Volver a Redes neurales y aprendizaje profundo

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114,865 calificaciones

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 de abr. de 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.

AH

29 de abr. de 2020

Amazing course, the lecturer breaks makes it very simple and quizzes, assignments were very helpful to ensure your understanding of the content. Hope for future learners you provide code model-answers

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por Harshit P

•29 de oct. de 2017

The main take away for me from this course is to learn how to systematically denote various quantities involved in deep-learning such that they can be recognized later without any confusion (e.g. dW is gradient of cost with respect to W and so on..) and to learn how to structure a code to implement any deep neural network. Also, from data analytics perspective, I learnt about the limited representational capabilities of simple models like logistic regression and why deep networks tend to work better than shallower models.

por Anish P

•23 de mar. de 2019

It's a very good beginner level course on the basics of deep learning. Back propagation has been explained very well. The intuition and derivations of mathematical formulae are not too deep but can definitely be researched in text books. The assignments involve a lot of hand holding which is fine. One can attempt the assignment all over again in their own Jupyter notebook but this time write the entire code from scratch (referring to the assignments only when needed). The assignments also teach the best coding practices.

por CLAUDIO A

•4 de jul. de 2019

The course is really well structured, Andrew's lectures are really very easy to understand and on top of that, he also goes over certain topics more than once so that reinforces your learning . The assignments and quizzes are very well organized so you should not have any issues or ambiguities when submitting them. I was interested in the Neural networks topic since being an "old school" grad in Computer Science , at the time this field was not even in the syllabus of the universities so this certainly filled the gap !

por Vikas

•24 de jun. de 2020

Loved the course. Big Big thanks to Andrew Ng for teaching the concepts of Neural Networks right from scratch with the great explanation and step by step deriving the equations and explaining each n every bit. I have taken other courses on Machine Learning and Neural Networks but no one has taught the concepts like this. You must take this course if you want to learn the concepts of Neural Networks. The python exercises are also very informative and helps you learning and building the whole neural network from scratch.

por Mr.zhao

•18 de mar. de 2019

Thanks for Coursera for make this online education, letting more people to get to learn thing they interested. Professor Andrew Ng make this course very easy to understand, although you have a poor knowledge about the math. Besides the assignment was much easier than I thought, what you need to finish is the some few core code, and the whole structure was finishe to guide to finishe the whole project, after several testing and reviewing, you would finish it by yourself and have a better understanding about this course.

por 杨建文

•10 de ene. de 2018

The course starts from the basic structure, which make it very easy to understand. But very good courses can also have some small shortcomings:1.Lectures slides is not provided 2.It aims at very large population, so those who want to do research may need to dig deeper themselves(I suggest learners focus not only on the code you are required write, but also the whole network) 3.The programming exercise is a little bit repetitive. But overall, this course is still very helpful and efficient for beginners, thanks Prof.Ng!

por Nathan D

•11 de ago. de 2020

Really great way to learn about neural networks for both beginners as well as intermediates. The programming exercise with partially per-written code is very helpful and helps save a lot of time in coding so that students can focus on the important parts of the exercise, something which many online courses do not do, A big thanks to Prof. Andrew Ng for incorporating the heros of deep learning as an optional part of the course which helps students get motivated and understand where deep learning processes can be used.

por Joao N

•20 de oct. de 2019

The theory was laid down nice and easily even when maths started to get involved. The theory also tied up quite well into the practical assignments. One think that could be improved is the quizzes at the end of each video. I quite enjoyed them on Week1 and they do not seem to be consistent throughout the remaining weeks. Even having quizzes where the answers might not have been mentioned in class but they can be easily found with a bit of research (as long as the reading is worth it) could be an interesting addition.

por ZIQI Z

•12 de ago. de 2018

I would like to rate this course with a mark of 4.5/5 (although I rated it with all the stars). Overall, the course setting and content are great. Andrew does tell everything intuitively! It would be a great course for anyone who has certain background knowledge about neural network and deep learning.

However, the only thing that I would probably suggest is that maybe we can make the programming assignment more challenging.

But anyway, this is a wonderful course! I am looking forward to stepping into the next course!

por Max

•31 de dic. de 2017

A very nice introduction to neural networks. The build-up form logistic regression to a deep network was executed very well, and allowed me to attain a good initial understanding of ANN's. My feedback would be to include a bit more optional video's/written materials on the derivation of all the formula's (especially vectorized back propagation). Having some calculus experience I managed to do the derivations myself, but I think it would be nice if the derivation is explained somewhere clearly in some sort of appendix

por Ashwin A

•29 de sep. de 2017

Amazing course. It was well paced and structured. The programming assignments were fun and intuitive. It would have been nice to have had a few more optional ungraded programming assignments though so we could try our luck with different kinds of problems.

I especially enjoyed Professor Ng's explanation of forward and backward propagation in computation graphs . It was very intuitive.

