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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
stars
63,243 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

CM

Dec 23, 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

Thanks.

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6576 - 6600 of 7,261 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

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 Snehitha D

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Jun 20, 2024

the concepts are a little complex and tricky and i hope there's a project by the end of the specialization

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.

By Jayshree R

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Jul 4, 2019

An intuitive approach towards Hyper parameters. Covers the concept of optimization algorithms quiet well.

By Makragić A

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Jan 9, 2019

Great lectures, I'm little disappointed with TensorFlow tutorial, there should be 1 week for that only...

By Richard H

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Sep 28, 2017

Fills in the tricky gaps in using DNN that are necessary to transition from basics to practical projects.

By Harry L

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Mar 21, 2020

Too much code is given, which makes the programming assignments too easy. The material is great, though.

By Shijian G

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Nov 29, 2019

These series are generally clear and well-organized. It would be better to provide tensorflow materials.

By Kevin T

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Mar 29, 2023

The assignments could have been explained a little bit better. The course was overall very interesting.

By Joseph A

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Dec 6, 2020

The course was very great, although I feel I needed a better explanation about tensorflow functionality

By Tien N V

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Jun 24, 2020

This course so very good for person who wants to understand the optimization topic in machine learning.

By Tanmay

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Jun 2, 2020

Nice content; creators must try to focus on enhancing the confidence of learners to code by themselves.

By Ranjan D

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Jul 17, 2019

Great explanation on tuning different hyper parameters and how they can effect the model's performance.