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....

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 .

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.

筛选依据：

创建者 donglingwang

•Nov 16, 2017

After studying Lesson 1, I learned a lot and solved many problems I've been puzzled before. Andrew-NG's depth explanation and detailed writing move me deeply. Teacher's profound knowledge and responsible attitude is my learning example .The teacher can make the complex knowledge lively and interesting, but without losing its own contagion. After-class exercises design is also distinctive, providing great convenience for our beginners . After class, the active discussion and exchange provide a wide range of ideas and rich ways to me. Thank you, deep leaning team. we thank coursera for offering rich courses, thanks to Miss Wu's team for doing so excellent course.

创建者 Dmitry T

•May 03, 2018

Considering how clear and thorough lectures by Andrew Ng were and overall how hard things were made simple in this specialization I can't give it anything but 5 stars. Thank you very much for your hard job on it!

However, I would prefer a bit harder and more theoretical course, personally. This one was adapted for a very broad range of listeners, which is a good thing generally. But it is absolutely not challenging to pass it: for instance, the programming excersices are great notebooks, but they mostly are already solved for you and you only need to fill the right lines into the right places. Only the last course on sequential models probably was a bit harder.

创建者 Nishant K G

•Jun 04, 2019

Very well designed and thought through course - Highly recommended for those who want to learn neural networks from scratch even extending it to deep learning.

This course will empower you to understand, create, and tune a neural network. Clearly describes about Parameters, Hyper-parameters tuning, Forward Propagation, Activation Functions, Backward Propagation, Updating Parameters and Predicting Labels.

On a side note :: Before this course I was only aware about analogy of human brain's neurons and neural network and after this course I am able to understand that no one knows (even neuro scientists) that what a single brain neuron does.

HaPpY Learning Guys !

创建者 Jagdeep

•Sep 11, 2017

Good introduction to Neural Networks. Professor Ing does a great job of simplifying the ideas for folks like me who did Masters in Operations Research more than 2 decades ago. This course brought back the happiest memories of my graduate school days on how gradient descent works. The course also took away the mystery I felt about what I am familiar with i.e. optimization vs how the human mind works. I have not gotten a clue on how the human mind works. I have no idea on how the neurons in the brain fire. I just know that neurons form a giant network and I have always enjoyed network flow algorithms thanks to Professor Dijkstra. This is a really good course.

创建者 Juan S D

•Oct 27, 2019

Excellent introduction to neural networks and deep learning! The course is very well structured, coming from the basic concepts of neural networks, up to building a modular deep layered network. Andrew does an amazing job at concentrating in the underlying and most important principles of deep learning, without spending too much time into the nitty-gritty mathematical and technical aspects of the topic. The lab programming exercises are insanely well written, and the ML interviews at the end of each week gave me a lot of perspective into the field and motivation to keep learning. Thanks to the deeplearning.ai team, you made an amazing job with this course!

创建者 André M

•Oct 22, 2019

Fantastic course, even better than the ML course by Andrew Ng. I love the Jupyter notebooks and have found them such an improvement over the ML's (already good) approach with MatLab. I've learnt tons not just from the course content, but basically from dissecting in my own Jupyter notebook what is going on in each lecture and programming assignment.

This course/specialisation is worth every penny. The interviews with heroes of DL have been very interesting and add a lot of value too. I love that Andrew always asks them about career advice and found Ian Goodfellow's interview particularly inspiring. Thank you Andrew and to all the team making this possible!

创建者 Harley J

•Oct 14, 2017

This course is excellent for both total beginners and people with a little experience in deep learning. I've implemented a few DL networks before, setting hyperparameters based on best practices. However, in taking this course, I came to understand the reasons behind some of the best practices I've used in the past. Dr. Ng does a great job of training and scaffolding for each lesson, building on the previous materials and leading to the next lessons. I'm also glad that he included interviews with big names in Deep Learning, so that I could see what's going on in the cutting edge of DL research, as well as finding more resources for learning even more.

创建者 Ashish V

•Jul 02, 2020

I found that the course was perfect and gave me a very top level overview of the ML. As a computational scientist I have considerable experience in the linear algebra, I did find that some classes were overkill since they focussed more on dimensional analysis and getting matrix dimensions right, something that (I consider) should be a requirement for this course. However, I do understand that the course is not created only for me. I was really happy to receive a "big picture" understanding of the subject, the teaching was simple and patient. The coding exercises were perfect for a first course in this subject. I can't wait to explore this field further.

