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

4.9
stars
62,820 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

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

AB

Aug 26, 2021

Amazing course which focus on the theoretical part of parameters tuning, but it needs more explanation of Tensorflow, as I felt a little lost in the last project. Except that, it is an amazing course.

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301 - 325 of 7,216 Reviews for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

By Steve S

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Dec 11, 2017

Provided a lot of deeper insights passed over in the previous course in the specialization. Between this course and the previous course, you feel like you have a very solid beginner's understanding of deep learning, but one that is also practical enough and comprehensive enough to start coding on your own.

By Marcin G

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Oct 15, 2017

Andrew Ng is a great teacher and will get you excited about improving deep networks. In this course you will get to know how to increase performance of your network. Essential course for deep networks specialists and amateurs. Additionally you will get to know most influential people befind the technology.

By Shashank S S

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Jul 8, 2019

All possible area of Improving Deep Learning models got covered in detail. I liked the lucid and intelligible way of explanation . Since the topics were vast to cover , I would recommend to get the course extended by 1 week with one more programming assignment on using tensor-flow with a capstone project.

By Vincenzo M

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Sep 11, 2017

This course will becoma a foundamental course for people that aim to work in the machine learning / deep learning area because it presents clearly the recent innovations in the deep learning. For production environment people will probably use open source framework, but this course clarify what is behind.

By Lily Z

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May 26, 2023

Excellent teaching . After learning, students  have direction for improving deep neural network, such as algorithms for optimization, order and scaling of hyperparameter.   understand how batch  norm   and how mini-batch works, as well as how to handle high bias and high variance  in the neural network.

By Joshy J

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Oct 3, 2019

Excellent course if you are passioned about Deep Learning. Walk you through the most basics on how to tune the model parameters so that you can reach the highest accuracy for the model. The lecture is simple and well ordered. The TensorFlow introduction part is more exciting. Overall a wonderful course.

By Dimitrios L

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Feb 18, 2018

Excellent course! Not only does it address critical deep-NNs training issues providing a clear exaplanation around why these tunings are needed, but also provides some empirical advices (e.g. on level of importance on the hyper-parameters, typical values etc) that can be valuable when training depp NNs.

By Aaron B

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Oct 27, 2018

The only thing I wish for is a 'live chat' when an instructor is available, a IRC/slack/chat room for students to help each other, or faster response time when posting to the forums. Also the forums are a bit clunky (I don't remember all the reasons why), but the search allowed me to find useful posts.

By Shashank M

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Oct 10, 2018

This course offers a very quick introduction to methods that could be used to improve usage of deep nets from a practitioner's perspective. Although the mathematical details are not covered in depth, the material furnishes concise list of topics that could be researched upon for in-depth understanding.

By G

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Dec 9, 2021

This is a nice add-on to more basic courses about Neural Networks. It was great getting an understanding why regularization works and to see some different optimization schemes. Also interesting to see batch normalization rather than just normalizing the input data. I strongly recommend this course.

/G

By Sachin W

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Dec 11, 2018

Amazing course, starts right off the bat with hyperparameters, regularization and tunings.

Studied about various optimization algorithms and normalization alongwith mini batches, also the TensorFlow framework.

Thank you to everyone involved in making this course. I highly appreciate what you've made us.

By Muhammad s k

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Dec 3, 2019

I always held an opinion that highly qualified instructors, specifically those holding doctorate degrees are not the good teachers because they can't teach students at their levels. But Sir Andrew Ng proved me wrong, he is a wonderful teacher and tries to explain the minute details.

Salute to you sir.

By Edoardo S

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Jan 20, 2019

Very impressive course, really well done and interesting. One suggestion: apart from the modelling part in the programming assignment, I would also introduce some coding about the computing of the results and the final cost plot (in all the programming assignment these parts are already pre-compiled)

By Shabie I

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Feb 18, 2018

Concepts buried deep in technical jargon and seemingly complex mathematical notation are laid out bare for everyone to understand.

Mr. Andrew Ng is a very special teacher. The humility and down-to-earth character also add immense value to the course. He makes you believe truly that you too can do it.

By Brandon E

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Sep 26, 2017

An excellent continuation of the series. I particularly liked the in-depth discussion of Adam's optimization and the introduction to TensorFlow at the end of the course. The course does a great job of targeting specific concepts with practical advice related to tuning and optimization on real models.

By Kwan T

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Sep 28, 2017

It is amazingly rewarding to learn from Andrew, who is able to articulate so much insights into so many complicated refinements of Deep Neural Networks from so many different research papers. The Tensorflow programming assignment is one of best tutorials I have seen. Thank you for your great effort.

By Olivera N

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Apr 13, 2021

Really great experience taking this course! Truly diving in the area with many details. When I came to the last programming exercise with TensorFlow, I started to really appreciate the software frameworks that allow you to use predefined procedures instead of having to code everything from scratch.

By Zhiming C

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Apr 18, 2020

A very good organize course! The knowledge is step by step introduced. From Python can one from scratch a learning code establish. And then the course turns into Tensorflow. Only with this method can man have good feeling about how Tensorflow is processed. Very good course, I strongly recommend it!

By Benjamín M

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Apr 19, 2020

Concepts very well introduced and explained, with really good explanations about the intuition behind every topic. It's perfect to be able to apply different techniques knowing what they are good for and when to apply them, and at the same time it also shows where to delve deeper if needed/wanted.

By Nityesh A

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Oct 8, 2017

Andrew Ng gives a good satisfactory explanation of the techniques covered in this course. He explains when to use the technique, how to use the technique and how one can implement it in Python and then goes on to give an intuition behind it. I think it should work well for newbies (worked for me).

By Tejaswini

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May 25, 2020

I've really enjoyed this course. It gives you a great deal of knowledge and I recommend this to anyone who wants to get an intuition of how to optimise, regularise and perform hyper parameter tuning to make your model learn efficiently. The variety of topics and depth offered was good. Thank you.

By Rujuta V

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May 18, 2020

This was an extremely informative course which provided an in-depth knowledge of how Hyper-parameters of Neural Network affect the results and methods of tuning those parameters from improving results. The Programming Assignment provides deeper insights of applying the taught methods effectively.

By Gary K N

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Mar 5, 2020

This course adds to the first with what you need to make models perform well and fast in practice. Each part of the learning process has possible tuning, tweaks, optimizations to improve performance. The material explains why each tweak works, at least at an intuition level. I have learned a lot.

By Eddie C

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Feb 18, 2019

My second AI course certificate from Andrew Ng after I left Taiwan AI Labs. Even though it took me more than 2 months to complete because of my kids' winter vacation and Chinese New Year break. I did learn a lot about how to tune and optimize a Deep Learning network. Keep going to the 3rd course.

By Shah M D

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Jan 20, 2019

Great Course. This course does explain some optimisation algorithm with quit a good detail. That is a good part of it. Many less courses explain those algorithms at a level of abstraction an undergraduate student needs. Also, it shows the usage of tensorflow, which is used by major practitioners.