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Opiniones y comentarios de aprendices correspondientes a Redes neurales y aprendizaje profundo por parte de deeplearning.ai

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Acerca del Curso

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....

Principales reseñas

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.

MZ

12 de sep. de 2018

This course is really great.The lectures are really easy to understand and grasp.The assignment instructions are really helpful and one does not need to know python before hand to complete the course.

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576 - 600 de 10,000 revisiones para Redes neurales y aprendizaje profundo

por Mario G M

27 de abr. de 2019

The quality of the videos could be improved, but the quality of the explanations is excellent. I already knew many of the concepts introduced, but I really appreciate the detailed explanations by the instructor, and the tips acquired from his experience. The evaluation tests are OK, although a bit short to be honest. Practical knowledge is enforced by means of well constructed and very detailed exercises. All in all this is a great course for beginners, I strongly recommend it.

por Leigh L

11 de nov. de 2018

This is an excellent course. I have read tons of the tutorials of deep learning on the other sites. But only this course gives detailed explanation of all the steps. Of course with notebook style step-by-step programming, and Professor Ng's gracious lecture, one will find this course is definitely one of the best Deep Learning Courses available these days. I also very much like Professor Ng's practical suggestions for how to apply Deep Learning principles for real applications.

por Oriol G E

13 de oct. de 2018

It was great understanding how programs can learn to do 'simple' tasks for humans, the steps and models and how they perform comparatively.

In this specific example, on interpreting images it seems however bizarre that a program needs thousands of trials and hundreds of images to classify the image. This seems much more easy to learn for a person.

Thanks for the course, it is great to have reached a basic understanding of it!

It was tough to use/learn vectorization in python... :)

por Malena M

14 de dic. de 2017

Andrew Ng as usual is superb at teaching this course. Providing intuitive explanations, which for this topic is super helpful. The programming assignments are good but the implementation of the last NN assignment uses a slightly different model than what Andrew Ng uses in his slides, which makes things really confusing at first. It would help if the person implementing the code for testing adds a note saying how the nn model is different. That would save people countless hours.

por Shakleen I

16 de jul. de 2019

A very good course for beginners hoping to get into deep learning. Professor Andrew NG makes Deep Learning theorems easy to understand and gives easy to understand examples where the theorems apply. Moreover, the graded programming assignments and quizzes help to solidify understanding of the knowledge gathered through the video lectures. The forums are there for anyone who gets lost or confused. Highly recommended for anyone whose interested to get started with Deep Learning.

por Alejandro A

7 de mar. de 2018

After finishing first course from Andrew, I've found this one much simpler to understand, especially the back propagation; This might be because this course was solely oriented to neural networks (leaving behind linear regression or unlabeled learning algorithms), and that on the previous course I've already had to rationalize the back prop process.

Anyway, the explanations are much clearer on this course, the only thing I miss is the Errata section, tutorials and week's notes.

por Jordan

1 de abr. de 2022

I'm so grateful to Mr. Andrew Ng and all developers and staff in this course for providing such a great Deep Learning content. His teaching is so careful and easy to understand for the difficult topic. I also watched the videos of his interview with famous AI developer in the world and they were really helpful for me to get a better insight into AI, what is necessary and unnecessary components to be successful in this field. I'll keep following all the rest of the courses.

por James M

27 de dic. de 2020

very useful course, key slides for me were the matrix shapes in prop equation walkthrough and forward/back prop equation summary. For me, using real life input (the cat images) was key to build a mental model to check and process understanding. I also found explaining the completed final assignment to someone else was useful in checking my own understanding - ended up using the cache in the final assignment to print X, W and Z matrix shapes to talk through the vectorisation!

por Yella S N

29 de ago. de 2020

The course is clear and to the point. Even though the Neural Networks is a tough topic to understand, Andrew is very good at explicating it in simple terms. Assignments could be a lot more lengthy, what I meant was rather than just changing the simple lines of code. Design the entire function could make the assignment a bit hard and also make us understand the problems we will face while writing the code. And also how to write the entire Neural Networks algorithm on our own.

por Tamjid R

18 de feb. de 2020

This is an excellent introductory course for artificial neural networks. The programming assignments are very helpful for solidifying the knowledge gathered from the video. I love the fact that most of the code is already done as boilerplate code and the learner gets to implement just the part of code that requires his/her concept of deep/machine learning. This way learner can focus on building his expertise in deep learning without being an expert in programming beforehand.

por Siddhartha B

2 de nov. de 2019

This was again an excellent course on the basics of how to deal with building a L-Layer MLP or NN. Working in python and numpy in Jupyter really helped. Solving the mechanics of the problem, especially in regards to tricks of matrix, vector sizes, rank arrays and piece by piece model building methodology really helped. I am ever thankful to Coursera , Dr. Ng and the fantastic team. Just a suggestion: make the programming exercise a little harder (like the original ML course)

por AVADH P

14 de jul. de 2019

This course teaches you the deep learning and neural network algorithms from the scratch of mathematics. You can actually get the intuition of the algorithms and understand how it is working. Also, the assignments are very helpful to create understanding and practicing the lessons learned in the theory part. I am really impressed by Dr. Andrew Ng's way of teaching and explaining one of the most complex parts of Calculus and Mathematics involved in Deep Learning. Thank you !!

por An H

25 de dic. de 2017

Absolutely the best presentation of neural networks I've heard. The way Andrew Ng organizes the material, thoroughly giving intuitions and building up concepts from simple single case implementation to vectorized implementation gives you the confidence to tackle the course from start to end. The most annoying thing for me were the little bugs with the coding assignments that resulted in an assignment being marked as 0 despite running and yielding the same results as written.

por Matthew C

22 de sep. de 2017

Great lectures put together by Andrew and team. Also found the programming exercises to be useful and informative using the Jupyter notebooks.

