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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
63,478 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

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.

筛选依据:

7151 - Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization 的 7175 个评论(共 7,283 个)

创建者 John D

Feb 13, 2021

The content was solid, but some of the labs seemed a bit buggy (getting full credit even though my code didn't run). I also wish the TensorFlow tutorial used TensorFlow 2.0

创建者 Debjit G

Jun 19, 2020

The course was amazing as expected. But the quality of videos needs improvement. Also if programming part was explained in the videos then that would be great. Thank you.

创建者 Sagar B

Jun 15, 2020

Too many issues with the auto grader system. Need to improve the know errors and save the time pf users. I spent more than 3 hours total just to fix the grader bugs.

创建者 Yogeshwar j

May 24, 2020

It could have been more detailed and interesting. Compared to the first course of the specialization, This course's material didn't clear all the concepts clearly.

创建者 Madhur S

Aug 4, 2020

Great course for a beginner like me. I wish however that sizing of hidden layers/units should have been addressed as it is very difficult to achieve the optimum

创建者 Aniruddh B

Apr 15, 2020

Docked one star because of using Tensorflow 1.4 instead of 2.0. Docked another star because I found the course content less interesting than the first course.

创建者 Kishore K

Sep 17, 2018

Some of the videos are very abstract and needs a bit of mathematical intuitions. These intuitions are best obtained by calculations rather than a lecture :)

创建者 Yazid H

Oct 12, 2019

A bit too theoretical for my taste, lacks practical homework and getting our hands dirty. Really appreciated the final week's structure and topics.

创建者 harmouchi m

May 6, 2018

ike usual andrew ng perfect explanation simple go to essential stuff.

the minus points some troubles with notebook

big thanks for andrew ng's team.

创建者 David S

Dec 19, 2024

Good introduction to the topic. The slides are chaotic, hard to read and useless on their own. The translation in the quizzes is inconsequent.

创建者 Marco B

Apr 20, 2020

There are errors on some exercises (adam of week 2) still unsolved after over 1 year (found same error reported on the forum/discussion)

创建者 Christian K

May 22, 2018

The lecture videos are good but the assignments are not that useful as they provide th answers within them and are somehow repetitive.

创建者 Marcos G M

Mar 23, 2025

The third week was confuse. Mainly the lecture about TensorFlow, the quiz and the lab. I think those can be significantly improved.

创建者 Pavel K

Aug 3, 2019

Lectures are good. Programming exercises are too easy. Too mechanical, no much thinking required, à la "fill the gaps" exercises.

创建者 rupamita s

Jun 1, 2020

I would give five starts if not for that grade error issue. I hope it gets resolved for good. Otherwise. Great course as usual.

创建者 Robert D

Jan 4, 2022

Mostly good, last programming assignment had some issues with shapes required for various code sections not lining up properly

创建者 Carsten B

Jun 9, 2020

Interesting, but not nearly as good as the first one. Disjointed topics, unconnected exercises made this less digestable.

创建者 Jean-Michel P

Jun 17, 2021

Decent class, but the last module(week) felt a bit rushed. Hopefully it was simply an introduction for the next class.

创建者 Kang L T

Jan 25, 2019

I think more should be done regarding the TensorFlow framework with more explanations given to what the functions did

创建者 Moustafa M

Dec 9, 2017

Lake of practice, Lake of intimations with good examples

Less in Ternsorflow don't know how to implement and deploy it

创建者 Mohammad E

Aug 13, 2020

The course and the material are great. However, the codes in the labs have serious problems which should be solved.

创建者 Lucas N A

Mar 6, 2020

Really helpful advises. I felt it was too focus on the implementation side but I liked the intuitions parts better.

创建者 Rishabh G

Apr 28, 2020

Week 3 of the course does not have a practice problem for batch normalization. Wanted to implement it and learn.

创建者 Ramachandran C

Oct 6, 2019

I found the video lectures useful to understand the concepts, but the programming exercises are over-simplified.

创建者 Edmund C

Sep 30, 2024

Not as good as the other courses in the specialization track. It seems there was a good amount of repetition.