Back to Neural Networks and Deep Learning

4.9

58,414 ratings

•

11,096 reviews

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....

Jul 15, 2019

Dear Andrew! Thank you so very much for making me belive in myself as a machine learning engineer. Your lectures & excercises are like "shoulders of Giants" on which a good student can stand out high.

Apr 07, 2019

A bit easy (python wise) but maybe that's just a reflection of personal experience / practice. The contest is easy to digest (week to week) and the intuitions are well thought of in their explanation.

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By Juan A O G

•Aug 30, 2018

TL;DR: It's a good course for people who are not familiar with neural nets. Otherwise, it feels kind of repetitive (I completed the course in 4 days)

Pros: Learn to implement efficient feedforward neural networks from scratch, by taking advantage of vectorized operations and caches; good understanding of how neural nets work and the reasons of their success; I loved how Dr. Andrew explained why we must initialize the weights to some small random numbers (I already knew neural nets before this course)

Cons: I expected to build neural nets in Tensorflow (after learning how to implement them from scratch); It'd have been good to include a gradient check (by computing the numerical gradient) to foolproof the backward pass; sometimes the explanations felt kind of repetitive (e.g. continuously going from one training example to the whole training batch). I would have just sticked to the batch learning after it was introduced

By Loren Y

•Feb 06, 2019

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

By Thomas M

•Jul 16, 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

By Miriam G

•May 18, 2018

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

By Jorge E C

•Oct 16, 2017

This course is good to just learn the terms and the basic aspects on architecture of deep learning. There is hardly any big explanations on the mathematical foundations of the topic which are of extreme importance to understand it.

It is a course for someone that dos not know much about neural networks or mathematics.

Is unfortunate that lead researcher in the area is able to say that it is not necesary to understand what a derivative is to be able to understand deep learning and the algorithm to update the weights of the network. I guess only for a first time course that is true, but I was expecting more from this course.

By Younes A

•Dec 07, 2017

Wouldn't recommend because of the very low quality of the assignments, but I don't regret taking them because the content is great. Seriously the quality of deeplearning.ai courses is the lowest I have ever seen! Glitches in videos, wrong assignments (both notebooks and MCQs), and no valuable discussions on the forums. Too bad Prof Ng couldn't get a competent team to curate his content for him. For such an basic level of content, you will find many other courses that are far better.

By Anastasiya L

•Jan 28, 2019

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

By Harsh T

•Jan 28, 2019

The course is good and it helps to clear the basic concepts of Neural Networks,

And the interactive assignments are just Awesome

By Jerry P

•Feb 03, 2019

Excellent course. Challenging, but doable. Andrew Ng is a great teacher. I learned about logistic regression, forward and backward propagation, code vectorization with numpy, activation functions, and many other topics.

By Antonio C D

•Jan 19, 2019

A good mix of theory and practice. The learning curve was perfect for me, and the course schedule is right if you study the material and work through the assignments in your spare time. Assignments are very well structured, I feel that trying to create the same implementations by myself (i.e. without the guides in the assignments and intermediate tests / check) would have taken 10x long.

By 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)

By WALEED E

•Dec 17, 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.

By Suddhaswatta m

•Apr 26, 2019

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

By Malte B

•Apr 08, 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.

By 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 .

By Xingchi L

•Aug 27, 2017

This is a very good course for people who want to get started with neural networks. Andrew did a great job explaining the math behind the scenes. Assignments are well-designed too. Highly recommended.

By Sameer K

•Aug 30, 2018

Nothing can get better than this course from Professor Andrew Ng. A must for every Data science enthusiast. Gets you up to speed right from the fundamentals. Thanks a lot for Prof Andrew and his team.

By Aman K S

•Jul 10, 2019

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

By 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.

By Sundar S

•Nov 27, 2017

Fantastic introduction to deep NNs starting from the shallow case of logistic regression and generalizing across multiple layers. The material is very well structured and Dr. Ng is an amazing teacher.

By Juan P

•Feb 12, 2018

I would love some pointers to additional references for each video. Also, the instructor keeps saying that the math behind backprop is hard. What about an optional video with that? Otherwise, awesome!

By Serge G

•Jul 15, 2019

Dear Andrew! Thank you so very much for making me belive in myself as a machine learning engineer. Your lectures & excercises are like "shoulders of Giants" on which a good student can stand out high.

By 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.

By 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.

By Francis J

•Dec 29, 2017

too easy, suitable as an entry level class

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