22 de nov de 2017
I learned so many things in this module. I learned that how to do error analysys and different kind of the learning techniques. Thanks Professor Andrew Ng to provide such a valuable and updated stuff.
30 de mar de 2020
It is very nice to have a very experienced deep learning practitioner showing you the "magic" of making DNN works. That is usually passed from Professor to graduate student, but is available here now.
por Girish G•
3 de mai de 2020
This particular course details all the minute aspects needed to have a better model. All the concepts were explained clearly in the course. I felt this course to be a like a "icing on the cake" to basic Neural Network course.
por Gustavo E Z•
6 de mar de 2020
Once more Andrew is greaqt teachng and very clear in his explanations. This course let me learn how to improve the development of a Deep learning project aiming at the right parameters and algorithms to be worked on the road.
31 de ago de 2017
Very useful ! It is a common problem of getting lost in ML projects, although the guidance seems abstract at first glance, it proves to be invaluable when ever we are in the midst of struggling for better modeling performance
por Rashmi S•
24 de jan de 2022
An Excellent Course to go with, will let you clear all your basic problems over distributions of Dev/Test sets, will make you understand and learn on avoidable bias inputs and choosing your parameters accordingly. Great one!
por Gudivada R K•
21 de jun de 2020
Much needed course for those who are in their starting/middle stages of DL/ML projects. This course gonna play a vital role in their projects. The explanation from Andrew Ng was interesting with real-time scenarios examples.
por Gourav K•
22 de ago de 2019
Thank You, Professor Ng, for creating so much valuable learning. The values to those are added and we get ambitious and inspired being through the interviews you took with great Deep Learning and Machine Learning scientists!
por David C•
11 de jul de 2018
I came into this course with the bias that it would be the least applicable of the five in the series-- however, I really feel that the information conveyed was extremely important for practical application of deep learning.
por Salim L•
25 de mar de 2018
Really helpful project strategy for Deep Learning that can save many months of work. While this course is a bit repetitive at times, Andrew Ng's recommendations are hugely important and his simulation tests quite innovative.
por Marko N•
26 de ago de 2020
Pretty interesting ideas on how you can improve your deep learning system. It teaches you a number of strategies that help you identify the most promising things to try. Quizzes are especially interesting in this course.
por Sanket D•
25 de mai de 2020
In depth learning of most sought and required concepts and giving insight on how to structure a ML project from scratch practically. The quizzes are just wow! They give a very good insight of how ML projects are structured!
por Justin K•
27 de abr de 2020
Short course with no programming exercises, but full of good information that is immediately useful such as where your time will be best spent depending on situations you're likely to encounter in pretty much every project.
por Aloysius F•
20 de mar de 2020
Excellent, this really goes into the nuance of successfully executing a project. Setting up an initial system is not that difficult. Understanding the sources of error a systematically resolving requires judgment and graft.
por amin s•
26 de jul de 2019
This course is great. Recommend it to anyone working on Deep Learning projects. Saved me lots of time, and taught me how to systematically think about my problem and opened new windows to improve my network. Thanks, Andrew!
por Sean C•
15 de fev de 2018
This was a valuable stepping stone in applying Andrew Ng's other teachings to realistic scenarios. The "simulators" were actually a great representation of realistic machine learning project issues & potential resolutions.
por Kurt K•
27 de nov de 2017
A clear explanation of a difficult subject with an emphasis on being able to create and to understand your own neural networks.- Plus in this module how to allocate your resources so you can achieve a successful project.
por Asad A•
2 de set de 2019
Really good insights into the practical aspects of structuring projects. Large scale deep learning/ ML is as much about people management and strategic prioritization as it is about complex algorithms and big data handling
por Arvin S•
10 de mar de 2018
This is a very useful course since that you can get an impotant instruction to build your own project. You can reduce your time cost and iterate quickly to produce more value by using the knowladges taught by this course.
8 de jan de 2018
Good. However, understanding the importance of strategy, either additional scenario quiz (the simulation type quiz is good) or a programming assignment would reinforce the understanding (given short duration of the course)
por Heidi V B•
17 de mai de 2020
I loved the translation of all the different succesfactors to the daily practice and examples in the course. It gave me an general idea of what to look out for when identifying my own AI problems and defining a NN for it.
por Abhishek R•
15 de set de 2019
This was probably the most useful course of the entire specialization with real-world examples, tips, tricks and techniques on how to approach the problems in Machine Learning world as a whole and Deep Learning in general
por Francisco R•
28 de set de 2017
Even though it's a short course and it doesn't have programming assignments, which I love doing, it has though these case study, which are quite fun and educative, helping you to get started in a Machine Learning project.
por Andrés S•
24 de mai de 2020
I liked this course because I gave me an idea of real situations I could face working on Machine Learning, but I think a little code would've been helpful, for example, to better understand how to do a transfer knowledge
por Ladislav Š•
20 de out de 2019
This part of Deep learning specialization is similar to Machine Learning Yearning written by prof. Andrew Ng. I read the whole book and for me this was mostly a repetitive information - however, very useful and relevant.
por Shishir V•
23 de nov de 2020
a lot of value for the minimal time invested, and the case study approach was the main reason I would give it 5 stars. Some parts in the videos could be fleshed out more with more real world examples where it was vauge.
por Naresh K P•
25 de jul de 2020
This course helped me understand how to prioritize problems that we encounter in Machine Learning space. On the surface this might look simple, but I think this course will have a huge impact as I implement ML problems.