Chevron Left
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
stars
62,825 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

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.

JS

Apr 4, 2021

Fantastic course and although it guides you through the course (and may feel less challenging to some) it provides all the building blocks for you to latter apply them to your own interesting project.

Filter by:

6451 - 6475 of 7,216 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Benjamin J

•

Oct 29, 2017

I would have liked more programming exercises related to regularization and hyperparameter tuning, but TensorFlow was well introduced.

By ISHAN P

•

Jun 30, 2021

Weeks 1 and 2 were awesome. However I think we ned a more intensive programing assignment on Week 3 to get hands on with Tensor Flow.

By Paulo M

•

Aug 26, 2020

I liked the course. I just think there should be more assignments. Perhaps after each week because the content is dense and complex.

By Vighnesh N G

•

Apr 21, 2020

Too much spoon feeding in the programming exercises, could have asked us to make a model with atleast x accuracy then left us alone.

By Mahesh S P

•

Apr 17, 2020

This was the toughest course since lot of mathematical, especially statistics back ground is required. However, I could complete it.

By S S

•

Aug 18, 2022

The course includes lots of information and need focus and concentration.

The Tensorflow part is not enough to solve the assignment.

By Sri K

•

Apr 10, 2020

Its require basic python programming for implementation of neural networks , different models techniques to get perspective of it .

By Ahmed N

•

Mar 24, 2018

One of my best courses i have ever participated in, i gained a lot of knowledge and knew the underlying mathematics of every model.

By Mathieu J

•

Feb 24, 2018

Second step of the specialization,

a bit less rewarding than the fist course as more fine tuning and less overview of deep learning

By Mohammed M

•

Oct 18, 2017

Programming assignments could have been more challenging. Otherwise, the course instructor is pretty awesome!! Thank you Andrew Ng.

By Swann C

•

Oct 6, 2017

Good material and definitely essential in order to gain a lot of time aiming at the right direction navigating all these parameters

By Amine D

•

Nov 22, 2019

Very good course. I would have liked a little longer introduction to the tensorflow architecture and less help on the assignements

By Curt D

•

Sep 5, 2018

A good introduction to gradient descent algorithms and hyperparameter tuning with a little TensorFlow thrown in for good measure!

By Jorge C

•

Sep 22, 2017

Good course, Hyperparameter tuning, Regularization and Optimization are well explained, and the Tensor Flow lab is very useful too

By Marcela I

•

Jan 18, 2021

Es un buen curso, basado principalmente en aspectos prácticos. Me pareció menos interesante que otros de la misma especialización

By Xin X

•

Sep 19, 2017

Tips are very useful! Have some typos/errors in assignments, more coding work can help understanding. Thanks for sharing, Andrew!

By JUI-CHIEH, W

•

Aug 26, 2017

Many practical tricks such as tuning hyper parameters and the use of major optimization techniques such as batchnorm and dropout.

By Md. T R

•

Jul 18, 2020

I think a bit more hands on teaching would be better for this course and also if you could mention resources it would be better.

By Jose G H H

•

Jan 25, 2022

Bastante bueno, explica de manera simple y práctica los distintos métodos para acelerar el aprendizaje de las redes neuronales.

By Arvind D R

•

Dec 31, 2021

The last assignment has no clear instructions on how to use the functions and has error which doesn't initazlie the parameters

By Victor-Hugo J

•

Aug 4, 2018

Very interesting course. But a bit fast I would say. Sometimes I didn't feel the programming assignments were that challenging.

By kevin k

•

Jul 6, 2020

The course was great overall, but I wish we had more involvement in the set up of the programs in the programming assingments.

By NAMAN M

•

Jun 3, 2020

It would've been much easier if the graded functions of the tensorflow practice assignment would've been explained separately.

By Dário P

•

Mar 18, 2020

Nice basics that help understand the fundamentals. The last part of the course should be upgraded to Tensorflow 2.0 already...

By Uddhav D

•

May 28, 2019

Again awesome explaining by Andrew, although I feel Batch Normalization should be a bit stressed upon and explained in detail.