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Learner Reviews & Feedback for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization by DeepLearning.AI

4.9
stars
63,175 ratings

About the Course

In the second course of the Deep Learning Specialization, you will open the deep learning black box to understand the processes that drive performance and generate good results systematically. By the end, you will learn the best practices to train and develop test sets and analyze bias/variance for building deep learning applications; be able to use standard neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking; implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence; and implement a neural network in TensorFlow. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI....

Top reviews

AM

Oct 8, 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

XG

Oct 30, 2017

Thank you Andrew!! I know start to use Tensorflow, however, this tool is not well for a research goal. Maybe, pytorch could be considered in the future!! And let us know how to use pytorch in Windows.

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1376 - 1400 of 7,253 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Gek H C

Dec 14, 2017

I love this course. Videos are very well split into short sessions, clear explanation, very good examples, quizzes and graded tutorials.

By Jitendra M

Oct 9, 2017

Andrew Ng has no parallel in bringing absolutely complex concepts down to a level that idiots like me can understand and apply. Salute!

By Kishore V

Sep 2, 2017

Programming assigments guide you through everything you need to know about choosing parameters and implementing optimization algorithms.

By Suzaki Z

Jul 23, 2023

Very helpfull in understanding how the fine tuning and everything works!!

love the way how Mr. Ng explain everything simple and neat....

By Praveen

Jan 18, 2023

This is an excellent course. Well organized with right amount of depth to understand the concepts. Please keep them coming! Thank you!!

By Camilo G

Apr 29, 2021

Me gustó mucho la forma en la que muestran las diferencias entre varias vías a tomar al hacer 'tuning' de los algoritmos.

Lo recomiendo

By Leonardo P

Sep 30, 2020

Really fun and informative, it was so clear I feel I have very good basics and understanding of neural networks structure and creation!

By Hasaan A

Jul 27, 2020

Awesome course that introduces you to tuning hyperparameters, different optimization algorithms and implement most things from scratch.

By Safvan V

Jun 10, 2020

Really interesting content, especially regularization and dropout. We must have to go through this before start implementation of DNN.

By Michael L

Apr 23, 2020

Overall great, very interesting! I think it would be great if you could provide some PDF with material summery in the end of each week.

By Carlos S C V

Apr 14, 2020

Muy buen curso para mejorar las Deep Neural Networks, bien explicado y las tareas ayudan mucho a clarificar su implementación en Python

By Noel J

Apr 10, 2020

Excellent material and even better presentation! Home work assignment are done so well and help you understand the material. Loved it!!

By Jean M C

Mar 15, 2020

Great course. It does use Tensorflow 1.0 though and I do feel like they hold your hand too much during the exercises. Enjoyed it still.

By Sergio B

Mar 10, 2020

Great course. I would sugest to add some extra practice on parameter tuning. It would be usefull to have some tensorboard introduction.

By 李子轩

Jan 21, 2020

感谢吴恩达老师带来的课程,这门课不仅仅使我对深度学习更加感兴趣.还让我想到了很多能够用其完成的一些事情. 课程虽然结束了, 但是有关于深度学习的学习才刚刚开始, 最后再次感谢吴恩达老师, 以及提供这个平台的coursera课程,让我在中国可以听到来自全世界的课程,谢谢!

By Renesteban

Jan 21, 2020

Excellent Course, I could go deep into the Machine Learning methodologies and I learned how to optimize Deep Neural Networks Algorithms

By Vignesh S

May 24, 2019

I got to know the optimization algorithms to use and also the Tensorflow programming framework in depth. It was a really useful course.

By Dawid P

Apr 2, 2019

Alot of useful info about neural network tuning and easy introduction to Tensorflow framework. Absolutely must see for every DL novice!

By Hashem A

Jan 17, 2021

Amazing course, with great practical insights on hyperparameter optimization for deep learning models. Andrew Ng is a great professor!

By Lavinius I G

Nov 4, 2020

The programming assignments can be a tricky to solve, due to lack of proper explanations and the horrible documentation of TensorFlow.

By Agam T

Sep 23, 2020

A must course for understanding hyperparameter tuning, regularization, and optimization for real-world applications for deep learning.

By William B

Jun 1, 2020

Great introduction to regularization and optimization. I wish the TF assignment were done in PyTorch instead, but it was still useful.

By Aditya N

May 20, 2020

Amazing content and wonderful teaching skills of Andrew Ng. Thanks for helping me out on such a difficult topic with so much of ease!!

By Yanto J

Mar 24, 2020

Thanks to Prof Andrew, now I can understand the roles of hyperparameters. Tuning them skillfully requires a lot of experience, though.

By Plusgenie

Jul 21, 2018

기본의 코스를 Numpy를 통해서 배우다, 본격적으로 프레임 워크 (Tensorflow) 들어가니 이해가 잘 됩니다. 특히 머신 러닝 강좌를 이렇게 코딩을 섞어서 만들기까지, 얼마나 많은 정성과 노력이 들어갔음을 알기에 경의를 표합니다.