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....

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.

May 31, 2019

I have learnt a lot of tricks with numpy and I believe I have a better understanding of what a NN does. Now it does not look like a black box anymore. I look forward to see what's in the next courses!

篩選依據：

創建者 Mattias K

•Nov 04, 2017

Great intro to deep learning. Although it's a bit repetitive at times, especially coding bits - one is not really forced to understand the components at times but can instead just follow instructions and copy paste bits and pieces. Would for example have appreciated that more time was spent on explaining the details of derivation of backwards propagation especially within "deep domain". The intuition is clear, but either forcing the user to do (or giving a link to) a step by step derivation would have been useful and saved time. Thanks for a great course.

創建者 Ged R

•Sep 07, 2017

I completed the original ML course earlier this year which gave the fundamentals of the practice. What I got out of this course was a reinforcement of the practices and ways of collecting my thoughts. There was enough difference in the approach and especially in the back prop areas to help clarify the understanding from what was a bit of magic, to a clear and more structured set of calculations. The platform of using the notebook is very solid, and of course there is the usual outstanding support from the community with respect to answering questions

創建者 Christos M

•Aug 12, 2019

Andrew NG's approach is one of its kind. Previously, I had taken several courses with other reputable online providers, and also did a lot of reading in tandem. What amazed me about Andrew's approach was the fact that crucial concepts were explained in much detail, one-by-one; this helped me complete the overall puzzle and/or fill in any missing links. I'm not sure if I could've followed the course without any previous experience, but if you're familiar with Python, NumPy and basic ML concepts, then this course will help you understand DL a lot better.

創建者 Basil A

•Aug 22, 2017

I think this course is very accessible, and gives you enough know how to hit the ground running. My only caution is that there is a bit of hand-holding involved (because of the limited background they assume), and that if you want a more rigorous foundation, you'll have to supplement this course with other materials. This doesn't detract from the quality of the course though, rather, it's amazing how much you can do with Deep Learning without fully understanding all of the finer details, and this is a good place to springboard into more advanced study.

創建者 Eduard L

•Oct 31, 2018

After a full course of Machine Learning, of course, this one is rather weak. The feeling that all 4 weeks we are talking about the same thing. This is probably done for those who are not at all in the subject. I see this course as an introduction to the specialization. I hope the continuation will be stronger. It's great that practical work is done in Jupiter on Python. Program exercises are easy, but it takes a lot of time to figure them out if we don't know Python very well. This is not a plus or a minus, just a statement of fact. Thank you Andrew!

創建者 Bernard O

•Oct 21, 2018

This was an amazing course for me. I've always wanted to get to the bottom of deep learning fundamentals and this course did not disappoint. It walks me through the basics to the more deeper concepts in incremental steps without overwhelming me with too much derivatives (but just enough to carry the point across). Just the right mix of theory and practice. Highly recommended as a starting point for deep learning, or if you're like me, developing more intuition towards the practice that I am already doing. Fills in the gaps in my understanding nicely.

創建者 Steven K

•Jul 08, 2018

A very nice introduction to deep learning. Covers the basics and builds up slowly. There is some prerequisite knowledge of Python programming and calculus to have success with the course. Professor Ng's explanation of the topic is focused on practical applications, and builds on years of experience gained in academia and industry. The exercises are focused on mastering core concepts. The notation takes a little bit of time to get accustomed to, but you begin to understand why the notation is the way it is. Very good course; I definitely recommend it.

創建者 Romina

•Jan 08, 2018

A really good intuition and introduction to neural network and deep learning. What I enjoyed the most was the fact that we needed to implement the learning algorithm step by step through the guided programming assignments as opposed to calling an in built function in libraries ( such as tensorflow etc). I felt the programming exercises were quite very successful in an attempt to draw and maintain the learner focus on the algorithm itself as opposed to other programming aspects, which can be learnt elsewhere/improved elsewhere. Great course. Thank you

創建者 Ankur G

•Nov 13, 2017

This course makes you implement your own neural network without using Tensorflow or Torch. As a result, the student gets to learn what neural networks are implemented internally instead of only learning how to use a particular software package. The course is full of small, practical, and highly useful information such as why we use a cross-entropy loss instead of sum of squared errors loss and why do we need to initialize parameters using not-too-large random weights. This information is very useful in implementing NNs at work or for job interviews.

