Back to Neural Networks and Deep Learning

4.9

stars

94,668 ratings

•

18,892 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....

BC

Dec 04, 2018

Extremely helpful review of the basics, rooted in mathematics, but not overly cumbersome. Very clear, and example coding exercises greatly improved my understanding of the importance of vectorization.

AT

Jun 30, 2020

I think that this course went a little bit too much into needy greedy details of the math behind deep neural networks, but overall I think that it is a great place to start a journey in deep learning!

Filter by:

By Karim W E A

•Aug 15, 2017

A lot of repeated material from Stanford's Introduction to Machine Learning, especially week 4. But of course, implementing all the assignments in Python, which is probably the most widely used language for ML and one of the most efficient ones as well; That was a big advantage over the material covered in Introduction to Machine Learning. Also, the material was explained in great detail and was tremendously organized. Would highly recommend the course to anyone who's looking into expanding their knowledge in Deep Learning. I can't wait to start Course 2 in the specialization!

By Fabian Z

•Aug 05, 2020

This is by far the best course I've had. It's detailed, intuitive, well-explained, and engaging. This course kept me having fun all the time. The brief questions in-between the videos are amazing. The videos aren't really that in-depth mathematically but the discussion forum provides those additional details for those who need it. I also really like the fact that you guys added the heroes of deep learning videos, they really are amazing people and I think it helps me to get to know the field better. Well, I'm out of words. To summarize, it's amazing and I really recommend it.

By Ricardo S

•Nov 24, 2017

I found this course to be a good introduction to neural networks and deep learning geared toward the uninitiated. For anyone with some experience however, the course can be rather easy, though it can serve as a review and it is fast enough to go through. I find it to be always good to start from basics, especially in the complex and always evolving field of machine learning, and this is an adequate starting point. I suggest that anyone taking this course with serious aims should seek to understand the mathematics introduced in it, though it is often mentioned as "not needed".

By Mark M

•Jun 20, 2019

The programming assignments in this course provide practical experience in building a deep learning neural network. The lectures are thorough and easy to understand, and they connect clearly to the quizzes and assignments. I'm grateful that Professor Ng and staff put this excellent resource together and make it accessible to all. I currently work in Cambodia, where I hope to introduce courses such as this to young people who have no educational opportunities. I highly recommend this course to all who wish to be aware of the incremental significance of AI in our time. Thanks!

By Aayush K S

•Apr 06, 2019

Really great course material. With minimal mathematics behind this, this course provided a great start to deep dive into deep learning. The video length and the quizzes and exercises were great. Also, since jupyter notebook was hosted by coursera itself, I didn't had to invest setting up infrastructure or downloading packages in my local system which was unlike AndrewNg's MachineLearning course which used Octave. This experience made completing the exercises more efficiently. helping me to utilize most of my time in solving it. Looking forward to complete the next courses.

By Matteo C

•Mar 08, 2018

A great course.

The topic is very compelling on its own, but the magic is all in the instructor. Andrew Ng is passionate and explains complex concepts by slowly building up to them. It was very important for me that he introduced the math and notation required, without assuming a lot of prior knowledge.

The programming assignments are worked on and submitted with Jupiter notebooks, which is great.

To make the most of this course, I would recommend doing the "Machine Learning" course from Andrew Ng, as it has a lot of relevant content and a good refresher on linear algebra.

By Casey

•Mar 08, 2018

Definitely recommended. I've taken various other deep-learning lessons and tutorials, but none of them gave me as much insight and practice as this course. I get the feeling that a lot of work went into the design of the course and even the homework problems.

A practical note for people considering the class: it'd be a good idea to review how matrix multiplication works before diving in, because that comes up again and again. There's a review in the course itself, but it doesn't come until week 4, and I found it necessary to analyze matrix dimensions as early as week 2.

By Abdur R K

•Dec 24, 2017

Amazing course! I didn't even know python when I begun properly (only C++,C and C# and octave/MATLAB) but all the required functions/commands were introduced in a way that I faced no issues whatsoever. Of course I did need to google a lot of syntax differences (like for loops and stuff), but the experience was very fluid and everything connects extremely well to Andrew's famous Stanford ML course. If you're somebody who has only taken that course and are wondering if you can take the Deep learning specialization without having to study python first, I would say GO FOR IT!

By Dimitar T

•Jul 24, 2020

Andrew Ng is an amazing tutor and this is a great introduction in the world of Deep Learning. This course is for anyone interested in the topic, however, in my opinion it is advisable to first go through his ML course as this one feels like a direct continuation that builds on top of it. That beind said, the lectures are really well structured and the assignments are fun. One minor downside is that the assignments tend to hold your hand through the process, so to really test your knowledge, you may want to implement the algorithms on your own, using different datasets.