创建者 Sanjit k

•Jun 23, 2018

I had previously gone through the popular course on Machine learning by Andrew and that course was quite exhaustive for starters. In this course we learn about how to build deep networks through python programming language. My one complaint is that the programming exercises were easy compared to his previous course. I think starters also wont find the programming exercises very difficult.I found the python implementations very good. The way you build helper functions first and then go on to program higher Layer neural nets. Through this course you will learn not only the basics of deep learning but also how to structure your code in an efficient manner.

创建者 Marta B G

•May 23, 2019

Really a nice course to take. I´m deeply thanked to Andrew because of his large capacity to simplify complexity - he's really didactic. I loved the way he build concepts from the very simple to the most complex, so that one thinks -- got it!. I like the interplay Adnrew uses between building blocks conceptualization (practical) and algebra & analysis foundations beyond (theoretical background). The assignments are very practical to follow , though after the course one probably couldn´t code from scratch unless she has a large practice on Python, the course is enough to settle the main concepts and learn a good collection of nice tricks in Python.

创建者 Jay P G

•Dec 24, 2019

Well , this has to be the best course for intro to Neural Networks and Deep learning . This course dealt with the basics and mathematics behind Neural Networks and the coding part was well covered in the assignments . If you pay proper attention during the lecture and make notes (I wrote in notebook) , it will help you later while revising all the concepts .

And while doing the assignment be honest and if you're not able to get any answer , just think for some time , pay attention to the small mistake you may have done , revise the concepts and you'll definitely get the answer .

Thanks and Congrats Andrew and his team for making such a great course

创建者 John L

•Dec 24, 2017

Great foundations. I really like to learn from the bottom up and this class provides exactly that experience - build your own NN from scratch. While I do like using Jupyter notebooks for the class to avoid the need to configure a local dev environment, I also find the "write 2 lines of code" style a bit limiting. At times (especially on the final assignment) it felt like it was more an exercise in book-keeping than exercising my knowledge. But of course, for a robo-graded class I think it would be a lot to expect more free-form assignments.

This is a great first class on deep learning and I will highly recommend it to my colleagues at Microsoft.

创建者 Vincent D W

•Oct 21, 2019

I was implementing convnet using keras for my undergraduate thesis before, and confused with the terminology used (hyperparameter tuning, gradient descent, global minima, etc). Alas, i persevere and finished my thesis with explanations i found online (albeit with much-unanswered questions and uneasy feelings). I decided to take this course to really dig deep into how this so called "brain simulation" works and i'm glad i did. It's giving me the much-needed intuition into how neural network really works. I now understand the mechanism behind gradient descent, and even gained insight into what derivatives really is (it is just a rate of change!)

创建者 Balaji H

•Jan 06, 2018

The course was great. The videos provided very clear explanation and intuitions behind critical components of the Neural Network. The course built beautifully from a single neuron to a multi-layer multi-neuron model, making it clear step by step. The most helpful & interesting part of this course were the quiz and assignments. Assignments gave a great understanding on the implementation of neural network and how to build them in a very modular way. Building this way, will really help anyone define and experiment with different models easily. The sincerely appreciate the time invested by the authors to build this quality course. Thanks a lot.

创建者 Marc A

•Mar 11, 2019

This is a nice follow-up to Andrew Ng's Stanford ML course. This one digs deeper into neural networks specifically, so if that's what you're interested in, this is a great course to take.

Note that the Stanford course used Octave and this course uses Python and NumPy (in Jupyter notebooks), so this is also nice because it gets you accustomed to using technologies that are more similar to what real ML practitioners are using. This course does still have you implement things by hand with NumPy and does not delve into higher-level frameworks like TensorFlow. For that, you will have to wait for the next course in the Deep Learning Specialization.

创建者 Ivanovitch S

•Feb 08, 2020

This course gave me an excellent overview of Neural Network, from the metaphor idea to math and implementation in Python. At least for me, the best way to study was a mix of pencil & paper (test and prove all equations) and reproduce the codes in the Coursera platform and Google Colab. The practice assignments are very related to theory lessons (equations using the same notation) that help the understanding. Only one note about the issues in notebooks, the Numpy version adopted is not the most recent, thus it is necessary to change some little things in order to reproduce the practice assignments on Google Colab (but this is not a problem).

