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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

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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1276 - 1300 of 7,253 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Oleksii M

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Jan 31, 2018

Thank you guys for the great course! The small typos in the assignment only make it more interesting :) But definitely should be easily to fix.

By Kyung-Hoon K

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Nov 19, 2017

Introduction on TensorFlow will help me with practical applications. As always, thanks for the best course, Andrew Ng, Mentors, and colleagues!

By Victor C

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Sep 30, 2017

Exceptional teaching and material. So often the big picture gets lost in a deep ocean of details. Somehow Andrew Ng manages to give you both.

By Viroopax M

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Mar 14, 2022

Goes into so many unique details and special coverage of the various hyperparameters and optimization techniques. Very useful for DL learners.

By Edwin C L

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Aug 27, 2021

Excellent course to learn about hyperparameters tuning (basics) and a gentle introduction to Tensorflow 2.x. Thanks for spread this knowledge!

By Ashik

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Apr 17, 2021

it was a really good introductory course for beginners , the clear explanation and programming exercises gave me a firm grip on the concepts .

By Milton E G L

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Jun 1, 2020

Es un curso pleno en temas de regularizacion y optimizacion. Provee información clara, ejemplos y ejercicios que permiten la aplicacion de DNN

By P S P 4 B E E

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Mar 27, 2020

This is the best course ever on hyperparameter tuning and regularization as well as optimization. A big thanks to Andrew Ng sir and his team.

By hiten s

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Sep 7, 2019

the grate content provie by the deeplearning.ai and the intiuation of the different concept of deep learning is teaching amazing by Andrew Ng.

By Fadel V S

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Jun 18, 2019

Amazing course so far! Loving every minute of it. Informative, challenging yet easy to understand at the same time. Highly recommend doing it!

By Ahmet

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Feb 22, 2019

This is the first time, i have learned how the softmax classification, batch normalization, deep nn with tensorflow works, thank you Prof. Ng.

By Sayan S

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Apr 2, 2018

Good learning. My Python is getting a tad better now (I was new when I started this course and have been practicing Python on the sidelines s

By Benji T

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Feb 17, 2018

Moves from theory to practice. Video pacing is good , Assignment has some slight mistake though, but all in all, one of the best course for DL

By Brian W

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Aug 18, 2017

Another great class, the final programming assignment is tricky and requires one to get a strong grasp of tensorflow (which is a great thing).

By Hikmet H

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Oct 25, 2021

Everything is perfect. The writings of instructable are non-readable though. Better to take notes yourself, instead of relying to pdf slides.

By Quan T

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Aug 28, 2020

Thank you so much, because of this course I can understand alot of knowledge. I hope to learn many things new from this course in the future.

By Rahul _

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Aug 23, 2020

I really enjoyed learning this course. I got a even more deeper understanding about the NN, how to improve them and also how to implement it.

By Alan K F G

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Aug 8, 2020

These two courses helped me a lot to undestand how programming frameworks work under the hood. Professor Andrew Ng is such a good instructor!

By Ayush G

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May 20, 2020

Excellent course! I liked the way optimization algorithms were taught. Although a bit more explanation could have been given on TensorFlow...

By mohamed a M

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Apr 30, 2020

a little harder than the first course, there are many details but Mr Andrew is just awesome as always thanks for making this course available

By James H

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Feb 19, 2020

Excellent instruction from Dr. Andrew Ng. He did an excellent job show mathematically and by coding how a deep neural networks step by step.

By Nikhil N

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Feb 12, 2020

Amazing course! Really helped me to understand the intricacies behind neural network workings and how to exploit them to improve performance.

By Pradeeshwar J

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Dec 16, 2019

This course really helps with improving neural network as suggested by the name. Tensorflow basics are also taught in this course at the end.

By Stephen V

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Jul 23, 2019

DEAR TEAM OF DEEP AI SPECIALISATION AND DEAR DR. NG !

THANK YOU VERY MUCH!

I'VE USED YOUR NOTEBOOKS AS A TOOL FOR BUILT MY OWN NEURAL NETWORKS

By bryanyau

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Jun 20, 2019

Through tackling more advanced concepts of neural network optimization, the course actaully runs you through the fundamental concepts as well