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In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning.
By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to your own applications.
The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI....

LV

6 avr. 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.

SS

26 nov. 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.

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par Patrick B

•16 janv. 2021

I took the Machine Learning course last year, and my only complaint was that the neural networks part was a bit confusing. (Once the bias unit had to be in the weights matrix, once it had to be removed in order to perform some calculations.) The formulas also didn't work all the time, and you needed to figure out the proper shapes (using transpose operations) on your own.

In this course, the notation and explanation is much clearer. Keeping the bias unit out of the weights matrix in the first place makes everything easier. (Of course, the video quality is also better.)

Thanks Andrew; you really listened to your audience and figured out how to explain those concepts more clearly!

par Ilya B

•18 janv. 2022

A very good entry-level course. Made me recall linear algebra and calculus but in a good sense :) Andrew does a great job in explaining the material and his "if you don't get it exactly - don't worry - there are plenty of deep learning practitioners who don't get it either and are still very successful" - is very reassuring. He is the type of the lecturer that explains to you the material in such a way that those who do not have enough background feel very comfortable as well as those who have enough background do not dose off either.

In general the course made me want to go on with the next courses in order to get more insights regarding what's going on inside deep learning.

par Maximilian v H

•13 févr. 2021

After the "Machine Learning" course from Professor Ng I saw this specialization and gave it a try. He manages to keep a great quality of content through out the entire course and explains everything in a great understandable way. The interviews he added to this course were especially great to listen to and hear how some of the pioneers of artificial intelligence see their own field and hearing about their stories/origins motivates one to really dive into the deeper topics and also develop interest in a specific field of AI. I really recommend this specialization to everyone who wants to dive deeper into AI and even gain new motivation on how to approach such a complex topic.

par Mateo P R

•25 févr. 2021

This is a really useful course to learn the basics of Neural Networks and the intuation behind it!

I have been working with Neural Networks for some years now. Recently I started preparing for job interviews in the field of Machine Learning, so I wanted to refresh my knowledge about the "theory" behind deep learning. Surprisingly, this course made me even learn some concepts and ideas that I had never considered before!

Everything is explained in a very intuitive way. Normally, in this field, we tend to work systematically without even considering what we are doing or why we do it this way, This course helped me easily understand Neural Networks in a way I will not forget.

par Abel G

•29 août 2017

Oh My God, my first Coursera course that i have finished to the end.. Supper happy and supper excited till I go to the next one. It is so engaging that even working on a temperature above 30 in no AC room did not slow me down. I also started this course while i was officially in vacation since I could not wait till i get back from vacation. Anyways, Very good content, easy to follow and the fact that I had to implement all the theory right away was just super. I learned not only the power of NNs but also my favorite programming language Python. Any one with a motivation and interest in DP should take this course because it gives the foundation in the best way possible.

par Ripon K S

•3 août 2019

This tutorial was so elaborated. And in each week Andrew Ng tried to recap important findings from previous lessons which were helpful. Sometimes it looks fuzzy to recognize if the instructor is referring some notation as raw or vector form. But mostly it was nicely designed. I love the way programming exercise was designed. It can provide the basis to build a neural net from scratch. Considering all levels of users, he gently represented all the complex term like derivative in a simple way. Maybe for the future suggestion, Besides handwriting, if those calculations of those function can be displayed in animated design, then it's possible to make it simplified enough.

par Gaetano S

•11 avr. 2020

Andrew is an exceptional teacher. Thanks to him, I clearly understood the structure of a neural network and the functioning of the whole network starting from the single neuron.The mathematics behind a neural network, which until recently seemed very difficult to me, is now very clear.

This course is even better than the one on Machine Learning of Andrew Ng because here you can directly use Python with the Numpy library and all the part of the exercises and practice is, in my opinion, much better structured and clearer than the other course. I recommend it to anyone with an interest in Artificial Intelligence. I can't wait to continue my Deep Learning Specialization.

par Ekaterina B

•10 janv. 2019

Andrew Ng is a fantastic intructor. I admire his teaching style. He pays so much attention to the fundamentals instead of rushing through the material, that I feel like I learned something that will actually stay with me. The homework codes are written beautifully. Introduction of broadcasting and vectorization was an eye opener - turns out I've been programming very inefficiently for years without knowing. This course on it's own is not enough for me to go and architect NNs on my own, but it definitely helps with general understanding of the process, I feel more confident now talking about it and reading papers. Will continue on to other courses in Specialization.

par ANGIRA S

•31 mars 2018

A must for anyone in deep learning research. This course aims to build the foundation of deep learning operations by not using the built-in functions but writing code yourself, which help tremendously later. It gives you the microscopic view of what calculations are carried at each neuron, layer, forward pass & backprop.

The interviews provide the right kind of motivation for aspiring researchers. They're like the cherry over the cake! The syllabus describes the course material but whats a plus in this course is Prof. Andrew Ng's tips when it comes to applying techniques and information about the latest (and probably near future) trends of the academia and industry.

par donglingwang

•16 nov. 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.

par Dmitry T

•3 mai 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.

par Kiran M

•6 août 2021

If one has already completed Andrew N.G.'s Machine Learning course that works on Octave & Matlab, then this course will be a piece of cake. However, the refresher here, is Python! And there are SO MANY things that course expects you to know - so much to learn! The Material is designed to NOT MAKE YOU UNCOMFORTABLE but if you really want to Learn Python, then you will have to take it as a challenge and learn pretty much everything that you see as unknown there.

Overall though, really excellent course material. Glad I picked up this course. And I think it is a good revision for one already versed with ML concepts that one can easily pick up this Specialization.

par Nishant G

•4 juin 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 !

par Jagdeep S

•10 sept. 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.

par Cole F

•21 mars 2022

An excellent introduction to neural networks! Andrew Ng is an engaging communicator, and the course offers programming assignments that give you an opportunity to apply what you've learned immediately. The programming assignments are, however, somewhat remedial. My only wish for the course was that the programming assignments were a little more extensive to really test your knowledge of the backpropagation algorithm, but I also appreciate that would lead to a much lower success rate for students, and is also something students can work on on their own time. Overall, this is a great introductory course with good fundamentals in the concepts of deeplearning.

par Juan S D

•27 oct. 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!

par André M

•22 oct. 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!

par Harley J

•14 oct. 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.

par Christian S

•19 févr. 2021

In general it could be more condensed. Instead of too many repetitions of the fundamentals I would have appreciated to get an overview in the first course on how CNNs, GANs and RNNs works roughly and when to use it. With this basic course. So I did not gain an overview in order to decide whether I need another course or if the basic deep networks are sufficient for my use case. I missed the part "what kinds of NNs are available on the market for what purpose".

In general the course was too simple, since I already know both linear algebra and Python very well. But this is of course no weakness of the course. I still learned a lot and it was worth doing it.

par Ashish V

•2 juil. 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.

par Sanjit k

•23 juin 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.

par Marta B

•23 mai 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.

par Jay P G

•24 déc. 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

par John L

•24 déc. 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.

par Vincent D W

•21 oct. 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!)

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