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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....

Jan 18, 2020

Very structured approach to developing a neural network which I believe I can use as foundation for any project regardless its complexity. Thanks professor Andrew Ng and the team for their dedication.

Sep 02, 2019

I highly appreciated the interviews at the end of some weeks. I am currently trying to transition from a research background in Systems/Computational Biology to work professionally in deep learning :)

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von Gokula K R

•Oct 19, 2019

Well, the concepts were crystal clear. To be honest, I got them theoretically but when i began coding, I could see that I could not connect few pieces here and there as there was the template given and I just had to fill in the blanks with whatever is given at the beginning of the module. I would suggest to let the learners code few functions from scratch, so that we could know what parameters to input, and what to return in the end. Also I think suggesting learners to visit documentations of few important modules like numpy, pandas, matplotlib etc. and instruct the learners to import them by themselves than importing them straight away at the beginning. Hope this would inculcate the developer culture and practice to beginners

von Anton V

•May 31, 2018

This is a great introductory course to deep learning and neural networks in general. The lectures are brilliant and so are the assignments. Best experience I've had with an online course. This one actually makes you want to complete it. I had some Python experience and a very foggy idea of how neural networks work after watching some youtube videos, however this course gives some really nice foundation for future development in the area. The assignments are easy to follow and give you code to use with things to fill in based on your understanding. This is a good way to get you started, I can now use those ideas to play around with a personal project and learn more. Looking forward to the rest of the courses in the series.

von Diego V R

•Aug 26, 2018

After Prof. Ng's Machine Learning Course, this new course appears at our Coursera's dashboard. I, again, enjoyed listening to Andrew's lectures. My personal recommendation is to first tackle Stanford's ML course and then this one. However, if you can only pick one and you are doubting between Stanford's Machine Learning Course and this one, pick this one, since it covers neural networks with way more detail than the original one. However, take into account that other machine learning related topics do not appear here such as dimensionality reduction techniques like PCA. Amazing treasure to the Deep Learning beginners out there. Thank you Prof. Ng and every one else who made this possible (The whole deeplearning.ai team).

von Nicholas B

•Sep 03, 2017

Definitely one of the best online classes I've taken. Even having studied this material a little bit before hand, I learned a lot--mainly in ways of building an intuition of why certain functions are chosen, or work the way they do. In this respect, I think there are few better at getting to the core of teaching: simple is better.

I appreciate that the course is designed to widen the reach of deep learning, but for those perhaps either more mathematically inclined or just extra curious, I highly recommend Lazy Programmer's Data Science: Deep Learning in Python class on Udemy for only $10. Gives a little more mathematical rigor...and hey extra practice in coding up a basic NN all in one script doesn't hurt. Thumbs up Ng!

von Vivek R

•Aug 25, 2017

Excellent course for beginners who are ready to put in extra effort to understand the material. By extra effort I mean repeatedly viewing the lecture videos and persisting with the programming assignment until the techniques are clear. That said, I am personally slightly disappointed with this course. Having completed Andrew Ng's original 2012 Machine learning course, I don't think there is anything new here for users like me. However it served as a good refresher. The other complaint I have is that the programming assignments are too simple. It's basically paint by numbers. If you really want to understand the material, you should write the programs from scratch and use any of the data sets available on the internet.

von Abhimanyu A

•Mar 03, 2018

What a marvellous roller coster ride it was! Thank you so much Andrew Sir and the entire team for putting up these efforts to provide such high quality material accessible to everyone! Cheers to all of you! I wish one day I would be able to share my knowledge like this! I wish!

One thing that I would like to add/suggest that, please provide some reference links, book recommendations and other useful information in the end of the program for anyone who wants to do some more research on the roots of the algorithms. Doing this, it will continue the learning path for the student and would keep the fire burning in him/her.

Please let me know if you have any questions regarding this review.

Thank you once again. :)

You rock!

von José A V M

•Sep 26, 2017

Amazing!! I've took part of the Udacity Deep Learning Nanodegree, but the math there was just not enough to my taste, here the thing is different, I love all the notation and the math behind. Also there are more focus in build the model step-by-step. As recommendation, I would like to include an activity to sketch the functions of the deep learning layers. For example, if I would like to build from scratch a deep learning model, what will be the functions I will need (in the assignments I could deduce them, but I would have like to have an activity related to this). Also I would like to have a little more focus in visualizing a small neural network, and write the values from the matrix to the visual representation.

von Pradeep K P

•Nov 20, 2017

Excellent course by Andrew and team. I am a big fan of andrew teaching style (since I took his ML course), no fancy screens just basic slides with great contents and explanation. My personal fav part in this course is programming assignment, this part is made very thoughtfully I think there are always some clue in the instructions and comments which one can pick to develop code to solve assignments.

