Volver a Redes neurales y aprendizaje profundo

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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 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.

AG

6 de mar. de 2019

I understand all those thing which you have discussed in this course and I also like the way first tell story of concet and assign assignment. Now I fall in love with neural network and deep learning.

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por critics

•14 de may. de 2018

This course is friendly to novice because Andrew is adept at making the originally complicated lessons easy to inteprete, and his clear pronounciation and moderate speed help students catch up his pace without extra effort even for non-native English Speaker.More importantly, we all known that Andrew is known as a prominent AI scholar around the world, and his intelligence is sparkling through the course, for example, the systematic course structure reveals his in-depth knowledge, as well as the practical advices on buliding a deep learning model shows his rich experience in actual implemention.

por Sikang B

•4 de dic. de 2017

Compare to the machine learning class years ago, this revamped NN and DL class took very modern approach and really take machine learning education to the next level by using new technologies, better programming models and last but not least, Python Notebook for education.

Assignments are helpfully guided, however the guidance felt a bit too excessive at times. Some text could be better delivered as hints rather than instructions.

This course is less demanding and is definitely perfect as an introduction course. The interviews are super relevant and highly engaging. Make sure you don't miss them.

por José A

•15 de sep. de 2017

It's only my first week in the course, and I'd say it's been good. It can be a little bit tedious to catch up with the terminology if you haven't seen any Data Mining or Machine Learning . Nothing that a good devotion of Google and YouTube-fu can't tackle.

Other than that, I have a very basic knowledge in the topic and I have had to do some good research about it. The 2nd week's lectures goes through each of the steps in building a Neural Network, including the explanation of a Gradient Descent, Logistic Regression, and derivatives.

I'll see if I can update the review after finishing the course.

por Paulo A F

•26 de ago. de 2017

Andrew Ng is the best! Congratulations to all the team involved in the course. It i at a very good level for everybody to join in. As an experienced programmer I though the programming assignments were on the easy side, but I guess they are at the right level for people coming from other areas. As for the maths, I think is a good idea to leave the deep stuff out of it and get people building the NN as a long as the maths behind it is solid, which it is in this course. People can delve deeper in other sources. I'm quite excited for the next 4 courses! All the best to the team and to the students!

por Mark D

•22 de mar. de 2018

I loved the programming assignments. The tasks are nice, the visualization of the neural networks' decision boundaries is very helpful, and the setup with the Jupyter notebooks is just awesome. In other courses it is often required to set up a programming environment first, which sometimes takes more time than the programming exercises themselves. In this course it was possible to dive into Python and Numpy immediately - without worrying about file paths, environment variables, compatibility issues and other nuisances. The lectures were also very good. All the concepts were explained very well.

por Rahul K

•27 de feb. de 2018

Beautifully structured course! Feels like a walk in the park if you've already completed the 'Holy Bible of ML', i.e., Andrew Ng's Machine Learning course on Coursera.

Very good programming guidelines, and a gentle introduction to anyone who isn't aware of the core concepts on Machine learning.

If you're wondering whether you should complete the Machine Learning course first, by all means, go ahead. However, I can guarantee that there will be no hardships faced even if you're a beginner in ML and want to dive head-first into Deep Learning.

After all, it's Andrew Ng who we're talking about here! :)

por Omair M

•25 de nov. de 2017

Prof. Andrew Ng explains all concepts from a very fundamental level and even nervous students will feel encouraged by his insistence on "don't worry about it" for derivations you don't understand. The assignments have a lot of hand-holding but I needed that to focus on other more important concepts instead of debugging python code which can be learned in a different course. Overall, I have learned how to build a deep neural network using a building-block approach and gained confidence regarding this domain which I had previously taken to be mysterious and cryptic and perhaps for the elite only.

por Somnath M

•17 de may. de 2020

I had always wanted, formulae on the research papers to make sense in real world applications. However, as a novice programmer I wasn't been able to put those formulas into code and had to always go through multiple links and videos to make it working which was really a bottleneck as I didn't knew where to start. This course is really comprehensive and well crafted to make one understand the very basics to build a Neural Network and use them any Deep Learning Requirements. If you have a intermediate Python understanding, than no other course can help you create your own Neural Net. Thank You!

