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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
63,507 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

BA

May 31, 2020

Very good course, useful and smart. Some of the example are on tensorflow 1 but I think that they will update them soon to keras tf2 Thank you!I will pass on what I have learned here to undergrads :)

DH

Apr 26, 2020

Everything, Everyparameter in neural networks looks familiar to me now. I feel like I can optimize them for better accuracy. Overall I learned some new things and the way of teaching was really nice.

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2926 - Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization 的 2950 个评论(共 7,286 个)

创建者 Aatish S

Feb 19, 2019

Very good experience using tensor flow framework for deep learning

创建者 Krishna P G

Jan 1, 2019

Provides good overview of hyperparameters and methods to tune them

创建者 Can K

Jan 1, 2019

This course is helping me a lot. I am an undergraduate researcher.

创建者 Aishwarya R

Jun 20, 2018

Brilliant tutorial- i thoroughly enjoyed studying for this course.

创建者 Sudhanshu S

May 26, 2018

Awesome as expected. Going to complete the entire specialization!!

创建者 王鹏飞

May 12, 2018

Thank you for sharing this class, it is so useful and interesting!

创建者 Gknow

Jan 2, 2018

Beautifully explained with thorough content. I love these courses.

创建者 Jebakumar S

Dec 4, 2017

Learnt a lot about tuning the hyper parameters of a Neural Network

创建者 Eric

Nov 1, 2017

A very detailed course for neural network tuning and optimization.

创建者 Justina D

Oct 29, 2017

Very interesting and usefull course, easy to follow and understand

创建者 Tomasz S

Sep 15, 2017

Just as the first course in series - excellent introduction to NN.

创建者 Sarat C V

Sep 13, 2017

A well taught course by Andrew Ng; I would recommend it to anyone.

创建者 YoungGun Y

Aug 28, 2017

Great balance between theory and practice. Thanks for the lecture.

创建者 Yunfan W

Aug 19, 2017

Very practical and helpful! Thanks for providing such nice course!

创建者 Drishti K

Sep 5, 2021

Loved the course but maybe TensorFlow needs to be explained more.

创建者 Willibald B

Oct 23, 2020

nice course. providing some good intuition for a lot of concepts.

创建者 Lucas B

Oct 6, 2020

Andrew is so clear at explaining difficult concepts. Recommended.

创建者 swetha n

Aug 15, 2020

Thank you for the amazing teaching, Prof Andrew Ng! Learnt a lot!

创建者 Taranpreet s

Aug 13, 2020

Andrew Ng explanations are detailed and build conceptual clarity.

创建者 Deena Y A

Aug 8, 2020

Amazing course about Deep learning Hyperparameters. Thanks a lot.

创建者 Deb b

Aug 6, 2020

Loved the course content and the tensorflow exercise especially !

创建者 Nien H

Jun 14, 2020

Another outstanding course. Professor Ng is an excellent teacher!

创建者 chhatramani s

Jun 2, 2020

amazing course, content was really helpful and easy to understand

创建者 Hirav S

May 17, 2020

Great course. Learnt many new things about hyperparameter tuning.