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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
별점
52,522개의 평가
5,940개의 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

CV

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.

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.

필터링 기준:

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization의 5,871개 리뷰 중 5301~5325

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: Rahad A N

May 13, 2020

Absolutely love the course and the way Andrew teaches us, though I have a little bit discomfort in writing codes.

교육 기관: Emmanuel

Mar 05, 2020

A little bit to theorical and with too many guidance at some points and not much at some other (for TF functions)

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: Zechen Y

Apr 12, 2020

The contents are explicit and adequate but I think It would be better if I could get more exercise about coding.

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: Johannes C d M

May 27, 2020

Very well explained, but the Tenserflow explanation is shallow for those that have less programming experience.

교육 기관: Dilip V

Apr 29, 2020

This Course Helped me a lot in learning how to get best-optimized models by tuning Hypermeters.I really like it

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: Shailesh

Apr 03, 2020

Really helpful in terms of practical application and tricks/tuning for DNN. Also starts on TF which is bonus!