創建者 Wei L

•Aug 18, 2017

It's a very good course. It illustrates the idea of neural network and deep learning in an intuitive way. I think this time I fully understand the idea and details behind them. Also, the python programming is very friendly. I have used R for years but not so familiar with python. However, folloing the instructions I can do the coding very efficiently. I think i just spent less than 1 week on this course but get 100% score on it. So it's not so challening compared to Machine Learning and PGM. I think PGM is the most difficult one among these courses.

創建者 Nicholas K

•Nov 07, 2019

Overall, an excellent course! The material is taught very well. The programming assignments were enjoyable and fairly straightforward. The Jupyter programming notebooks were really cool and fun to work with.

The only criticism I have is that week 1 material was extremely easy, easily doable within a day. Week 2, on the other hand, was quite difficult. I think it was the most difficult week overall because it introduced a huge amount of new concepts and math. After I had a good understanding of week 2 material, the rest of the course was not so bad.

創建者 Rohan S

•Feb 24, 2019

This course is a masterpiece. Excellent for beginners and for those who want to refresh their memory. Andrew Ng's way of teaching neural networks with the simplicity of matrix multiplication deserves a standing ovation.

Course Content - 5/5; The material is extremely well structured.

Simplicity - 4/5; though the course requires basic calculus, it shouldn't be a problem

Assignments - 5/5; they were challenging, but it made sure that you grasp the concept completely.

Teaching - 5/5 - Excellent delivery by the master supplemented with easy explanations.

創建者 Christophe T

•Apr 07, 2018

[FR]

Excellent!

Très bonne introduction sur le Deep learning. L’instructeur nous explique les fonctions de base très clairement. C'est ensuite suivi d'une forme de TD ou l'on peut implémenter ces fonctions en python et s'en servir sur des cas concrets.

On ressort en ayant compris.

[ENG]

Excellent!

This is a very good introduction to deep learning. The instructor explains very clearly all the intuitions and the basic fonction of neural network. Then you'll have an assignement where you implement thoose function in python and use them on a real example.

創建者 Sarmad A

•Sep 26, 2018

Very well made. Andrew Ng taught all the core concepts of neural networks very well. Before taking this course, I've watched videos on workings of neural networks. Forward propagation and back propagation always seemed a bit hard to me but Andrew made these concepts very simplified and made me to understand them thoroughly. Extremely satisfied by this course, looking forward to course 2. I would recommend this course to anyone, no prior knowledge of machine learning is required. If you have any interest in this field, I would say just dive in.

創建者 harm l

•Aug 23, 2017

Great introduction in neural networks / deep learning. Using Python learning environment is easier than using R which causes me to spend lots of time in installing the right packages in the right versions. Drawback is that i don't have the programming environment ready after finishing this course. It leaves me with knowledge but i have to rebuild the models in a tool i can afford leaving me with lots of overhead things to learn and implement. Overall, good focus on the matter and it's a great surprise to have these results in such an easy way.

創建者 Ferenc F P

•Mar 08, 2018

Prof. Andrew Ng provides in this course a comprehensive step-by-step instruction to build up your own deep feed-forward neural network (DNN) with backpropagation using only the numpy (library for array manipulations). His approach is from bottom to top starting explaining very basic concepts as building blocks. After those bricks are ready you can easily build your own DNN. It is a great course for beginners wanting to understand how a DNN works. Notebook assignments are moderately hard for a beginner and easy for a programmer with practice.

創建者 Volodymyr B

•Jul 21, 2018

Great course! A lot of useful information; definitely worth it, even after taking the into course. I do have two problems:

1) I wish the programming assignments did not help you THAT much. The assignments pretty much tell you what to write. As a contrast, I think that the assignments in the intro course were much more challenging.

