Chevron Left
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
43,089개의 평가
4,629개의 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.

AM

Oct 09, 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

필터링 기준:

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization의 4,563개 리뷰 중 4151~4175

교육 기관: Peter T

Apr 17, 2018

Useful information, good intuition, but lack of formal results. More homework would improve the learning experience.

교육 기관: Ashutosh P

Apr 04, 2018

It was a great course. Really well taught by Professor Andrew Ng. Some "from the scratch" coding assignments needed.

교육 기관: Suresh D

Mar 01, 2018

I hated the tensorflow part though. Would have much preferred it if we could have moved away from jupyter notebooks.

교육 기관: Francisco C

Jul 24, 2018

Very good content overall. Very well explained and good examples. Many mistakes in the comments in the assignments.

교육 기관: Abhinava K

Dec 08, 2017

Content is good, but assignments are not interesting. Some application oriented assignments will be be encouraging.

교육 기관: Manish C

Jan 23, 2020

Like all other andrew ng courses this course is also the best course to deep dive into neural network algorithms .

교육 기관: Francesco P

Feb 26, 2019

I would like to see more programming assignments. They are very well done and it'd be great to have more of those.

교육 기관: Angad P S

Dec 13, 2017

I would really benefit from this course if more assignments are provided to try different data sets and scenarios.

교육 기관: Giovanni C

Feb 11, 2019

I liked the course, but the explanation of tensorflow needs more propaedeutic introduction for a learner like me.

교육 기관: Charbel J E K

Jan 17, 2018

Really helpful ! Too much concepts to understand but only applying few in the course. I really liked this course.

교육 기관: Jay R

Dec 24, 2017

Good course to get familiar with hyperparameters and improving the neural networks. And cliff hanger was amazing!

교육 기관: Mads E H

Oct 26, 2017

Nice and practical. The assignments could go a step further in trying out different things to get better results.

교육 기관: Jayanthi A

Apr 05, 2018

It was great course, however, I would have liked it to be a lot slower with more time being spent on Tensorflow.

교육 기관: Joshua S

Nov 13, 2019

A good course that provided more intuition on which models to work with and how to tune parameters effectively.

교육 기관: Aayush A

Aug 03, 2019

The Jupyter notebooks had a lot of mistakes which wasted a lot of my time otherwise the course content was good

교육 기관: Corbin C

May 10, 2018

Good lectures, but the jupyter notebook examples are inconsistent and sometimes use deprecated Tensorflow code.

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?