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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
57,290 ratings
6,578 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

NA
Jan 13, 2020

After completion of this course I know which values to look at if my ML model is not performing up to the task. It is a detailed but not too complicated course to understand the parameters used by ML.

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

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6426 - 6450 of 6,499 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Chaobin Y

Nov 3, 2017

Maybe this course can merge with the 1st one. they both cover too little materials.

By AHMED A H P

Sep 12, 2020

WEEK-3 was a little bit messy, it would have been better if it was tensorflow 2

By joel a

Apr 25, 2020

taught concepts well, but the programming assignments felt like it was spoonfed

By Xin H

May 12, 2018

Good: Contents on Tensor Flow

Bad: No real useful content compared the Course 1.

By Péter D

Oct 6, 2017

great lectures, simplistic programming assignements, ridiculously easy tests

By SAMBATH S

Aug 2, 2020

It would be better to use TF2 as there are lots of changes in the usages.

By 笛 王

Jan 18, 2018

Harder to understand. Overall quality is not as good as the first class.

By Kenneth Z

Mar 20, 2018

It is a bit abrupt to jump into tensorflow without explaining in depth.

By Rishab K

Apr 17, 2020

good course to learn, but more assignments should be introduce n week3

By Rajat K S

Jan 11, 2020

Most of the solutions to the assignment were written in instructions.

By Ganesan G

Dec 28, 2017

I am not getting to see the programming exercises that i have done :(

By Jonghyun K

Apr 25, 2020

voice was too small compared to noises made by clothes and others.

By FREDERIC T

May 13, 2018

Good courses, the sound quality is very poor (high tone noise).

By Morisetty V A S K

Jan 20, 2019

Interface for evaluating is not great and assignments are easy

By Alex I E

Sep 4, 2017

The Tensorflow part should have started sooner in the course.

By Aloys N

Jul 1, 2019

We could have more guidance on setting a tensorflow model

By HAMM,CHRISTOPHER A

Apr 30, 2018

Lots of theory and not enough practical implementation.

By Stefan S

Sep 22, 2020

Content starts to feel old, but still interesting.

By Hasnaa

Feb 10, 2020

the circulum was some hard and over detailed

By luca m

May 5, 2020

I would have loved to have a session on TF2

By Kenneth C V

Aug 29, 2019

Course is a bit complex due to the subject

By Kartheek

Feb 1, 2019

week 3 topics would have been a bit better

By Tushar B

Jun 12, 2018

Assignments vs lecture, difference is huge

By Aashita G

Jun 1, 2020

fast paced not enough emphasis on topics

By Amod J

Mar 18, 2018

Want to download my own work but cannot.