Also, as someone working a full-time job, I really appreciate the balance between breadth and depth. The coursera team has made it very easy for me to pop open the courses for maybe an hour a day and continue where I left off at another time. The ease and simplicity are one of the key reasons I'm able to continue taking these courses.

por Pallab D

4 de sep. de 2020

A fabulous introduction to Deep Learning. What amount of thought that has gone into building this course is evident. The programming exercises were easy, but that's not a bad thing. I don't want to get demoralized by an exceptionally tough programming exercise right up-front. They were easy, but not trivial. The code blocks were exceptionally well thought out and the notebooks had all the info you needed. They were also directly related to the material taught in the videos.

por Amit J

13 de nov. de 2019

An EE (with experience in the field of ASIC and electronic systems design). Wanting to get hold of the field of Deep Learning due to its tremendous potential in about every field and a recent surge of activities in ASIC designs for Deep Learning.

I did Andrew's Machine learning course before this (and I feel that ML course is a must as prerequisite to this) and found this course very good in all aspects (material, quality of presentation, quizzes and programming assignments.

por Brian B

26 de jul. de 2020

Great class for learning the basics without delving into quite as much of the linear algebra and theory. There is still plenty of the fundamental information shared and used throughout the labs.

By the end of the course, I had a firm understanding of how forward and backward propagation worked, how to set up the appropriate calculations through numpy, and how to debug when things went wrong.

Very much recommended, especially as step one of the Deep Learning specialization.

por Jack S

1 de nov. de 2018

This course provides an excellent introduction to deep learning not skimping on the technical detail, while remaining succinct enough that the learner is able to follow fairly readily.

It does require at least a fundamental knowledge of Python, and a basic understanding of machine learning to fully engage with the content.

There hasn't been anything more enjoyable for me personally than to create from scratch (more or less) an artificial neural network and see it in action!

por Tamas K

14 de ago. de 2017

Excellent course focusing on the implementation details of multilayer neural networks. I find this course as a potential companion course for Geoffrey Hinton's Neural network lectures which provides you deep insights about various network types, and you can hear many stories about the early days of NNs from a cognitive scientist's point of view, but lacks of hands-on coding and practical exercises. The regular coding exercises in this course help a lot. Highly recommended.

por Sapna S

19 de jun. de 2020

Very nice course. Specially the way it has been developed is just what one needs to understand the functioning of neural networks.The notebooks are very helpful and descriptive and guides us to reach the correct code and understand the maths very easily. Couldn't have asked for more. This was my first course with Andrew Ng, and I really like the simplicity with which he explains the the various layers of the neural network. Simply amazing! Thanks for the wonderful course.

por Geoff L

22 de ago. de 2019

This course takes a step-by-step approach to build a solid foundation, then move on to elaborate on other essential aspects of Deep Neural Networks. The programming assignments are also very well prepared that started with implementing helper functions before constructing end-to-end neural networks. Optional exercise is also a fun way to apply what I have just learned to a topic of my own interests. Well worth the time and effort to learn from an inspiring master teacher.

por Maria P

9 de may. de 2020

Very clear explanations. Gives very good intuition of the math so that I could compute the derivations and convince myself of the math. The only objection I have for the course is that for the last programming assignment some functions were precoded but not revealed. You have to go into the supplementary files and look at what the function does. I think it would be best to add the functions to the assignment and preferably let the person taking the course write the code.

por Harshdeep S

18 de oct. de 2017

The course has helped me understand the nuances in Deep Learning and has provided a basic understanding of the topic.

The course was easy to understand and the assignments were structured in a way that would enable beginners to grasp the concepts easily.

Suggestion: I feel we can have a practice project (ungraded) that would bring together all the elements discussed in the course. This would be a self motivated project that could be showcased as a part of the coursework.

por Ololade S

3 de feb. de 2020

Thanks to the deeplearning.ai team for organizing this course!!!

Although I had zero experience using Python, I enjoyed this course. The videos were easy to follow and were great at breaking down concepts. I found myself going back to my saved notes on many occasions to refresh my knowledge or clarify my doubts.

The assignments, quizzes and lectures helped me surpass my goal - get the gist of neural networks. I now have the confidence to further my knowledge in this area.

por Abhinav P Y

10 de jul. de 2019

Prof. Andrew Ng has done a great job by explaining the mathematics behind the workings of a neural network, and the programming exercises using python have been very well organized. This course definitely serves as an amazing background to other advanced courses in deep learning. I have thoroughly enjoyed and learnt a significant amount throughout the course. This course might also serve as a great refresher to those who haven't been in touch with the mathematics behind