It would also be nice if the lectures could have links to some of the literature behind the algorithms and concepts discussed in them

por Ram S

•11 de sep. de 2017

Superb course. Not only is Professor Andrew Ng a colossal scholar, but he is a brilliant teacher and knows how to get complex deep learning concepts to anyone who has basic math (algebra and calculus) skills. He also brings out the insight and intuition into why deep learning works. And the course is so very well designed and the programming exercises so thoroughly and precisely crafted. I enjoyed every minute of doing this first course in the series and look forward to the remaining courses in the series. Cheers Ram

por Joe M

•7 de jun. de 2019

Great course, the material was clearly presented with alternating between high level and actual coding implementations. The interviews with practitioners were really insightful. More references to some of the background on things like linear algebra or other math topics would be great. Some tricky parts of the programming assignments, despite much of the code laid out for you. They definitely helped me -- an experienced coder who hasn't looked at that much math in a long time -- on some of the higher level concepts.

por 罗广地

•13 de abr. de 2019

Deeply sighed by Andrew Ng, learning this course is a great way to enjoy the process. Among them, the check-in benefits of programming settings can consolidate and enhance understanding of what you have learned. The program in week4, when I was not learning, I wanted to write a neural network that could configure the number of layers arbitrarily. Under the leadership of Ng God, the work is very comfortable. This program can also be ported in other projects in the future. outstanding. I really like the series. thank.

por Niall O

•7 de mar. de 2019

I loved this course. The course builds the conceptual understanding and maths to build a functioning Neural Network from scratch using just python and numpy. I would recommend people wishing to take this course first take Andrew Ngs Machine Learning course on coursera and pay particular attention to the first 3-4 lectures that build up your visual intuition for ML and Logistic Regression. Now that I know the basics I'm looking forward to completing the remaining courses on the specialisation to improve my knowledge.

por Christian B

•24 de ago. de 2018

This is really an excellent course. In particular the notebooks are very well done. I passed the course but have to admit that I still need to go back to be fully clear on the dimensions of the vectors and matrix as well as how all the helper functions we implemented play together. But this is what I was looking for. An example where you really get through the network development and understand step by step what is happening. Thank you Andrew and team. I am looking forward to the other courses of the specialization.

por Yash P

•2 de dic. de 2020

It's the easiest to understand course for deep learning by Andrew Ng. Deep Learning is my goal, and I wanted to get started with it from the most basic things. The instructor has done it very nicely that an absolute beginner could get started with DL, having some basic programming skills and high school math. I loved it and strongly recommend it to the high school students like me who want to learn Deep Learning. I am very thankful to Andrew Ng and deeplearning.ai for making it a lot easier than what it looks like.

por Roudy E

•5 de nov. de 2020

A very elaborate course. It is also very practical and hands-on with its programming assignments. You will learn al the theory behind neural networks and how they work and you'll get the chance to build your own from scratch (without using Keras + TF which hide everything behind the scenes). Also, all the proven math functions that will be used in the implementation is also supplied to you during the assignments so you don't have to be an expert in calculus in order to obtain the required equations and derivatives.

por Nitesh S

•1 de oct. de 2020

The course has been designed brilliantly with not just easy to understand lecture material(and hands-on Python based labs) but also very practical and informative interviews with some of the pioneers in Deep Learning domain. It's worth every minute I spent on it. As always, Prof Andrew and his teaching staff managing the discussion forums are very knowledgeable, well-read and (most importantly) eager to help others learn. Thanks Coursera for approving my financial aid so I could finish this extraordinary course! :)

por James T

•9 de jun. de 2020

Great course! Super clear and easy to follow lectures and assignments. Love that we learned a thorough mathematical basis for almost everything behind deep learning (other than some complicated derivatives). I would gladly recommend to anyone trying to learn the basics of deep learning. The programming assignments were also incredibly convenient (Jupyter notebooks in browser), though it might help to give students a quick intro on debugging in Python (I was already familiar with ipdb and used pdb in the notebooks).

por Vishal M

•12 de oct. de 2019

It's the perfect course to start with understanding of neural networks. The way the concepts are explained, multiple times starting from shallow level to a deep level and are converged at the right place is amazing. The quizzes and programming assignments are well structured. The course spans over 4 weeks but can be completed in couple of days. The programming assignments are hand-held with lots of documentation and hence reprogramming the assignment without the jupyter notebook is recommended post the completion.

por Agile B

•10 de oct. de 2018

The teaching of Andrew NG is very educational - he builds all the necessary information about calculus into the lectures step-by-step, and repeats the confusing notation syntax over and over. This gets almost seamlessly translated into the programming exercises. Only on rare occasions, the python code is not updated, e.g. it misses the ravel() transformations required for some variables. Overall, this course deserves a sixth star for super good integration between the theory videos and the programming assignments.

por Dietrich B

•28 de sep. de 2018

A very enjoyable and effective introduction into Deep Learning! The most important concepts are first introduced and immediately after practised to program your own simple Deep Learning Networks. Interviews with some of the most famous Deep Learning practitioners help to put the learned material into context. The only thing which I could imagine to make this course even better would be a written summary the student could print out to have the material available for later use and repetition. Highly recommended!

por Kiran R

•2 de jun. de 2019

Great foundational course. A minor feedback - the crux of the programming assignments are the way we should approach structuring the problem (including defining helper functions, etc.). Perhaps the assignment could be further broken down (as an optional exercise) where the student is made to come up with the design choices for the functions as well. This will help students gain a great understanding of the various blocks that go in building these models, which will be helpful when they do it for themselves later.

por Mohammadreza M

•26 de dic. de 2019

Thanks Andrew. I really enjoy this course. Although there are plenty of knowledgeable lecturers in Coursera, a few of them know the teaching skills like Andrew. I specially took your course since I had taken Andrew's ML course in 2013 as well, and I knew how patient he is and how well he can teach to anybody with different level of knowledge. Assignments were challenging but clear. The checkpoint helps a lot and make sure learners if they made a mistake, they would not lost. Merry Christmas and Happy New Year <3

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