por MANUEL A F C

•31 de dic. de 2019

El primer curso de la especialización no solo te presenta el aprendizaje profundo de forma teórica sino que se ve reforzada con los ejercicios elaborados en el lenguaje python. Terminando con una construcción guiada de una red neuronal de 4 capas y entendiendo cada paso pues son planteados adecuadamente en funciones definidas con anterioridad. Recomiendo ver cada video a detalle y tomar apuntes, así como, practicar uno mismo implentando las funciones y decifrar que es lo que realiza cada línea de código desde un inicio para no perderse después (recomendación: lean los foros). Excelente curso.

por sahil m

•20 de ago. de 2018

Andrew sir introduces the idea of neural networks using a single neuron(logistic regression) and slowly adding complexity — more neurons and layers. By the end of the 4 weeks(course 1), we are introduced to all the core ideas required to build a dense neural network such as cost/loss functions, learning iteratively using gradient descent and vectorized parallel python(numpy) implementations.

Andrew patiently explains the requisite math and programming concepts in a carefully planned order and a well regulated pace suitable for learners who could be rusty in math/coding. I love this course.

por Ye W

•28 de oct. de 2017

This course serves as a great intro. I saw many comments complaining that the course is a bit too easy. As a stats PhD student, I admit that the technical details in this initial course is trivial, but I feel that I learned a lot of useful things, e.g., vectorization, intuition, etc. In fact, the entire concept of deep neural net is very straightforward, i.e., nothing but a generalized linear model (GLM) from a statistical prospective. I feel what is important is the intuition behind it and how to implement it efficiently in practice. This course covers both aspects in great details, love it!

por GAUTHAM M N

•6 de oct. de 2020

It is a fantastic course for any one craving about the thrills of programming or math. Anyone without prior knowledge of programming can learn to like this course a lot. Knowing a bit basic math about matrices and calculus kinda makes it more fun, but no compulsorily needed. I am enrolled to all 5 courses in this specialization and will complete all of THEM! Andrew Ng is simply too good :)

P.s: It will get tricky a bit, because the 'what' of deep learning is tough to contemplate. Dont worry too much and just keep learning the 'how'. As time progresses you'll be able to understand it finally.

por Qiongxue S

•14 de dic. de 2018

This course helps me to understand what is neural network and how would we use the NN and deep learning method to solve the practical problem. This is real science. For the content I have to say that this is the best AI course I have ever had. The related theory and mathematic equations are all clearly explained. Besides I learned a lot from every assignment. The point to build NN is make sure we understand the theory first then the programming part will not be hard. But before learning the course I think we need to have basic knowledge about python. Excellent course! Thank you so much.

por Chris R

•9 de sep. de 2017

I have already completed Andrew Ng's Stanfor Machine Learning course on Coursera, but the neural network coverage was limited. This course helped me understand the underlying principles of deep learning more completely and I'll be taking all five to earn the specialization. The pace of this course seemed perfect for me having some knowledge of Python, linear algebra, and calculus. This course also helped to refresh older memories and learn new things about Python libraries like numpy. This is an excellent course and has left me very excited about possible applications for deep learning.

por Balwinder C K

•9 de nov. de 2020

Simply Awesome. I took another course from Andrew Ng (Machine Learning) 4-5 months back but didn't get much idea what's going on. Then i planned to take this deep learning course but before that i did quite to grasp the concepts. I must have watched the other course's video 50 times and must have done 50 small ML examples but that was beginning. This course was breeze as I knew this time the terminology, the concepts and specially what i wanted to learn from the course. It's always good to know underlying concepts as it give you power to debug the not so subtle scenarios.

Thank you Andrew

por Khizr M K

•18 de abr. de 2020

The course is very good and I found it really easy because I was familiar with python. There are two things which I want to suggest in your courses .

The first thing is you should also teach how to use python libraries for deep learning. This will teach students how to use library in different types of problems.

