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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

39,128개의 평가
4,156개의 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


Dec 24, 2017

Exceptional Course, the Hyper parameters explanations are excellent every tip and advice provided help me so much to build better models, I also really liked the introduction of Tensor Flow\n\nThanks.


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 4,085개 리뷰 중 3951~3975

교육 기관: Aditya S

Aug 09, 2019


교육 기관: Laurence G

Aug 11, 2019

Decent intro to tuning neural networks. I felt the parts on normalization and regularization could have gone into more detail, but perhaps the math was deemed too complicated. Labs are ok, but still a bit buggy despite errors being reported in the forums a while ago.

교육 기관: Dr. H H W

Aug 08, 2019

Interesting material but a bit complex to follow all the equation derivation. Need to repeatedly watching the video to understand the content. After learning this the hyper parameter setting in the ML setup is clearer to me.

교육 기관: Aymen S

Aug 13, 2019

Cours intéressant merci beaucoup Mr Andrew Ng

교육 기관: Armaan

Aug 15, 2019

Extremely well designed course, the key reason for 4 stars is Andrew Ng's amazing leactures. The programming assignment though do quite a bit of handholding which can be reduced.

Amazing experience overall!

교육 기관: Mukesh K

Aug 19, 2019

The content of the Course is very precise and assignment truly reflect what is been taught in the lectures. Explanation and presentation of algorithms are what I like the most. Assignment were very engaging and interesting.

교육 기관: Gerrit V

Aug 19, 2019

Sometimes quit slow

교육 기관: Nguyễn H T

Aug 20, 2019

I think this course is great. Because we learn about some definitions about hyperparameters, optimization which are frequently appears in papers or in the functions in some Deep Learning frameworks.

교육 기관: Asad A

Aug 17, 2019

Great videos but wish there were more per-lesson exercises that were there in Course#1 for this track. Also, the transition to TensorFlow was quite abrupt as the key concepts that TF uses are completely new and don't easily borrow from the much cleaner Numpy concepts

교육 기관: Daniel E B G

Aug 26, 2019

I think this course would benefit from a little more explaining. There are a lot of new concepts and some explanations were too quick in my opinion.

교육 기관: Cristhian A B

Aug 28, 2019

It's a hard course but the materials are great and their explanations

교육 기관: Ralf S

Aug 28, 2019

Good course overall. but labs could be expanded. Don't know if the Coursera platform supports it, but labs between lectures about different topics would be nice instead of having all practical exercises at the end.

교육 기관: Bharath C

Jul 02, 2019

A good theoretical explanation and good working assignments that impart basic understanding of different optimization methods, hypertuning methods and tensorflow framework. But, some mistakes in the tensorflow assignment in the script itself, needs to be rectified.


Jul 04, 2019

Content was excellent and it was delivered by Andrew Ng sir in an outstanding way

교육 기관: Jayshree R

Jul 04, 2019

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

교육 기관: Thomas D

Jul 07, 2019

Some very interesting material for beginners. At times it feels like concepts are being repeated over and over again, but there is enough new concepts to keep it worthwhile to repeat.

교육 기관: SUNIL D

Jul 07, 2019

Very Good Course to understand Step by Step

Hyperparameter tuning, Regularization and Optimization to improve Deep Neuaral Networks & Practical Assignments !

교육 기관: Ernst H

Jul 07, 2019

Obvious problems. Lessons and quizzes need to be polished.

교육 기관: Yating G

Jul 14, 2019

The courses are vey well organized and easy to understand.

교육 기관: daniele r

Jul 15, 2019

One of the best and most technical course in this Specialization: I enjoyed learning a lot on optimization algorithms. Really good practical hints on tuning and on bias variance analysis, that are very difficult to find in textbooks

교육 기관: Siddharth K

Jul 15, 2019

Need Information about other parameters like #of iterations, how to choose number of hidden layers?, number of neurons in hidden layers, inclusion of few other strategies to choose neural network model will be helpful. If they are covered in next courses, then please ignore.


교육 기관: Mor k

Aug 30, 2019


교육 기관: Eamonn G

Sep 04, 2019

Overall good class.

교육 기관: Prerna D

Sep 07, 2019

Very good course. All the concepts explained very well. I just feel programming assignments were too easy, they could be a little tougher

교육 기관: Hossein M

Sep 09, 2019

too complicated, many lessens in couple of short videos.

poor video transcript