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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

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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.

筛选依据：

3076 - Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization 的 3100 个评论（共 7,286 个）

创建者 Anil K

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Jul 3, 2020

Awesome course, Thanks Andrew ng to taught again great topics

创建者 haiderali

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Jun 3, 2020

Simple, fact full and amazing course. Definitely recommended.

创建者 Vishal S

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May 15, 2020

Very good course for Deep Learning revision before interviews

创建者 Siba S P

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May 12, 2020

Yet another wonderful course by deeplearning.ai and Andrew Ng

创建者 vikash c m

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Jan 2, 2020

Best course for Hyperparameter tuning of Deep neural network.

创建者 Douglas C

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Dec 28, 2019

Well structured. Maybe too much help on the coding exercizes?

创建者 Amish T

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Dec 18, 2019

Excellent course. It helps in fine tuning your neural neteork

创建者 曾湘

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Nov 25, 2019

nice design to clearly show the principle of neural networks.

创建者 JackyChung

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Nov 23, 2019

Thanks for Coursera, I have learned so much from this course!

创建者 anantharamaiah k

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Sep 18, 2019

This course gives excellent in site on Hyperparameter tuning.

创建者 Michael Z

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Aug 19, 2019

The experiences from the teacher will help the starter a lot.

创建者 Miguel Á S A

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Aug 13, 2019

Este curso es fantástico!!, he aprendido muchas cosas nuevas.

创建者 Erik H

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Jul 18, 2019

Very insightful. I really enjoyed the programming assingments

创建者 W X

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May 26, 2019

I have learned a lot from the course, and I had a lot of fun.

创建者 Abraham K

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May 11, 2019

Loved it. All of the courses in the program are outstanding.

创建者 Jose P

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Mar 22, 2019

This course, so far is surprisingly useful and well explained

创建者 Graham M

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Feb 7, 2019

Very practical. Great to get to TensorFlow by the third week.

创建者 poleNgrek

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Dec 9, 2018

A great course, suitable for everyone and easy to comprehend!

创建者 Sidharth S

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Oct 22, 2018

Learned so much about deep learning optimisation. Amazing !!!

创建者 tobechukwu n

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Mar 31, 2018

Concepts were presented with an intuitive approach. Awesome !

创建者 Kurt S

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Mar 4, 2018

Good introduction and content for Optimizing Neural Networks.

创建者 siddartha y

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Jan 21, 2018

Very fast. Would not recommend to non maths background people

创建者 Kirsten S

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Nov 5, 2017

another amazing class. I really like the whole series so far.

创建者 Muhammad A b C M

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Sep 15, 2017

Very good, such a new perspective obtained after this course.

创建者 Gerzson D B

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Sep 4, 2017

Fantastic course, useful details, understandable explanation.