返回到 Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

4.9

40,004 個評分

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4,259 個審閱

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....

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.

Jun 03, 2018

Just as great as the previous course. I feel like I have a much better chance at figuring out what to do to improve the performance of a neural network and TensorFlow makes much more sense to me now.

篩選依據：

創建者 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!

創建者 Mukund A

•Nov 29, 2018

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

Best way explanation. Awesome quiz & programming exercises.

創建者 yash g

•Nov 29, 2018

Amazing course gives useful insights for training!

創建者 Renjie T

•Nov 29, 2018

Great Course! Appreciate!

創建者 hexinlin

•Nov 29, 2018

great

創建者 Nagadeepa S

•Nov 13, 2018

Very easy to follow instructions. Great learning.!

創建者 Khoo T S

•Nov 14, 2018

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

創建者 Satyam N

•Nov 13, 2018

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

創建者 Chen N

•Jan 18, 2019

Awesome as always.

創建者 Abhishek B

•Jan 16, 2019

Awesome Content and tutors!

創建者 Raj

•Jan 17, 2019

Awesome course.

創建者 Wei L

•Jan 16, 2019

Fantastic course design!

創建者 Shravan M

•Jan 17, 2019

Thank You!!!

創建者 John l

•Jan 16, 2019

good course indeed

創建者 Kirk B

•Jan 17, 2019

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

創建者 Shayan A B

•Jan 05, 2019

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

創建者 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.

創建者 Qasid S

•Jan 06, 2019

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

創建者 chanish a

•Jan 05, 2019

I never have enjoyed this much while studying.

創建者 Ruiliang L

•Jan 06, 2019

Help you get the best understanding of the deep learning

創建者 Babu, C

•Jan 07, 2019

Excellent optimization techniques articulated very well

創建者 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.

創建者 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!!!!

創建者 Abdullah

•Jan 07, 2019

Very thorough explanation about the hyperparameters and optimization techniques.

創建者 Sourav

•Jan 05, 2019

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