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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
stars
42,216개의 평가
4,505개의 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.

HD

Dec 06, 2019

I enjoyed it, it is really helpful, id like to have the oportunity to implement all these deeply in a real example.\n\nthe only thing i didn't have completely clear is the barch norm, it is so confuse

필터링 기준:

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization의 4,440개 리뷰 중 4026~4050

교육 기관: Alex N

Sep 12, 2017

Good pace

Only drawback is that some of the safe checks are wrong in the programming assignments, even with the right seeds.

교육 기관: Khalid A

Sep 15, 2019

It is definitely very informative, but I wish the lectures would be more in depth in regards to the derivation and proofs.

교육 기관: Ruud K

Feb 06, 2019

Really love the course, the quizes and programming assignments. But not 5 stars cause the audio quality is extremely poor.

교육 기관: Arkosnato N

Nov 28, 2017

course content was very good, but this course should be longer. there was a lot of material covered in a very short time.

교육 기관: Michael B

Mar 17, 2018

More pragmatic approach with theorems would be more appealing....or maybe it is me as i'd prefer Java (DL4J)...not sure

교육 기관: Viviana M

Oct 29, 2017

I really enjoyed the classes, in the training I would've liked to try and improve the model with all the tools learned

교육 기관: Amit J

Nov 22, 2019

Great practical insights.

I wish there were programming assignments on "Hyperparameter tuning" and "Batch norm" too.

교육 기관: Christopher S

Oct 25, 2019

Good intro to the available tools. Very guided course. For concepts to really stick, own projects or courses needed.

교육 기관: George L

Oct 24, 2018

it's good, but definitely not as good as the first course since Prof. Ng was not very clear on some of the concepts.

교육 기관: Ruixin Y

Apr 30, 2018

The course itself is great, but the notebook (programming assignment system) is not stable, it's annoying sometimes.

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: 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.