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学生对 DeepLearning.AI 提供的 Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization 的评价和反馈

4.9
63,493 个评分

课程概述

In the second course of the Deep Learning Specialization, you will open the deep learning black box to understand the processes that drive performance and generate good results systematically. By the end, you will learn the best practices to train and develop test sets and analyze bias/variance for building deep learning applications; be able to use standard neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking; implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence; and implement a neural network in TensorFlow. 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....

热门审阅

AA

Oct 22, 2017

Assignment in week 2 could not tell the difference between 'a-=b' and 'a=a-b' and marked the former as incorrect even though they are the same and gave the same output. Other than that, a great course

AM

Oct 8, 2019

I really enjoyed this course. Many details are given here that are crucial to gain experience and tips on things that looks easy at first sight but are important for a faster ML project implementation

筛选依据:

3001 - Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization 的 3025 个评论(共 7,283 个)

创建者 Farrukh

Oct 17, 2017

Complicated ideas presented very clearly! Would highly recommend

创建者 王贤

Oct 3, 2017

Ng的课一如既往的浅显易懂,而且十分有用,希望能学习更多有关tensorflow方面的内容,不知道后面几门课是否会经常使用该框架

创建者 aisling.he

Sep 23, 2017

it helps a lot for me to understand the Neuarl networks! thanks!

创建者 Yi W

Sep 12, 2017

Very practical tips and intuitive explanations for these tricks.

创建者 Keith B

Sep 6, 2017

hurray! more good stuff, and nice intro to tensorflow in week 3

创建者 臧雷

Sep 5, 2017

Practical knowledge about how to implement a NN that works well.

创建者 Maciej O

Sep 4, 2017

Excellent mixture - from theory to implementation best practices

创建者 陈啸(Shawn C

Aug 24, 2017

You will learn many useful techinics to help you improve you NN.

创建者 Zifeng K W

Aug 21, 2017

Clear and informative. Just like Course 1 of the Specialisation.

创建者 Julián U

Jan 6, 2021

Andrew is a great teacher, seeking forward to continue learning

创建者 Aditya R

Aug 14, 2020

this course gave me insights on how to tune the model properly.

创建者 Nikhil V

Jul 27, 2020

Best course I have attended ever with lot of learning outcomes.

创建者 Prathamesh D

May 4, 2020

Got A very good intuition of Hyperparameters and Regularization

创建者 Shivaji

May 1, 2020

Best way of teaching Optimization and regularization techniques

创建者 Faysal M

Apr 2, 2020

This course helps me a lot to improve my neural network skills.

创建者 junping z

Sep 29, 2019

very useful course and bring insight on how to train parameters

创建者 Xia H

Aug 5, 2019

Great explanation like always from Prof. Ng. Thank you so much!

创建者 Juha J

Jul 16, 2019

First it was easy but then I really had to start using my brain

创建者 Yuri G

Mar 8, 2019

Great course but why there is not downloadable PDF with slides?

创建者 Akash C

Oct 4, 2018

Very nice course with good material and programming assignments

创建者 neeraj c

Apr 12, 2018

Complex concepts made simple by building upon using easy steps.

创建者 Yu S

Feb 11, 2018

I hope instructors could fix the wrong notation in the lecture.

创建者 Abhishek

Dec 29, 2017

Nice course> i loved to be a part of it.Just needs lots of time

创建者 Li Z

Nov 25, 2017

I especially liked many "intuition"s on deep learning concepts.

创建者 Leo S

Oct 18, 2017

Necessary to understand how to use neural networks in practice.