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....

RG

6 de sep. de 2020

I have learned a lot from this detailed and well-structured course. Programing assignments were very sophisticatedly designed. It was challenging, fun, and most importantly it delivered what is aimed.

SS

26 de nov. de 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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por Anjan D

•1 de oct. de 2017

Excellent course with great assignments. I have learnt from the beginner level in DL. It also helps one to brush up the calculus and linear algebra knowledge very much.

por Kieran S

•22 de oct. de 2017

Extremely well structured course that gives you good intuition about how deep learning works by starting with simply examples and adding layers of complexity.

por James G

•9 de ene. de 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 Rajavel K

•24 de sep. de 2020

Andrew Ng has the best explanation for neural nets, when compared to so many online resources I have saw upto this time.

por Gurudutt N

•29 de nov. de 2019

Such a complex subject made look like so simple. Every concept is covered in detail. Thank you Andrew Ng.

por Benito C

•2 de sep. de 2017

Very hard work in designing the notebooks so the pupils's learning processing is maximized.

por Michelle

•20 de dic. de 2019

very clearly explained and can't find anything better, loved the intuition part the most.

por Aman K S

•10 de jul. de 2019

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

por Vaibhav K

•15 de jul. de 2021

This course is amazing and you don't need any prerequisite for this course 😇

por Suddhaswatta M

•26 de abr. de 2019

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

por Lakshya k

•20 de dic. de 2019

Lovely course and it will surely boost my career. Everyone should do this.

por Md. S R

•20 de dic. de 2019

An excellent course to start your journey on A.I. and Deep Learning.

por Wasim Z

•8 de jun. de 2020

Thank you so nice of you Andrew Ng, you are one of my best teachers

por Anastasiya L

•28 de ene. de 2019

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

por Suciu V t

•9 de mar. de 2021

If you are a beginner this is the best course you can find

por kunyuwang

•23 de oct. de 2020

nice course from the base of the deeplearning

por Chinmay H

•20 de dic. de 2019

Andrew Ng is an awesome instructor!

por 王玥

•15 de ago. de 2019

对我很有用，虽然有些还不是很懂，但是我会经常复习巩固，直到完全理解。

por Weiyi S

•15 de jun. de 2020

it's not bad however too easy

por JITENDAR K

•8 de jun. de 2020

best learning material

por Abhinandan A

•8 de jun. de 2020

Amazing course!!

por 华德禹

•23 de ago. de 2017

greate

por Ivan M

•24 de may. de 2020

The course is fantastic, but I did Andrew Ng's Machine Learning course before and I miss some things here.

First, this course is more direct and faster than the other one and there are some basic concepts that are not explained here, so I recommend doing Machine Learning before. Also, I miss the little questions inside each video (especially the ones that ask about ideas that are about to be explained and make you think a little more). They have been included in the test at the end of the week, which has now 10 questions instead of 5. I also miss the lectures after the videos, which helped with the hardest concepts. The whole Machine Learning course seemed more inspiring than this one. As a little detail, I preferred the sans-serif font in the Machine Learning course slides than the one used here.

The other thing I don't like are the Jupyter notebooks. I get the point and they should be a good tool to code and learn and to evaluate the exercises, but I prefer the pdfs and the downloadable programming files. In the Machine Learning course you had a lot of structured Matlab/Octave files in your computer that you could then reuse easily. Here you have a document mixing text and code and it is not clear where all the code files are or how to download them for later use, Also, I like to program in my own environment, with my preferred text editor (with autocompletion, colors combinations, keyboard shortcuts...). Here you must use a basic online editor that also is hard to navigate through using the keyboard, because the text parts are also editable and selectable and you must jump from one part to another to move yourself through the document. And you need to do so, because your screen has a size and the explanations and other functions are long and they are far away from the code when you start programming. It's a very awkward way of working.