For anyone planning to take this course I would suggest to refresh maths mainly topics like calculus, Linear algrbra, though Andrew explains required maths with a great ease. but still good to have maths background.

I am excited and looking forward to explore upcoming courses in deep learning series.

All the best!

von Paolo A

•Mar 12, 2020

As Business Executive I was rather skeptic and a little worried starting this adventure "in deep" as I have not programmed for many years, but still have some notions of linear algebra for luck. My personal objective is to understand AI deep networks to propagate it inside my Firm and especially to improve quality of living, freedom, sustainability in society. I think I got the right direction!

I have been very impressed by Andrew's approach and style that make you comfortable learning these not simple matters. I wish I could have time to continue the Deep learning Specialization.

Thank you very much to Andrew , to the whole Faculty Team and to the brilliant colleagues attending this wonderful course.

Cheers:)

von Syed M I

•Aug 23, 2019

Mr Andrew will make you climb the mountain while holding your hand.

There were sections in which I found the subject getting a better of me, but at the end of those videos he would come up and say "Don't worry if you didn't get full sense of what's going on" or "This is one of the hardest mathematical portion in machine learning" or "Even after all these years i am sometimes not confident of my approach but the model works magically".

In the confusing sections, he almost writes down the code for you to copy-paste. He had pre-written most of the codes for us, but make us feel that we are the ones writing it, because the ultimate aim is to learn things and be confident enough to replicate the learned skills later.

von Mukesh K

•Jan 22, 2019

First of all, thanks for offering the course on the platform. Before starting the course I had a good knowledge of machine learning and have been thinking about exploring the field of Neural Networks and Deep Learning. I could not do it in my college but the course provided me the opportunity.

The course material is very concise. Professor Andrew Ng presented very complex concepts in very easy language. The Programming Assignments are very helpful. They test and enhance both your Python Programming Skills and python code Implementation skills. While doing the Programming Assignments I was not only learning the concepts but also enjoying them. The entire course as well as the assignments are very much engaging.

von Kevin

•Oct 06, 2017

Andrew you did it again! This is the best intro theory and implementation course on Neural Networks out there. It combines enough theory (optional Calculus/Linear Algebra) and full implementation. The discussion groups are great for hints when you get stuck. Thank you to all the assistants and TA's who put in so much time to this course!

Now, for anyone who is debating taking this, having a calculus and linear algebra background will definitely help you in this course for the theory, but it's not a necessity at all. Some prior Python experience is needed as you will need to understand how functions are being called, but that wouldn't take a lot of time to get caught up on, but would require additional effort.

von Justin T

•Oct 12, 2018

Fantastic course! As someone who has done several online tutorials that use frameworks like TensorFlow or Keras, even having implemented things like deep reinforcement learning agents and image classifiers with these frameworks, I've never really gone through any formal training on much of the lower-level concepts/mathematics of deep networks. But this course cemented a lot of the fundamentals about deep learning into my brain that I was missing before, organizing topics in a clear and concise sequence of videos/lessons that really helped me keep things organized in my own head. Doing the exercises in a "manual", framework-less way using just standard Numpy was an awesome and enlightening experience too!

von Balachandran S

•Oct 16, 2017

One of the best Deep Learning courses (probably 'THE' best) around. Been a fan of Andrew Ng's sessions after I took his Machine Learning course in Coursera.

The course is perfectly designed such that its complexity increases gradually every week. The instructor makes sure that the participants follow completely.

Some points about the course -

1) Assignments are made too easy (too much hand-holding). But maybe the others who are new to programming might appreciate it better.

2) Andrew laboriously reiterated the points that required understanding.

Sometimes, I felt the course topic to be highly repeatative but later after completing the assignments, I felt it was required to be so and it was totally worth it!

von Vishnupriya V

•May 17, 2019

As a beginner who is interested in Deep Learning, this course was very useful an informative. The explanations given by Prof. Andrew were to the point and precise. However, detailed explanations behind the mathematics(calculus) could've been given as optional videos. The quizzes and the programming exercises were also very challenging at a beginner level. They quizzed us not only on the equations but also on the concepts. The only drawback in this course that I faced was trying to submit my programming assignment through Jupyter notebooks. They can very annoying at times. Also, the discussion forms were very active with the mentors and fellow student who would quickly help by replying to your questions.

von Steve S

•Dec 03, 2017

This course is a very thorough introductory review of neural networks that doesn't require expert level knowledge in some of the underlying math like calculus, but nevertheless manages not to talk down to you. In fact, the straightforward way the material is presented inspired me to learn calculus on my own to back up the material. Regardless, it gives you (almost) everything you need to start coding neural networks on your own. Where I did have some trouble it was owing more to lack of experience with Python and the Jupyter environment. I also would have liked a little more visibility into the data we were inputting, although I think that may be covered more in classes further on in the specialization.

von Arnaud S

•Oct 29, 2017

I found this course absolutely excellent. The structure and approach are absolutely great, and I am very happy that you force students to understand the mathematical underpinnings of backpropagation instead of letting a DL framework do the heavy lifting for you. Engineers need to understand what they do deep down.

