AM
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.
TG
1 de dez de 2020
I learned so many things in this module. I learned that how to do error analysis and different kind of the learning techniques. Thanks Professor Andrew Ng to provide such a valuable and updated stuff.
por Diego F
•26 de set de 2018
TOP
por Vikram M
•17 de set de 2017
o
o
d
por laixiaohang
•27 de ago de 2017
很实战
por Keshav B
•10 de jun de 2020
<3
por Radoslav N
•15 de out de 2019
ok
por Ming G
•25 de ago de 2019
gj
por Pham X V
•6 de nov de 2018
:
)
por Shannon C
•19 de ago de 2017
好!
por Abdel R k a M
•15 de jul de 2022
O
por jkfx
•28 de dez de 2020
酷
por Valerii P
•17 de set de 2020
!
por Parth P
•19 de abr de 2020
-
por Uday B C
•30 de set de 2019
.
por sonal g
•28 de set de 2019
f
por Ishmael M
•15 de jul de 2019
V
por Caoliangjie
•20 de fev de 2019
T
por Dayvid V R d O
•31 de out de 2018
f
por Michele C
•25 de jul de 2018
v
por Huifang L
•7 de mai de 2018
V
por Yujie C
•1 de fev de 2018
好
por Jampana b
•18 de out de 2017
G
por StudyExchange
•21 de ago de 2017
V
por Ali K
•29 de mar de 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?
por jxtxzzw
•3 de abr de 2020
This course from setting machine learning strategies, setting goals, error analysis and data distribution, migration and multitasking learning, and depth of the end-to-end neural network training and so on about the strategy of machine learning, to strengthen the depth of the first two lessons we learn the basic knowledge have the very big help, deep understanding of the depth of these knowledge is very good for our study harder to learn knowledge, such as convolution neural network. The greatest help of this course is that it makes us understand how to solve problems encountered in the actual development process and what is the most reasonable solution through two case studies.
por Oleg P
•30 de nov de 2018
In this course, Andrew is giving very interesting practical insights into how to proceed in different project settings and how to speed up each iteration. Think of it as a stand-alone optimization algorithm for deep learning projects. What I'd further expect from this course are practical assignments, e.g., data acquisition and preprocessing patterns, data (image) augmentation, and transfer learning and multi-task learning (preferably building upon introduction to tensorflow in the previous course). As I already stated in the previous previews, optional assignments without grading would also do the work in motivating the students to do something on their own.