2) Although I was able to do the derivation myself, I wish there was optional videos to show the derivation of back-propagation, as I think it is a valuable piece of information for full comprehension of the process.

創建者 Milo C

•Sep 05, 2017

I have pass this class.

Except test case of L_model_backward is not match to the teacher, everything is very good.

For the learning strategy, I also have some suggestion for new learner.

If you don't has any experience about machine learning, then Machine Learning class in Coursera by Andrew Ng is good for basic background knowledge. It can help you to quickly understand in simple way. so you can quickly understand the course of Neural Networks and Deep Learning.

Thanks Andrew Ng make everything become simple and good to learn :) Thank you

創建者 BlueBird

•Jan 07, 2019

This Deep Learning course on coursera platform just meets my needs. The instructor of this course is Professor Andrew Ng, who has many years of experience in this field. His Instructional videos and textual materials can help me understand the essence of the theory of deep learning. In addition, after-class quizzes and programming assignments can also greatly increase our practical skills. Therefore I believe this Deep Learning course can help me to possess the basic ability to work in the field of artificial intelligence and deep learning.

創建者 Ryan S

•Dec 04, 2017

Very basic concepts are taught, but the material is presented clearly and relatively concisely. The concepts are very accessible and some depth on the mathematics and theory is provided, although not as much as you would get in a graduate level college class. The programming assignments are very good, balancing first-principles implementation with a focus on implementing the most important concepts rather than writing boiler-plate code. This is a good introduction for practitioners and is easily covered in much less time than that allotted.

創建者 Aman R

•Mar 18, 2018

Started this course 3 months back, but from past two weeks I sat for around 4 hours per day, to complete this course. The programming assignments may not seem difficult intitally, because Andrew provide the vectorised equations but what really boils down and deepens my understanding was how am I going to use it in my application. How I will build my own image classifier ? When I try to answer such questions then yes it was very very helpful to me. I am still in learning phase, a beginner, so yes course was difficult but it was manageable.

創建者 Jairo J P H

•Feb 01, 2020

El curso es muy bueno, particularmente estoy muy agradecido con COURSERA, por darme la oportunidad de hacer los cinco cursos de la Especialización en Deep Learning con ayuda economica y permitirme tener acceso a este tipo de capacitacion y certificacion. Muchas Gracias…!

The course is very good, particularly I am very grateful to COURSERA, for giving me the opportunity to do the five courses of the Deep Learning Specialization with financial aid and allowing me to have access to this type of training and certification. Thank you very much!

創建者 Sayed A B

•Jan 20, 2020

I've been interested in learning NN and ML for a long time and Coursera finally provided this opportunity for me to do it in a timely manner. The time was very limited for such a wide topic, however, I believe they deserve a 5-star for how they managed to benefit such a limited time in a very efficient way. Andrew Ng is one of the best teachers I've had. He's both very knowledgeable, explains the concepts in a simple language, and he's very humble at the same time! Looking forward to getting more courses with him and with Coursera ...

創建者 Andrew E

•Sep 10, 2017

Pros:

Pragmatic presentation of fundamental mechanics of feed forward networks. In particular I appreciated the clean tutorial of the ndarray vectorized implementations.

Cons:

The one feedback I would give is that the coding exercises had a lot of hand-holding. For a specific suggestion: some of the "asserts" used for checking correctness give away the answer. I suggest refactoring the checks to be private methods invoked in the notebook but implemented server-side. That way they can be inserted in the code without leaking the solution.

創建者 Ivan

•Mar 10, 2019

Amazing stuff. I've been looking for a good introduction to Neural Networks, looked through a lot of tutorials and blog posts (of which there are multitudes these days, since Deep Learning is all the rage now) which only confused me more, and finally decided to take on a full-blown course. Turns out, once somebody like Andrew Ng explains this stuff, it's no longer mysterious and convoluted.

Note, that it's better if you're at least familiar with matrices and vectors from calculus before taking this course since NN are all about it.