创建者 Giuseppe T

•Nov 03, 2019

This course is amazingly paced and also strikes a very good balance between required knowledge and depth of the topics covered. I cannot imagine how to improve this course except by asking for "more of the same". I had enough background in math and computer programming and I read already some articles and tutorials on Neural Networks. But only after this course I grasped the concept a little better. Andrew Ng is a very good educator: always ready to trade one pound of mathematical rigor for an ounce of intution. And I believe this is the only way to provide good contents here on Coursera. I strongly encourage everyone to take this course.

创建者 Gaudi

•Feb 26, 2020

Very practical approach, full of code examples. It teaches you how to implement the NN with multiple layers from scratch in incremental steps. From the easiest approach (with single layer) to multiple layers. The code uses mainly simple code structures (i.e. loops, dictionaries, lists, vectorized operations and functions), so you do not need knowledge in OOP. Although I think some concepts if explained in OOP framework would be easier to grasp. But this is my subjective opinion. The course material is very well explained. If you want to learn and understand the way neural networks from inside out this course is definitely worth taking.

创建者 sampson w

•Jul 31, 2018

I've tried other introductions to deep learning courses, and they seem to focus too much on math or too much on coding - assuming the student is coming from one discipline or the other. This course nicely addresses both the math behind the algorithms, and the code required to implement it, without delving too deeply into either and focusing on the core of DL. This course uses Python and the libraries commonly seen in Kaggle kernels, and includes interviews with some of the most prominent names in AI, making it very relevant in 2018. I took the machine learning course from the same instructor and enjoy the delivery and organization.

创建者 fheinrichs

•Sep 14, 2017

As always, Andrew Ng's explanations help to grasp the material quickly and effectively. The programming exercises are interesting, yet not too challenging.

The course is, however, a bit light on the theoretical side. So if you are a practitioner looking for "hands-on" experience to get started with deep learning, by all means, this is your course.

If on the other hand, you are looking to understand the theory behind some of the concepts (i.e., you are not to afraid of a bit of math and would like to, e.g., see the derivation of the backpropagation algorithm), this course alone might not satisfy you. But it's a good start nevertheless.

创建者 Maximiliano B

•Oct 06, 2019

This course is excellent and it is a great introduction to deep learning. Every week you learn new techniques and at the end of the course you are able to build a real deep learning application. If you have a solid math background you will gain a better intuition about the details of the algorithms. Finally, Professor Andrew Ng explains the content clearly and shares several best practices as well as useful advices that will make your learning experience very rich. I've loved the heroes of Deep Learning interviews and it is a great plus. I definitely recommend this course and I can’t wait to start the next one of the specialization.

创建者 N Z

•Jan 18, 2019

Amazing course! I have tried learning concepts of neural networks by creating a syllabus for myself which consisted of different resources over the net. However at some point or another I would always reach a big obstacle which would prove to be extremely difficult to surmount and I would always inevitably give up. This course is structured in such a way that respects the current level of the learner and guides the learner through all the concepts without it being impossibly difficult or too easy. This course is only the beginning and I would gladly continue pursuing the other courses to strengthen my deep learning foundations!

创建者 Sebastián J

•Jun 26, 2020

As a teacher myself, I am impressed by how well organized is the course and how well they designed the assignments. Think they are introducing new knowledge to laypeople and they do it very well. However, I would like to get to know more about why neural networks work? In the content, there is a lot of the basis but you do not get to know where the magic comes from? I also love the interviews with the heroes of machine learning. That is something that really takes this course out of a purely instrumental one. Thanks a lot. The course fulfills my purpose of getting to know deep learning and keep me motivated to keep learning.

创建者 Saurabh M

•Jun 30, 2020

Andrew presented the course material in a very structured and systematic manner. The material is definitely a bit heavy, but Andrew does a great job in motivating the solution strategies. The systematic breakup of the backprop system of equations is probably the toughest part of the course, but that too was well-guided and the intuition was explained very well. I had some basic understanding of neural nets coming into this course, but I learnt a lot -especially the implementation aspect. Overall -this icourse had a perfect blend of theory and implementation for me to feel like I can now implement my own Neural Nets!

创建者 David R T F

•Nov 01, 2017

Andrew does a fantastic job of making this material accessible. This course is a great introduction to deep learning and won't overwhelm you with the details of the underlying mathematics. If you understand some fairly basic linear algebra and know how to take derivatives you'll be fine. The lectures are incredibly clear, and this is one of the best Coursera classes I've taken. The only critique I have is that the homework could be a little bit more challenging - or (if that would undermine the introductory nature of the class) there could be additional optional problems that push students a little bit harder.