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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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63,493 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

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

RR

Jun 12, 2020

Could have increased assignments and some more indepth knowledge of tensorflow and proper installation way of tensorflow cause mine is showing error when iam trying to practice as shown in the video

筛选依据：

3051 - Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization 的 3075 个评论（共 7,283 个）

创建者 Md. M H

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Feb 12, 2019

Great course and great tutorials on a deep learning framework.

创建者 伟杰 邓

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Dec 5, 2018

I will keep taking the following courses given by professor Ng

创建者 Vinay M

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Aug 15, 2018

Excellent start for using TensorFlow and hyperparameter tuning

创建者 Sudip C M

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Aug 5, 2018

Excellent introduction to hyperparameter tuning and tensorflow

创建者 Shounak D

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Jul 31, 2018

Great course as usual..excited to complete the specialisation.

创建者 SUBHABRATA T

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Jul 23, 2018

In depth theory but i need some more programming explaintion .

创建者 rachit g

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Jul 17, 2018

Excellent course. This course has no substitute in the market.

创建者 Ganesh R

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Jun 30, 2018

Good course to understand the Hyperparameters and Optimization

创建者 Divya R

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Mar 31, 2018

Great, but gets complex. Which is good, but also difficult! :)

创建者 Cheryl A

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Mar 21, 2018

Really great insights and intuition on tuning and optimization

创建者 Phi N V

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Dec 14, 2017

the course is so good but the problem set should be more handy

创建者 Amit P

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Dec 7, 2017

Anyone who wants to learn Deep learning must take this course.

创建者 Rodney R

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Nov 16, 2017

Amazing course! That explain step by step how to do.

Thank you.

创建者 Amy L

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Oct 24, 2017

Amazing Course, the teaching approach is from shallow to deep.

创建者 Khurram S

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Oct 13, 2017

Great course! good to learn Hyper Parameter to tune Parameter.

创建者 Sarth S

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Feb 15, 2024

Excellent course for finer details of machine learning models

创建者 Milad S

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Oct 29, 2021

this is LOVELY course, TNX you mr NG and coursera.

with LOVE

创建者 Shahnawaz S

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Sep 13, 2021

I like the content and the way trainer explained all about it

创建者 Hengrui C

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Jul 22, 2021

Great introduction to tensorflow and related tuning concepts.

创建者 Alejandro F N

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Mar 6, 2021

Great continuation for improving my neural networks knowledge

创建者 Saurabh S

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Feb 23, 2021

Very much enjoyed doing this course. Cheers, Andrew and team.

创建者 Apurva T

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Nov 28, 2020

Perfect course to learn Hyper-parameter tuning and Tensorflow

创建者 Rafael M

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Jul 23, 2020

Excellent course. Only downside is week3 is in TensorFlow 1.0

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.