By Самигуллин А

•Dec 23, 2017

Very good course that can build understanding of neural networks and machine learning key concepts in a straight way. It is also interesting for some people, who thinks that he is advanced in machine learning, like me, but have only conceptual understanding of neural nets and no coding practice (just some experience with visual matlab plugin for NN).

Thanks for professor Ng and his deeplearning.ai team for preparing this course and for Coursera team for hosting it and making available.

P.S. It is so cool course that I'm helping with translation of this course to Russian.

By Francois R

•Jun 30, 2018

The Super Excellent: How the course is built, with a lot of small block well placed on top of each others. The honest rendering (cutting over the hype) by Andrew Ng of DL and ML in general.The Excellent: The new notation and organization of the matrices (compared to Andrew Ng's previous Machine Learning course). The new explanation of backward propagation.The Good: The use of caches between Forward Prop. and Backward Prop., but also between the different functions. Note: The latter would benefit cleaner names and the usage of assert() on entry of the functions.Thanks,

By Victor D L M

•May 14, 2019

Great introduction to Deep Learning and Neural Networks! I took the Machine Learning course offered by Stanford University and Professor Ng. and did not quite (fully) grasp what a Neural Network was doing. However, with this course, my intuition and understanding about Neural Networks and their inner workings was greatly enhanced. In addition, the course offers the most recent and best practices seen in the industry (e.g. introduction of the tanh and Relu activation functions ). I would recommend this course to anyone interested in Deep Learning and its applications.

By Abdelhak M

•Aug 20, 2017

Hi Andrew,

It's just Awesome Andrew !.. it was a pleasure to achieve this course 8 years after I achieved your first course in 2011 (before coursera borns). Thanks to you and to all your team at stanford.

I can't wait for the next four courses :)

I was teaching the machine learning course to my students in the past 3 years and I plan to teach this current course to my students this year. They have the barrier of English language and I'm trying to do my best to explain the main ideas I understood from your course.

Abdelhak,

Professor at Mohammed V University

Rabat, Morocco

By Michio S

•Sep 18, 2020

Prof. Andrew Ng made it easy for the beginner to digest the systematic discipline of Deep Learning.

In addition, I cannot say enough thank you to all those teaching staffs and other peers who helped me better understand the course through Discussion Forum. In particular, I would like to mention that my smooth learning progress owed to Mr. Paul T Mielke. Thanks, Paul!

After the completion of this course, I would like to view Prof. Ng's course CS230 at Stanford via YouTube videos to enhance the understanding of what I learned from the Coursera course.

Thank you very much

By Peter A

•May 04, 2020

This course is a perfect introduction to neural networks. It builds on simple concepts to then put together larger processes that link and compound these concepts together. In all, the user begins to see how something like fitting a Logistic Regression model is not that different from some other learning models that are regarded as more complex, such as neural networks. Surely, as one continues, these things will diverge, but this course does a good job os using the knowledge users likely already have, in order to better introduce them to some more complex concepts.

By Hamed K

•May 15, 2020

I liked the course. Without any previous knowledge of neural networks or deep learning, now I can claim that I know the basics and the reasoning behind fundamental steps of deep learning. The slides that were downloadable also were a great point of this course, since it made it easier to take and add your own notes. The instructor also explained topics quite clearly. I also like the system of grading for programming assignments where were divided in different sections unlike some similar courses on Coursera. Totally recommended for people interested in this topic.

By S. M F

•Feb 20, 2020

The best thing about this course was that the course gets easier and easier! Prof. Andrew Ng, the community, and the arrangement of the assignments always got my back! I never felt like "I must skip this line as this is out of my scope". With a little bit of hard work, anyone can build any layer NN for image classification problem with 80%+ accuracy. If there is any scope for improvement, I'd say that the notebooks get disconnected frequently, which should be improved. Otherwise, this is the best course I've ever had! Thanks all who are involved with this course!

By JAGANNADHA L

•Aug 22, 2017

Amazingly well done course. The best thing I liked about is the attention to detail that Prof. Ng has paid. For example I always had tons of problems with the rank 1 matrix. The frustration levels used to be so high. However, being the consummate practitioner and teacher, he identified what kind of problems one encounters when one learns python and deep learning for the very first time. It was more like symphony. I tried other courses in other websites. But this easily is the best of it all. I strongly recommend it to everyone who wants to get into deep learning.

