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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

40,598개의 평가
4,318개의 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


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


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.

필터링 기준:

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization의 4,258개 리뷰 중 4076~4100

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: Fredrik C

Jul 18, 2019

Great, but could be better. Fix the typos. Add summarized video notes. Etc.

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: Om S P

Jul 19, 2019

Some assignments, even though I get the same result as the output given, it get marked as wrong... Please try to rectify it

교육 기관: Екатерина Р

Jul 24, 2019

The course was very helpful as now I understand optimization techniques and all the parameters of neural networks. Unfortunately, the course has not answered my question how to tune the whole bunch of hyperparameters from the scratch, what is the correct order and logic of the full ANN tuning, not just one parameter.

교육 기관: Pascal A S

Jul 22, 2019

A bit too technical for my taste. But useful examples to work through.

교육 기관: Nicolas B

Jul 24, 2019

This is a very interresting course that go past basic deep neural network knowledge. I learned a lot. Still I would have like a bit more programming exercices to have more part of the theoretical course covered (batch norm, hyper parameters tunning).

교육 기관: Harsh B K

Jul 30, 2019

Good Insights of hyper parameters with other techniques to improve learning rate.

교육 기관: Andrew W

Jul 30, 2019

Felt fast faced. But a good introduction to neural network hyperparameter optimization.

교육 기관: LEO L

Jul 31, 2019

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

교육 기관: Oleksandr T

Jul 29, 2019

Last code assignment is a mess. Looks like organizers have no intention to fix errors.

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: Shubham K J

Aug 08, 2019

Grader is not performing well even though my outputs are matching.

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: Aditya S

Aug 09, 2019


교육 기관: Gianluca S

Aug 10, 2019

No course material available

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: 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: ATIK M

Apr 25, 2019

Good can be improved by providing more code based video like Tensorflow.

교육 기관: Efthimios K

Jun 13, 2019

Good but need letter recognition NN to understand what he writes

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: 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.