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Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization(으)로 돌아가기

deeplearning.ai의 Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization 학습자 리뷰 및 피드백

4.9
43,022개의 평가
4,618개의 리뷰

강좌 소개

This course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good results. You will also learn TensorFlow. After 3 weeks, you will: - Understand industry best-practices for building deep learning applications. - Be able to effectively use the common neural network "tricks", including initialization, L2 and dropout regularization, Batch normalization, gradient checking, - Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence. - Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance - Be able to implement a neural network in TensorFlow. This is the second course of the Deep Learning Specialization....

최상위 리뷰

NA

Jan 14, 2020

After completion of this course I know which values to look at if my ML model is not performing up to the task. It is a detailed but not too complicated course to understand the parameters used by ML.

XG

Oct 31, 2017

Thank you Andrew!! I know start to use Tensorflow, however, this tool is not well for a research goal. Maybe, pytorch could be considered in the future!! And let us know how to use pytorch in Windows.

필터링 기준:

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization의 4,547개 리뷰 중 4151~4175

교육 기관: Srikanth C

Oct 01, 2017

I particularly benefited from the explanations of dropout, batch normalization and the RMSProp/Adam optimisers.

교육 기관: Narendran S

Oct 01, 2017

TensorFlow needs more time dedicated to it. I didn't completely understand the concepts behind this framework.

교육 기관: Arun J

Sep 17, 2017

really loved the course material but would have loved it more if it gave more in depth tutorials on tensorflow

교육 기관: Hector D M P

Sep 02, 2017

Nice and clean; with nice focus in the framework; but they also could be more in depth regarding the exercises

교육 기관: Raman J M

Aug 20, 2017

Quizes as part of middle of lectures help to check the understandings. For many lectures quizzes are missing.

교육 기관: Yunhao Z

Mar 21, 2018

-1 : Serveral bugs inside the assignments, causing 0 grades in auto grader

That said, a perfect intro to DNN.

교육 기관: Qihong L

Oct 02, 2018

sometimes the teacher speaks too fast to follow, but the content itself is very good and easy to understand

교육 기관: Gustave G

Dec 23, 2017

Very good videos but programming exercises are way too easy and some written material would be appreciated.

교육 기관: Donguk L

Nov 25, 2017

Maybe providing some video or reading resource for back propagation processes for batch norm would be good?

교육 기관: Aaron E

May 05, 2019

its a good intro, if not a little simplistic with the coding exercises, bring back the quizzes mid lecture

교육 기관: Oleksiy S

Dec 11, 2018

A small validation output error that is still not fixed prevent to rate all stars for the exellent course.

교육 기관: 苑思域

Aug 03, 2018

This one is actually a little bit better than the first one, maybe less content, maybe more understandable

교육 기관: Leitner C S E S

Aug 29, 2017

Excellent course. But -1 for using TensorFlow, a not-really-free framework, to introduce students to them.

교육 기관: Jayshree R

Jul 04, 2019

An intuitive approach towards Hyper parameters. Covers the concept of optimization algorithms quiet well.

교육 기관: Makragić A

Jan 09, 2019

Great lectures, I'm little disappointed with TensorFlow tutorial, there should be 1 week for that only...

교육 기관: Richard H

Sep 29, 2017

Fills in the tricky gaps in using DNN that are necessary to transition from basics to practical projects.

교육 기관: Shijian G

Nov 29, 2019

These series are generally clear and well-organized. It would be better to provide tensorflow materials.

교육 기관: Ranjan D

Jul 17, 2019

Great explanation on tuning different hyper parameters and how they can effect the model's performance.

교육 기관: Keanu T

Jun 26, 2019

I wish it went a little more in-depth with softmax classifiers but I can find that online so it's good.

교육 기관: Byron M

Apr 09, 2018

The final assignment didn't have the right instructions, a lot of misleading comments and instructions.

교육 기관: Matías L M

Oct 30, 2017

The professor is really good at explaining. The projects got more interesting than in the first course.

교육 기관: Per K

Oct 03, 2017

Get you to a more practical understanding of deep learning. The introduction to TensorFlow is valuable.

교육 기관: LEO L

Jul 31, 2019

All is good except the submission part, sometime return submission failure without specifying a reason

교육 기관: Hamza E B

Jun 22, 2019

Great Course ! I learned a lot, but I would have preferred another Framework though (like Pytorch) ...