4 and 5), and I need to go over them just to really nail them down, as things scale up quickly. Course 3 has no exercises as its more theoretical. Some exercises have bugs - so make sure to look at the discussion board for tips (the final exercise has a huge bug that was super annoying).

3 - there are no summary readings - you have to (re)watch the videos in order to check something, which is annoying. This is partially solved because the exercises themselves usually hold a lot of (textual) summary, with equations.

4 - the 3rd course was a bit less interesting in my opinion, but I did learn some stuff from it. So in the end it’s worth it.

5 - Slide graphics and Andrew handwriting could be improved.

6 - the online Coursera Jupyter notebook environment was a bit slow, and sometimes get stuck.

Again overall - highly recommended

par fahad

Aug 25, 2019

This course was really clear my concepts of Deep Learning and how actually neural network works.

par Omar A

Jul 22, 2019

If you have taken this course after ML by Andrew, you will see exactly the same material covered in 1 week expanded in 4 Weeks except using Python instead of octave or Matlab.

If you have calculus background I expect you to get tedious from elementary approaches in the lectures to get rid of Math and calculus.

Programming exercises in this course are very easy and below the level of first excellent experience with ML course.

There is no easy way to get lectures slides, No reading sections in this course. Like this course made to make systematic approaches to get things done without actual care about understanding the theories and concepts.

The good news comes when you have no previous knowledge about NN and elementary python skills, then this course is an excellent way for you to start.

par Alessandro

Sep 09, 2017

The content is great and I learned a lot. Certainly there could be a lot more feedback by the instructor in the forum. My feeling is that the students are really left on their own. Good from one point of view (cause you really have no choice than crush your head on the problem for days until you understand or give up), bad from another (it takes a lot longer to clarify difficult points). Fortunately the forum is populated by very clever students that take the time to answer questions. As a beginner I learned the broad strokes and intuitions for NN in this course, but the details about certain formulas are still very obscure and I was hoping for a better explanation of those.

par Jérôme B

Nov 16, 2017

To me, this is a failed attempt at simplifying those concepts. After spending hours trying to figure it out, now I find the algorithm behind the Neural Network very simple, and I can easily explain it to someone. But in this course I had to figure out by myself what was the point of those hundreds of lines of maths. So, very interesting concepts, but the "transmitting style" wasn't for me.

par Ofer B

May 01, 2018

Very abstract, and the examples are not as concrete as they could be. I'd use better visuals to ensure that the concepts in each video are understood 100% visually.