By Thorbjørn Ø B G

•Aug 22, 2017

This is an excellent starting point for learning about Neural Networks and Deep Learning.

Many technical derivations and details are left out but this is only a plus. These details are much better learned with a working knowledge of the basics/implementation of neural networks. Besides, it is clearly stated whenever such details are omitted. This course will not make you an immediate expert in coding nor neural networks but it is the best starting point out there for a broad audience. Regarding becoming an expert, always remember that Rome wasn't build in one day.

By deniz

•Jul 12, 2020

Andrew Ng literally hacked the teaching method of deep learning. I have also finished his 2011 zero to hero machine learning course. I can easily say that, Let if flow people. Take the journey with him.

You need to know Python syntax and semantics (types, functions, lists, tuples, Especially Dictionaries.). Otherwise you will be going on a adventure. Watch out for "cache" dragon.

I suggest you to learn partial derivatives.

And everything else you will need in Deep Learning will be given to you in a perfect order.

I love you Coursera Team <3 Have a great time.

By Mark P

•Nov 01, 2017

Great quick overview and introduction to neural networks and basic deep neural nets. Great intro for those without a lot of the required math background. I would have liked to see some more quizzes (even if optional) on the derivations of the gradients. That was a bit of black box and we were just given the equations. I also thought it was a bit odd to have examples-by-column rather than rows. Assuming this was done to simplify notation (less transposes) - but it's counter to almost every other presentation in machine learning and stats that use example by rows.

By Zhengyang L

•Jul 12, 2020

This course has provided me the most suitable level of math details. Many other books and tutorials tend to overlook the fact that learners are usually not experts in machine learning and statistics. An example is the explanation of why the cost function of logistic regression should be the form provided. When I first saw the formula in a book, I was confused and could not rationalize it myself, which troubled me a lot because I don't think I can implement an algorithm without knowing the cost function. This course is really good for beginners who cares "why".

By dsp

•Aug 23, 2018

Well motivated. Clearly structured. Generalizing from Logistic Regression over shallow Neural Network to Deep Neural Networks was easy to follow and reinforced the structure of the approach. I overall liked the presentation of the maths and assume that it is well suited for an audience of differing affinity to maths. For myself, I will have to do the calculations again on my own to get a real grip on them. [Writing db (=something that should grow with steeper b) for dL/db (=which shrinks with steeper b, given the same change in L) still feels wrong.] Thanks!

By Raimond L

•Aug 19, 2017

Nice basic course, gives a clear look at what is happening inside neural networks, all details are explained in quite clear and understandable form with practical tasks of implementing everything, so that you really know what is going on.

After that course you will have a knowledge of how to implement a simple neural network and it's learning algorithm from zero. Also you will get some knowledge about matrices operations, derivatives and python programming.

I do highly recommend this course for novices and for more skilled people. It was a positive experience.

By Nicholas M W

•Jan 02, 2018

Excellent presentation of the material. The homework assignments made this approachable by holding my hand as I learned "how to walk" with matrices and multilayer neural networks. I feel like there could have been one more "do everything yourself" assignment, where we had to build another L-layer neural network completely from scratch, but maybe that isn't the point of this course, since I expect I'll be using keras or something in "the real world". An optional quiz involving some of the derivations for some equations might have been a nice stretch, as well.

- AI for Everyone
- Introduction to TensorFlow
- Neural Networks and Deep Learning
- Algorithms, Part 1
- Algorithms, Part 2
- Machine Learning
- Machine Learning with Python
- Machine Learning Using Sas Viya
- R Programming
- Intro to Programming with Matlab
- Data Analysis with Python
- AWS Fundamentals: Going Cloud Native
- Google Cloud Platform Fundamentals
- Site Reliability Engineering
- Speak English Professionally
- The Science of Well Being
- Learning How to Learn
- Financial Markets
- Hypothesis Testing in Public Health
- Foundations of Everyday Leadership

- Deep Learning
- Python for Everybody
- Data Science
- Applied Data Science with Python
- Business Foundations
- Architecting with Google Cloud Platform
- Data Engineering on Google Cloud Platform
- Excel to MySQL
- Advanced Machine Learning
- Mathematics for Machine Learning
- Self-Driving Cars
- Blockchain Revolution for the Enterprise
- Business Analytics
- Excel Skills for Business
- Digital Marketing
- Statistical Analysis with R for Public Health
- Fundamentals of Immunology
- Anatomy
- Managing Innovation and Design Thinking
- Foundations of Positive Psychology