Chevron Left
Retour à Réseau de neurones et deep learning

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

4.9
étoiles
97,151 évaluations
19,420 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

AK

May 14, 2020

One of the best courses I have taken so far. The instructor has been very clear and precise throughout the course. The homework section is also designed in such a way that it helps the student learn .

MZ

Sep 13, 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.

Filtrer par :

76 - 100 sur 10,000 Avis pour Réseau de neurones et deep learning

par Sreenivas 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 mostafa n

Mar 04, 2020

This course really helped me and gave me new skills by applying my first neural network in very cleared way from prof Andrew ng as usual. big thanks for everyone who worked on this course and helped us to increment our knowledge, i recommend this course to everyone.

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 Andy W

Jun 08, 2020

Good for those who just want an experience with the fundamental processes of NN. I would recommend spending more time reviewing if you are not trying to do an applied version of Neural Networks.

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 Rajavel K

Sep 24, 2020

Andrew Ng has the best explanation for neural nets, when compared to so many online resources I have saw upto this time.

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 Wasim Z

Jun 08, 2020

Thank you so nice of you Andrew Ng, you are one of my best teachers

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 Weiyi S

Jun 15, 2020

it's not bad however too easy

par JITENDAR K

Jun 08, 2020

best learning material

par Abhinandan A

Jun 08, 2020

Amazing course!!

par 华德禹

Aug 23, 2017

greate

par Ivan M

May 24, 2020

The course is fantastic, but I did Andrew Ng's Machine Learning course before and I miss some things here.

First, this course is more direct and faster than the other one and there are some basic concepts that are not explained here, so I recommend doing Machine Learning before. Also, I miss the little questions inside each video (especially the ones that ask about ideas that are about to be explained and make you think a little more). They have been included in the test at the end of the week, which has now 10 questions instead of 5. I also miss the lectures after the videos, which helped with the hardest concepts. The whole Machine Learning course seemed more inspiring than this one. As a little detail, I preferred the sans-serif font in the Machine Learning course slides than the one used here.

The other thing I don't like are the Jupyter notebooks. I get the point and they should be a good tool to code and learn and to evaluate the exercises, but I prefer the pdfs and the downloadable programming files. In the Machine Learning course you had a lot of structured Matlab/Octave files in your computer that you could then reuse easily. Here you have a document mixing text and code and it is not clear where all the code files are or how to download them for later use, Also, I like to program in my own environment, with my preferred text editor (with autocompletion, colors combinations, keyboard shortcuts...). Here you must use a basic online editor that also is hard to navigate through using the keyboard, because the text parts are also editable and selectable and you must jump from one part to another to move yourself through the document. And you need to do so, because your screen has a size and the explanations and other functions are long and they are far away from the code when you start programming. It's a very awkward way of working.

The programming exercises are very guided and you must just fill little snippets of code, which is not hard to do. They must "cheat" you giving you half the info you need for a formula to make you think a little more or it would be too easy, but the whole structure of the program is done and, although everything is very detailed in the comments, the fact that you don't program all of it doesn't help you understand the key concepts explained in the slides.

You must know some Python to be comfortable when coding, because there is no explicit material about the language syntax (in the Machine Learning course there was a video with a quick tour about Matlab/Octave and more optional short videos to learn the basics in less than one hour),

Anyway, the course materials are great and updated, and the derivatives role in the learning process is here explained clearly (I didn't understand its importance in the other course). I love the interviews with the Heroes of Deep Learning, which give you an insight of how things are now and how they have been before, explained by the poeple who invented the functions and tools we use today.

Andrew Ng is a great teacher.