Learner Reviews & Feedback for Structuring Machine Learning Projects by DeepLearning.AI
About the Course
Top reviews
YP
Jul 25, 2018
Very important and valuable intuitions about DNN training/optimization. It's full of really practical information while implementing my own models.DNN을 실제 적용할때 반드시 이해하고 적용해야 할 실질적 내용들로 구성된 멋진 코스 입니다!
SV
Feb 19, 2019
This is the knowledge in which we will get from lots of experience only, but the andrew has shared in this course which might help us in future by saving a lot of time through this course experience
4576 - 4600 of 5,743 Reviews for Structuring Machine Learning Projects
By 华卓隽
•May 11, 2019
666
By Diego F
•Sep 26, 2018
TOP
By Vikram M
•Sep 17, 2017
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By laixiaohang
•Aug 27, 2017
很实战
By Keshav B
•Jun 10, 2020
<3
By Radoslav N
•Oct 15, 2019
ok
By Ming G
•Aug 25, 2019
gj
By Pham X V
•Nov 6, 2018
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By Xiangning C
•Aug 19, 2017
好！
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•Jul 15, 2022
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By jkfx
•Dec 28, 2020
酷
By Valerii P
•Sep 17, 2020
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•Apr 19, 2020
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•Sep 30, 2019
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•Sep 28, 2019
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By Ishmael M
•Jul 15, 2019
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•Feb 20, 2019
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By Dayvid V
•Oct 31, 2018
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By Michele C
•Jul 25, 2018
v
By Huifang L
•May 7, 2018
V
By Yujie C
•Feb 1, 2018
好
By Bapiraju J
•Oct 18, 2017
G
By StudyExchange
•Aug 21, 2017
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By Aleksei A K
•Jun 22, 2023
This is an excellent course for those who want to develop applications that use neural networks meaningfully. However, I did not find hints on solving the problem of what data to put on the input level.
For example, for a neural network that evaluates a chess position, there can be at least 4 different approaches to this: 64 numbers or codes that describe the content of each of the cells of the chessboard; 32 numbers describing the position of chess pieces (or maybe 64 again, if we describe the position of each piece by vertical and horizontal lines, and not by the single cell number; 10 64-bit sets that give the placement of the same type of pieces (5 types, each from a pawn to a king, taking into account 2 colors) on a chessboard (this is the representation used by the leading chess programs to maximize the speed of enumeration of possible lines of moves); finally, just a variable length standard FEN string, which gives the generally accepted description of a chess position (however, also line-by-line for each of the horizontals, i.e., consisting of 8 parts). Before doing this by trial and error, I would like to hear some kind of "philosophy" about this.
Also, at the end of this course, I would like to try to work with the code in Notebook, as it was in the previous ones.
By Ali K
•Mar 29, 2020
In this course, the instructor from his experience gained through several machine learning and deep learning projects explains how to prioritize tasks in a big machine learning projects. This course does not introduce the reader to CNN or RNN but rather makes the user aware of some ML/DL tips to make the most efficient use of time and resources. Some of the most important questions addressed in this course are: 1) Why a single evaluation metric is important and what are some of the widely used metrics? 2) What is human-level performance and is it a good estimate of Bayes error? 3) What is Orthogonalization in the context of ML tasks and why is it important? 4) How to measure avoidable bias, variance error, data mismatch etc? 5) How to address data mismatch error? What is transfer learning and how is it different from multi-tasking 6) Whether one should opt for traditional or end-to-end deep learning approach?