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

4.9
stars
62,856 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

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.

AS

Apr 18, 2020

Very good course to give you deep insight about how to enhance your algorithm and neural network and improve its accuracy. Also teaches you Tensorflow. Highly recommend especially after the 1st course

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6526 - 6550 of 7,218 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Dilip V

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Apr 29, 2020

This Course Helped me a lot in learning how to get best-optimized models by tuning Hypermeters.I really like it

By Joshua S

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Nov 13, 2019

A good course that provided more intuition on which models to work with and how to tune parameters effectively.

By Aayush A

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Aug 3, 2019

The Jupyter notebooks had a lot of mistakes which wasted a lot of my time otherwise the course content was good

By Corbin C

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May 10, 2018

Good lectures, but the jupyter notebook examples are inconsistent and sometimes use deprecated Tensorflow code.

By Srikanth C

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Oct 1, 2017

I particularly benefited from the explanations of dropout, batch normalization and the RMSProp/Adam optimisers.

By Arran D

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Jun 12, 2023

Despite completing the course, I feel there is much more that I could be tested on to cement my understanding.

By Narendran S

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Oct 1, 2017

TensorFlow needs more time dedicated to it. I didn't completely understand the concepts behind this framework.

By Arun J

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Sep 16, 2017

really loved the course material but would have loved it more if it gave more in depth tutorials on tensorflow

By Hector D M P

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Sep 2, 2017

Nice and clean; with nice focus in the framework; but they also could be more in depth regarding the exercises

By Crack I

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Jan 20, 2024

Great course by Andrew. The exposure and the length of what I had to learn changed the circuitry of my brain.

By Samuel C

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Sep 27, 2021

Some of the programming exercises weren't as polished as part 1 of this specialization. Still great overall!

By Shailesh

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Apr 3, 2020

Really helpful in terms of practical application and tricks/tuning for DNN. Also starts on TF which is bonus!

By Ramanjee M

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Aug 20, 2017

Quizes as part of middle of lectures help to check the understandings. For many lectures quizzes are missing.

By Pabbisetty S R

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Jul 5, 2020

explanation is very good but assignments need to be done comletely by student not like filling missing parts

By Rohit G

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Mar 26, 2020

The tensorflow portions need to be updated. Otherwise it's a great module, building on the previous courses.

By Ryota M

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Mar 21, 2018

-1 : Serveral bugs inside the assignments, causing 0 grades in auto grader

That said, a perfect intro to DNN.

By Qihong L

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Oct 1, 2018

sometimes the teacher speaks too fast to follow, but the content itself is very good and easy to understand

By Donguk L

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Nov 25, 2017

Maybe providing some video or reading resource for back propagation processes for batch norm would be good?

By Mozhdeh S

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Mar 21, 2022

I needed more foundation for understanding tensorflow programming. However, I learnt a lot in this course.

By Parjanya P P

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Jun 23, 2020

The answer in the last assignment was wrong, wasting a lot of my time. But otherwise the course was great.

By Aaron E

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May 4, 2019

its a good intro, if not a little simplistic with the coding exercises, bring back the quizzes mid lecture

By Alex S

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Dec 11, 2018

A small validation output error that is still not fixed prevent to rate all stars for the exellent course.

By 苑思域

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Aug 3, 2018

This one is actually a little bit better than the first one, maybe less content, maybe more understandable

By Leitner C S E S

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Aug 29, 2017

Excellent course. But -1 for using TensorFlow, a not-really-free framework, to introduce students to them.