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Back to Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

Learner Reviews & Feedback for Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization by deeplearning.ai

4.9
40,038 ratings
4,260 reviews

About the Course

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....

Top reviews

AM

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

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.

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26 - 50 of 4,189 Reviews for Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

By Jhon S

Nov 26, 2018

cool

By Marc M v W

Nov 25, 2018

As good as the first one with more insights in hyperparameters tuning and faster convergence techniques. The whole course finishes with a small introduction to TensorFlow.

By Leigh L

Nov 26, 2018

I love this course. It has many in-depth tips and advices based on many real life experiences. Many suggestions can be applied directly into solving difficult Deep Learning practices.

By Priyanka D G

Nov 25, 2018

It is already good, But places need to correct typo

By Peter S

Nov 26, 2018

Andrew Ng is a seriously talented educator.

By ahmed a s

Nov 26, 2018

Super awesome!

By Narain

Nov 24, 2018

it was a very good content to start from scratch

By Marcelo F

Nov 25, 2018

It will help me to find solutions in ANN implementations

By 罗炜儒

Nov 26, 2018

相比系列课程的第一课,该课程内容更加深入,对于深度学习模型构建过程的参数问题及优化方法给出了详尽具体的分析和探讨,能很好的让学生理解深度学习背后的意义,满分推荐

By Zhaiyu C

Dec 13, 2018

learnt a lot!

By Ricardo N d R B

Dec 13, 2018

Excellent course

By Manish N

Dec 13, 2018

great learning

By Andrei K

Dec 13, 2018

Very good. I've improved my knowledge in understanding and tuning! Thanks

By Thibault R

Dec 15, 2018

Thanks you very much, this is really a great course!

By chiven

Dec 13, 2018

finishing the courses, I learned how to tune hyperparameters and basic use of tensorflow

By Dhruv D B

Nov 28, 2018

Awesome

By Renjie T

Nov 29, 2018

Great Course! Appreciate!

By Daniel G

Nov 28, 2018

Genau so gut wie der 1 Kurs dieser Reihe. Ich bin sehr überzeugt von dieser Möglichkeit sich neuen Wissen anzueignen.

By Hari K

Nov 27, 2018

Andrew makes the topic interesting and easy to grasp. The assignments are very well written. Great course!

By ahmadreza a

Nov 27, 2018

please correct the mistake in the week 3 assignment, placeholder

By hexinlin

Nov 29, 2018

great

By Mohammed A B

Dec 15, 2018

awesome content !

By Ventsislav Y

Dec 16, 2018

Awesome course. I really like the detailed explanations and the exercises. I'm looking for the next one.

By YASH H J

Dec 16, 2018

Improved by knowledge drastically!

By Akash R

Dec 16, 2018

I understood the bits and pieces of how tuning particular hyperparameters will lead to a great improvement model being developed. Andrew NG was great to teach everything with examples and deep dive into the concepts. Thanks for the opportunity provided.