My only improvement suggestion would be to provide a more detailed explanation of why we do the matrix multiplication & transpose in the computation of dW and of dA[l-1]. It turns out that in the case of dA[l-1] the explanation goes to the heart of reverse-mode differentiation and how to avoid combinatorial explosion. Cfr's Colah's blog excellent paper on backpropagation for details.

von Simranjit S P

•Jan 19, 2020

I liked this course very much. I have done coding and trained models in Pytorch and didn't have strong grasp in the math's part i.e Gradient and derivates that is the why i have taken this course at the first place. Though the course doesn't contain everything but it has given me enough knowlegde to start with deep-learning.The quiz and programmning excersice are really good. I have to think enough at some part and have done mistakes many times but got my concepts cleared. And thanks to coursera team for approving my financical aid.And though review may be good or bad depending on the person but i have learnt what i want to learn and it is good enough rahter than youtube or online material.

von Mukund C

•Sep 13, 2019

Absolutely Fantastic. I thought the programming assignments were a little too easy, but that's probably because I am familiar with python programming. I must say that the structure of the code really helped me focus on the core algorithms and vectorization (using numpy methods), so, in retrospect, it is probably a good way to make the student focus on the core concepts. I wish, however, there were some (more) optional lectures on the math and some more detailed derivations and some "optional" practice problems on doing partial derivatives etc., just to cement some important concepts such as back propagation. Highly recommend this to students wanting to learn the basics of neural networks.

von Greg A

•Sep 06, 2017

Awesome course. I have fairly little previous math experience though I have been working on some calculus and LA immediately before/while taking this, and all the topics were easy enough to understand how they are supposed to work. Much recommend.

One small thing I think could have helped a bit is the practical examples do a little bit too much hand holding. It makes it a little hard to know if you are actually grasping the knowledge or just able to tell what to do based on what information has already been made available from the templates and such. Had to step outside of this and try to do some of it on my own to see which pieces weren't fully making sense. But still, awesome course!

von Yaseen L

•Sep 08, 2017

Great, just like the first Intro to Machine Learning course Professor Ng distributed. Same style with improvements made in course design. For example, notation is much more consistent this time around probably because it is a more focused course unlike the first one. I would say taking Intro to ML first would help as it is a perfect primer for this course. Also, I'm glad they've decided to use Python which is just much more general purpose than MatLab. I would also say a solid grasp of the language is needed as a lot of boiler-plate code is provided and understanding it could be difficult if not otherwise comfortable with Python. Looking forward to continuing the full specialization.

von Borut H

•Mar 16, 2019

Amazing course! The creators are very good teachers. Materials have the right mixture of motivation, real world examples, theory and practice. I also like Andrews presentation style - one can really feel that he truly cares about the students being given good information and getting encouraged to learn. The assignments were also very well made - everything works, the code is good and there is so much help in the context/comments (eg. someone could even finish the labs without understanding the subject) - but this basically allows each student to choose how much effort he/she wants to put into the subject (also meaning how much knowledge she/he wants to absorb during this course...)

von Debraj T

•Apr 29, 2018

I found this course very helpful in furthering my understanding and clearing a few doubts that I had from the Machine Learning course. I seem to understand back propagation much better now.

This course also helped me give a structure to the steps involved in actually building a Neural Network... gives me more confidence.

My only issue was with the programming exercises. I felt they were very tightly structured, maybe because of the automated grading system. It was almost impossible to go wrong. More flexible and open exercises, I think, will help in learning the real intricacies of building a NN from scratch. Don't really know enough to comment on how this change can be incorporated

von Chitra V

•Jan 09, 2019

The course is well structured and the programming exercises are so detailed, I am going to refer to them in future while implementing neural networks. The best part about the course is, Andrew Ng actually taught the math behind the network. Rather than taking his students through a library function for neural networks in python, he taught his students how to code from scratch while also covering nuances such as suitable activation functions for different cases and ideal values for weights. The documentation for programming exercises is very detailed and must have taken plenty of time for those who worked on it. Recommend it for anyone wanting to start. Kudos to the instructors!

von Ritesh A

•Jan 13, 2018

The programming assignments (things which the student had to fill in) seemed repetitive and very limited (e.g. mostly needed only mathematical formulas to be filled in using numpy). However, to keep the grading similar and also cater for less advanced users simultaneously, the assignments could be tiered by beginner-intermediate-advanced (by concealing more and more stuff) but still grade based on the current beginner level only. So, one could start with advanced and then reveal more to get to intermediate in case he is not able to solve etc .. May be optional bonus grades for solving it at advanced level etc.

Otherwise a good course overall to get intuition into deep learning.

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