Volver a Redes neurales y aprendizaje profundo

4.9

64,735 calificaciones

•

12,240 revisiones

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....

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 :)

May 31, 2019

I have learnt a lot of tricks with numpy and I believe I have a better understanding of what a NN does. Now it does not look like a black box anymore. I look forward to see what's in the next courses!

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por Brandon C

•Dec 04, 2018

Extremely helpful review of the basics, rooted in mathematics, but not overly cumbersome. Very clear, and example coding exercises greatly improved my understanding of the importance of vectorization.

por Mohammad G H

•Oct 01, 2018

Very basic level

por nikcojeanian

•Dec 02, 2017

Programming assignment is too simple

por Niloufar Y

•Jan 12, 2018

not satisfied

por Ashkan A e A

•Nov 13, 2018

Too easy

por Jonathan C

•Mar 24, 2019

The lectures and assignments are extremely shallow, unengaging and poorly edited and recorded. Andrew Ng is riding the waves of the popularity of his ML course. I regret every dollar and minute I wasted on this crap. DON'T ENROLL DO YOURSELF A FAVOR GO READ A BOOK!

por Nikhil D K

•May 12, 2019

This is a good review of the concepts. It helped even more once I finished the course and reflected on the material by working out the equations for back propagation by my own hand. Looking forward to the next course in the series.

por Anil L G

•Mar 07, 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.

por Jerry P

•Feb 03, 2019

Excellent course. Challenging, but doable. Andrew Ng is a great teacher. I learned about logistic regression, forward and backward propagation, code vectorization with numpy, activation functions, and many other topics.

por Harsh T

•Jan 28, 2019

The course is good and it helps to clear the basic concepts of Neural Networks,

And the interactive assignments are just Awesome

por Antonio C D

•Jan 19, 2019

A good mix of theory and practice. The learning curve was perfect for me, and the course schedule is right if you study the material and work through the assignments in your spare time. Assignments are very well structured, I feel that trying to create the same implementations by myself (i.e. without the guides in the assignments and intermediate tests / check) would have taken 10x long.

por Juan P

•Feb 12, 2018

I would love some pointers to additional references for each video. Also, the instructor keeps saying that the math behind backprop is hard. What about an optional video with that? Otherwise, awesome!

por Juan A O G

•Aug 30, 2018

TL;DR: It's a good course for people who are not familiar with neural nets. Otherwise, it feels kind of repetitive (I completed the course in 4 days)

Pros: Learn to implement efficient feedforward neural networks from scratch, by taking advantage of vectorized operations and caches; good understanding of how neural nets work and the reasons of their success; I loved how Dr. Andrew explained why we must initialize the weights to some small random numbers (I already knew neural nets before this course)

Cons: I expected to build neural nets in Tensorflow (after learning how to implement them from scratch); It'd have been good to include a gradient check (by computing the numerical gradient) to foolproof the backward pass; sometimes the explanations felt kind of repetitive (e.g. continuously going from one training example to the whole training batch). I would have just sticked to the batch learning after it was introduced

por Thomas M

•Jul 16, 2018

Course starts with a lot of math without any context what all those computations and parameters are used for or what they have to do with N

por Miriam G

•May 18, 2018

Really just mathematical background knowledge. Nothing you would ever need, since there is keras. No own thinking during assignments neccessary, either.

por Jorge E C

•Oct 16, 2017

This course is good to just learn the terms and the basic aspects on architecture of deep learning. There is hardly any big explanations on the mathematical foundations of the topic which are of extreme importance to understand it.

It is a course for someone that dos not know much about neural networks or mathematics.

Is unfortunate that lead researcher in the area is able to say that it is not necesary to understand what a derivative is to be able to understand deep learning and the algorithm to update the weights of the network. I guess only for a first time course that is true, but I was expecting more from this course.

por Loren Y

•Feb 06, 2019

The assignments are not good. Too easy and too much handholding. Also lots of technical issues.

por Younes A

•Dec 07, 2017

Wouldn't recommend because of the very low quality of the assignments, but I don't regret taking them because the content is great. Seriously the quality of deeplearning.ai courses is the lowest I have ever seen! Glitches in videos, wrong assignments (both notebooks and MCQs), and no valuable discussions on the forums. Too bad Prof Ng couldn't get a competent team to curate his content for him. For such an basic level of content, you will find many other courses that are far better.

por Anastasiya L

•Jan 28, 2019

Easy to follow class, breaks everything down to small simple steps.

por James G

•Jan 09, 2019

Great content and pace was more than manageable.

(Unrelated but worth mentioning is that I have found Coursera the platform to be incredibly buggy)

por WALEED E

•Dec 17, 2018

This course formed a concrete background in building multi-layers neural network from scratch. The best advantage of this course is I was able to immediately apply the knowledge I gained into real world problem like humanoid navigation towards known targets. Illustration is great in terms of mathematical explanation and coding in a step by step walk through.

por Suddhaswatta M

•Apr 26, 2019

Converting Mathematical equation to Python code are very well explained !!!

por Malte B

•Apr 08, 2019

Great course to get a practical understanding of (Deep) Neural Networks. I would recommend to take Andrew Ngs "Machine Learning" course (also available on Coursera) beforehand, because the latter is much more rigorous when it comes to matrices operations. Thus it is unfortunately possible to just fill in the provided code in this course but don't really understand what it does.

por KOTHAPALLI V A S S

•Jun 19, 2019

The course gives you very deep intuitions about neural networks and glimpse of deep learning .NO special mathematics course is not required formal understanding of high school calculus is enough .The programming assignment are too good actually they multiply your understanding, you get a feeling of real world application .

por Aman K S

•Jul 10, 2019

The most comprehensive and illustrative Machine learning course I could get through.

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