Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization に戻る

4.9

39,941件の評価

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4,252件のレビュー

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....

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.

フィルター：

by Mahmut K

•Nov 30, 2018

This second course was great in terms of showing improvements. I would have enjoyed a little more rigorous treatment of why improvements work, but then the course could go on and on... I sill think Andrew can spend a little more time on overcoming overfitting. All in all, excellent balance!

by Mukund A

•Nov 29, 2018

Awesome! Very helpful & interesting. Looking to take up more courses in future.

Best way explanation. Awesome quiz & programming exercises.

by yash g

•Nov 29, 2018

Amazing course gives useful insights for training!

by Renjie T

•Nov 29, 2018

Great Course! Appreciate!

by hexinlin

•Nov 29, 2018

great

by Nagadeepa S

•Nov 13, 2018

Very easy to follow instructions. Great learning.!

by Khoo T S

•Nov 14, 2018

Great course. I've learnt a lot on hyperparameter tuning and optimization strategies. The Tensorflow makes coding simpler :)

by Satyam N

•Nov 13, 2018

Gives great detailed insights over parameters tuning and steps to improve the neural network performance.

by Chen N

•Jan 18, 2019

Awesome as always.

by Abhishek B

•Jan 16, 2019

Awesome Content and tutors!

by Raj

•Jan 17, 2019

Awesome course.

by Wei L

•Jan 16, 2019

Fantastic course design!

by Shravan M

•Jan 17, 2019

Thank You!!!

by John l

•Jan 16, 2019

good course indeed

by Kirk B

•Jan 17, 2019

Andrew Ng is hands down the best teacher in this space. Excellent lectures and a well run course.

by Shayan A B

•Jan 05, 2019

Another well-taught course. Cant wait to complete more in the specialization.

by Sudheer P

•Jan 05, 2019

This course teaches the mechanics of deep neural networks and how to optimize the neural net. Prof goes at a reasonable pace so that the student understands the concepts.

by Qasid S

•Jan 06, 2019

Great Course!! This course should be part of every deep learning career path.

by chanish a

•Jan 05, 2019

I never have enjoyed this much while studying.

by Ruiliang L

•Jan 06, 2019

Help you get the best understanding of the deep learning

by Babu, C

•Jan 07, 2019

Excellent optimization techniques articulated very well

by Arsalan

•Jan 06, 2019

I believe a approach Sir takes while teaching the course makes it comparatively easy to learn the very difficult concept of deep learning.

by Arram B

•Jan 05, 2019

Thank you Andrew Ng Sir, you made every complex topic easily understandable with very efficient way.

Thanks for everything Sir!!!!

by Abdullah

•Jan 07, 2019

Very thorough explanation about the hyperparameters and optimization techniques.

by Sourav

•Jan 05, 2019

Learnt a great deal about tuning models. Concepts of regularization, batch norm and optimizers were very well explained.