The programming exercises are very guided and you must just fill little snippets of code, which is not hard to do. They must "cheat" you giving you half the info you need for a formula to make you think a little more or it would be too easy, but the whole structure of the program is done and, although everything is very detailed in the comments, the fact that you don't program all of it doesn't help you understand the key concepts explained in the slides.

You must know some Python to be comfortable when coding, because there is no explicit material about the language syntax (in the Machine Learning course there was a video with a quick tour about Matlab/Octave and more optional short videos to learn the basics in less than one hour),

Anyway, the course materials are great and updated, and the derivatives role in the learning process is here explained clearly (I didn't understand its importance in the other course). I love the interviews with the Heroes of Deep Learning, which give you an insight of how things are now and how they have been before, explained by the poeple who invented the functions and tools we use today.

Andrew Ng is a great teacher.

por Stephen K

•7 de nov. de 2019

Tying your shoelaces is easy...if you have two hands. Some reviewers say this course is easy too. But you will be confronted with multiplying matrices and some differentiation. More than anything, I found it difficult to keep track of the different matrices, and particularly their dimensions, which if you do this course you will see is vital. There's also a lot of notation to overcome. You will need to understand some python, particularly how to extract values from tuples or dictionaries, and being familiar with user-defined functions will also help. So, easy?

The course starts with a 0-level neural network and builds up to a deep neural network. It's a nice way to easy yourself into what is clearly a complicated subject. The downside (at least for me) was that each week I was hit by yet more new notation, and I felt that some of what I'd been taught in the previous week (and was clinging on to by my fingertips) was almost redundant. It made my head spin. Nonetheless, I persevered and passed the course.

So, I've gained an appreciation of approximately how a neural network works. I could not build a neural network from scratch without massive recourse to my notes and assignments, and plenty of time. Is this how people build neural networks, or are they using libraries to make the job much easier (Tensorflow, Keras, etc.?) Or, can I use the final assignment as a template and apply this to many problems? I don't know.

I thought the notes were quite poor. There is a mountain of writing on most slides at the end. I scribbled more notes to explain Andrew's notes, otherwise a week later it'll be clear as Aramaic. However, Andrew repeats and explains well what's happening. He has a calm and reassuring manner, which I really liked.

People have complained about assignments being too easy. Not for me. I thought they were a good way to reinforce the lectures, and provided a means to see how a neural network could be built in practice. The assignments are more like lectures with your participation than traditional assignments. This is a plus point, in my view.

Finally, I'm still blown away how just a 'simple' logistic regression with sigmoid activation function can predict cats from random images so well. I've done the course, but it's like magic!

por D. R

•1 de oct. de 2019

(09/2019)

Overall the courses in the specialization are great and provide great introduction to these topics, as well as practical experience. Many topics are explained clearly, with valuable field practitioners insight, and you are given quizzes and code-exercises that help deepen the understanding of how to implement the concepts in the videos. I would recommend to take them after the initial Andrew Ng ML course by Stanford, unless you have prior background in this topic.

There are a few shortbacks:

1 - the video editing is poor and sloppy. Its not too bad, but it’s sometimes can be a bit annoying.

2 - most of the exercises are too easy, and are almost copy-paste. I need to go over them and create variations of them in-order to strengthen my practical skills. Some exercises are quite challenging though (especially in course 4 and 5), and I need to go over them just to really nail them down, as things scale up quickly. Course 3 has no exercises as its more theoretical. Some exercises have bugs - so make sure to look at the discussion board for tips (the final exercise has a huge bug that was super annoying).

3 - there are no summary readings - you have to (re)watch the videos in order to check something, which is annoying. This is partially solved because the exercises themselves usually hold a lot of (textual) summary, with equations.

4 - the 3rd course was a bit less interesting in my opinion, but I did learn some stuff from it. So in the end it’s worth it.

5 - Slide graphics and Andrew handwriting could be improved.

6 - the online Coursera Jupyter notebook environment was a bit slow, and sometimes get stuck.

Again overall - highly recommended

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