The second thing which I found was the course was bit easy and it should be made little bit difficult by removing certain hints such as formulae. This will force students to make notes seriously while listening your video lectures and implement formulae in their code on their own.

por Vu L

•20 de ago. de 2017

I took the ML matlab version that Prof Ng created, but could not make it because I could not understand the homework problems and the content of the course. Thus, I got out after the fifth week because I could not understand how to do the assignment. However, luckily, right after I got out, he opened this course. It was a relief that I was able to understand everything I did not understand from the previous one, and I was able to do the homework. Therefore, I would suggest this course to everyone who wants to learn AI. Thank you Prof Ng and your dedicated team for this tremendous effort!

por Karl-Andero M

•4 de oct. de 2020

I really liked the way the python exercises were structured (Jupiter notebook). To let the student only focus on course-specific code, and not implement the entire program from scratch. The only downside to everything I learnt is that I did not develop the intuition for the mathematical formulas of backpropagation. I tried and I watched the optional videos as well but it was quite difficult, because I haven't done calculus in a while. I will rewatch and try to understand that part better. Everything in the course was perfect, will be looking forward to learning the next ones. Thank you!

por Hugo M S P

•6 de dic. de 2017

I loved this course and I got much more inspired to pursuit my search in this area hoping that one day I can join this amazing community and get a job in this area!

Thank you very much for your great work: loved the good sequencing of the videos, with very simple and bright intuitions about the more complex math topics, and also enjoyed a lot the Interviews with the gurus/legends of AI and ML!

Congratulations to all that participated and made such a great effort to put up this course available in such a professional format and by such a filantropic price!

Keep on with this outstanding job!

por Yasoda S K

•23 de may. de 2020

I really enjoyed this course, as far as my knowledge is concerned no other Instructor make this course as understandable than Andrew. Being a person from different background initially I am scared about gaining intuition about the topics but Instructor explains everything in a lucid manner. I am very happy that all the programming assignments are guided in this course, a person with introductory knowledge in python can attempt and gain good grades. I recommend everyone who wants to take this course upon interest can take without hesitation irrespective of their area of study. Thank you

por George Z

•19 de may. de 2019

The Neural Networks and Deep Learning class from Andrew Ng, deeplearning.ai and Coursera is very well structured and taught. I learned a lot and I am glad I was able to use calculus and Python to better understand what is going on underneath the hood with forward propagation, cost, parameters, backward propagation, predictions and more. Andrew and his team are exceptional instructors. The Deep Learning hero sessions are very motivational and inspiring. I also enjoy Andrew's sessions from Stanford's CS230 online. Looking forward to my next adventure in this Deep Learning Specialization.

por Ivo G

•21 de ene. de 2021

This course is very complete and takes plenty of time completely covering linear algebra, calculus and programming. If perhaps a bit slow this certainly helps whenever there is something you might not understand at first. I have found the high quality programming assignments to portray exemplary structure in the code and a very accessible way to get some experience working with different machine learning models. The course is setup in such a way that it can be comfortably done alone (haven't really tried the forum). I feel well prepared to get to work on my own deep learning project!

por HEF

•24 de mar. de 2019

Before taking this course I have learned Machine Learning, which is another famous course in Coursera, also taught by Professor Ng. My feeling is that this course is not as intensive as that one, but still I learned so many new stuffs which are extremely useful in my own deep learning projects. Before taking this course, I had zero coding experience in Python and so was really nervous about the programming exercises. However, the exercises are very well organized that I think every one can handle easily. So what I want to say is, don't say you can't do it if you never give it a try.

por Ronak V

•15 de oct. de 2017

Not sure how other people would fare, but I felt like in order to have a deep understanding of what was actually going on, I needed to go study the calculus and linear algebra behind the material (which I had done previously). I know that probably turns a lot of people off and is why it's somewhat glossed over, but thought I'd just put it out there.

I will say that this course was super helpful with seeing how a theoretical understanding of DL translates into code. The coding exercises were 100/100. So thank you for that! :). Looking forward to the next courses in the specialization!

por Kemal A

•7 de dic. de 2020

I personally enjoyes this course very much. I think the videos are pretty straight-forward. I really like how every video offers a very brief, yet incredibly detailed recap of what was completed previously and what is about to be reviewed. I'd personally prefer more mathematics, but Andrew provides the equations and optional videos. This enabled me to derive all the equations manually and to compare my results with the ones provided from the course, whereas people less keen on maths were not bothered by this. I would recommend this course to anyone wanting to start neural networks.

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