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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
63,489 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

DD

Mar 28, 2020

I have done two courses under Andrew ng and I am grateful to Coursera for their highly optimised and easily learning course structure. It has greatly help me gain confidence in this field. Thank you.

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

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1251 - Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization 的 1275 个评论(共 7,283 个)

创建者 Indranil B

May 19, 2019

This course did not just only open my abilities to deep learning framework's mechanics but also made me realize the use of those hyperparameters.

创建者 Manpreet M

Jan 28, 2019

Good content and explanation. There are good practical suggestions given in the course. Also, TensorFlow programming is introduced which is nice!

创建者 Horacio P

Jun 10, 2018

Amazing explanations. Great tips to solve and improve DNN problems. Exercises also are useful as base for robust projects. Thanks Andrew an Team!

创建者 杜凌波

Mar 6, 2018

Very fantastic lesson! Have learned much things by study it. someway,I think homework is too easy to review the knowledge.

Thanks you, Andrew Ng !

创建者 Edgar V

Feb 11, 2018

With this course I not only understood more about DNN, but also learned to program better using Tensorflow in Jupyter notebooks. Thanks Andrew Ng

创建者 Eduardo M O

Oct 8, 2017

Amazing intuitions and insights how to tune and understand "hyperparameters world". Thanks again Andrew Ng and Coursera for this amazing journey!

创建者 Rami K

Aug 23, 2017

Excellent course, would have loved to learn more about Thiano in addition to TensoreFlow, but overall very happy with the content of this course.

创建者 ali k

Feb 2, 2025

It's a little bit hard course and needs to take much more time. even if you completed it, you need to review this course cause of the importance

创建者 jyotikrishna B

Sep 17, 2021

really impressed by the way of teaching by Andrew sir, and got to know how to optimize the the neural network and improve its accuracy and speed

创建者 shawn s

Jan 25, 2021

it's a great course to deep learn the machine learning, it will clearly share how to initialize the parameter. how to quickly coverage the model

创建者 Carlos V M R

Aug 31, 2020

Os conteúdos são excelentes, assim como a didática do professor. Perfeito para quem já possui uma experiência na área e quer se aprofundar mais.

创建者 Vidhan G

Jul 14, 2020

It was well formed and the instructor had a vast knowledge about the subject , would highly recommend this course to deep learning enthusiasts..

创建者 Shashank D

Jul 5, 2020

Whilst the topics were covered with good perspective, as usual by Prof Andrew NG, more sessions on Tensorflow could have made the course better!

创建者 Hari D S A

May 13, 2020

FABULOUS!! This course helps build deep neural network with higher accuracy and reduced training time. On top of it you get to learn Tensorflow.

创建者 Federico R

May 11, 2020

This is a beautiful course. Andrew is exceptionally good at explaining interesting (but complex) topics in a very intuitive, straightforward way

创建者 Yash A

Apr 16, 2020

Really good of learning TensorFlow from basic Linear Function of a small scale Network. Finally got to know the workings of different Optimizers

创建者 Yashveer S

Mar 14, 2020

This was an excellent course for learning about hyper-parameter tuning. I really enjoyed this course and look forward to the rest of the courses

创建者 Victor g

Oct 23, 2019

Great. Thanks to this course I have gone a bit deeper and understood concepts that I now realize I did not understand them as well as I thought.

创建者 Sonal K

Aug 2, 2018

This is really an awesome course. This covers the basics so well and enables to have a clear picture of what is going on. Highly recommended..!!

创建者 Roi S

Oct 29, 2017

I really like how Andrew Ng is really to the point and really focuses on real world applications. I'm waiting for the other courses to come out.

创建者 Mohankumar S

Aug 31, 2017

A Wonderful backdrop on tuning parameters and optimization, culminating with the best fundamental intro ever to TensorFlow. Thanks again Andrew!

创建者 Oshan J

May 10, 2021

Great course. The theory videos were very clear and easy to understand. Quizes and programming assignments helped a lot to grasp the knowledge.

创建者 Alexander

Feb 20, 2021

It's really helpful for me to get a quick view of neural networks . I have learned how to tune the hyperparameters during the learning process.

创建者 Metin K

May 26, 2020

It's perfect to understand what affects a Neural Network performance and how to tune them. Seems short but definitely lots of information here.

创建者 Chandrasekhara S V

May 10, 2020

Very good course, Thanks Andrew Ng and team. Mathematical background of each hyper parameter is clearly explained and the assignments are good.