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Improving Deep Neural Networks

Hyperparameter tuning, Regularization and Optimization

This course is a part of Deep Learning, a 5-course Specialization series from Coursera.

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.

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deeplearning.ai

Rating 4.8 based on 3,411 ratings
Length 4 weeks
Effort 3 weeks, 3-6 hours per week
Starts Jun 3 (3 weeks ago)
Cost $49
From deeplearning.ai via Coursera
Instructors Andrew Ng, Head Teaching Assistant - Kian Katanforoosh, Teaching Assistant - Younes Bensouda Mourri
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Science Algorithms Machine Learning

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What people are saying

According to other learners, here's what you need to know

andrew ng in 196 reviews

Andrew Ng's simple explanations made learning and grasping new concepts easier than ever.

Andrew Ng is as always very precise about the issues presented and helps build up our knowledge step-by-step in a super structured way.

ike usual andrew ng perfect explanation simple go to essential stuff.the minus points some troubles with notebookbig thanks for andrew ng's team.

Course if full of rare intuitions you could get only from someone like Andrew Ng.

宝贵的超参数调试经验,谢谢分享 Prof. Andrew Ng is fantastic at explaining complex ideas in a systematic, easy to understand way.

Great course, Andrew Ng is just the best teacher for NN and machine learning.

Andrew Ng does a GREAT job of breaking things down and making it easy.

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batch norm in 48 reviews

A lot of useful info regarding ADAM , RMSprop and Batch Norm.

Struggled a bit to grasp the batch nomalization, Initially Regularization was also hard to grasp the first time, subsequent viewing made it clear though but batch norm still is a bit hazy.

However, as I am currently not utilizing large datasets, the material on Batch Normalization and Hyper-parameter tuning is not useful to me ( now and in the foreseeable future).

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

we didn't try to implement the batch normalization ourselves and to incorporate batch normalization with other parameters etc.

It would be nice to include more details about the implementation of batch normalization.

Covers batch norm and optimizers nicely.

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deep neural networks in 45 reviews

For the first time, I now have a better intuition for optimizing and tuning hyperparameters used for deep neural networks.I got motivated to learn more after completing this course.

More pragmatic approach with theorems would be more appealing....or maybe it is me as i'd prefer Java (DL4J)...not sure Overall very good course where you quickly gain an overview of current optimization techniques for Deep Neural Networks Fantastic course.

The course make sense of all you learned previously on deep neural networks.

It is really great and it help you to get starting to Tensorflow True to the claimed learning objectives, the course Improving Deep Neural Networks shows some of the magic behind deep learning algorithms.

Awesome course by awesome teacher :)) Great Course and great learning 好 Very informative course on tuning the deep neural networks.

Having introduced the building blocks of deep neural networks, in this course Andrew teaches more advanced and practical concepts - like: regularization, advanced optimization techniques, batch-normalization, etc - that can significantly improve the implementation of the models we build.Also, in this course we get to learn TensorFlow, a widely used and wonderful deep learning framework.I highly recommend this course.Thank you Andrew & Co. :-) Good demonstration It was very helpful to grasp the differences in the concept of 'Normalization, Regularization and Optimization' Thank you for clarifying the most relevant techniques for improving the methods.

Excellent course and explanations on fundamental concepts for improving deep neural networks.

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tensor flow in 44 reviews

Good: Contents on Tensor FlowBad: No real useful content compared the Course 1.

Great introduction to hyperparameters adjustment and Tensor flow Excelent course, very useful.

The class should include more introduction on the current ml frameworks such as tensor flow etc.

But anyway, this course is already good enough A very nice course providing intuitions and concepts for tuning the hyper parameters in a neural network.Also, provides a taste of using Tensor Flow (Neural Network Framework) in a comprehensive manner.

this course is very good it gives good practical ways to implement neural networks in python and with Tensor Flow, i like it very much I REALLY ENJOYED IT !!!!

nice I would like to have a brief introduction to Tensor Flow or a simple beginners tutorial (at least to have it clearer the usage of variables, constants and placeholders) Practical with lots of helpful tips.

Nice introduction to hyper-parameter tuning and tensor flow.

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batch normalization in 33 reviews

Grate course, only criticism is that week 3 didn't thoroughly explain how batch normalization parameters (gamma and beta) get updated during gradient descent.

It was very helpful to learnt batch normalization, regularization and tensorflow.

Batch Normalization4).

Some more advanced topics are presented that students don't typically learn on coursera courses, such as improvements to gradient descent, batch normalization, and dropout.

Contains very good understanding of Hyperparameters and their tuning process.Secondly, teaches very well the mathematics of optimizers such as GD, SGD, GD with Momentum, GD with RMSProp and ADAM.Finally, a small glimpse of Batch Normalization.Highly Recommended!!!!!!

Great Very good course, although it'd be awesome if Andrew went over the backprop associated with Batch Normalization and perhaps a programming example of using Batch norm on my test set.

A very well curse, For me the information of batch normalization is very important Content great!

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highly recommended in 30 reviews

Highly recommended.

It is a highly recommended course for those who want to understand what is happening under the hood when using a neural network framework, like tensorflow.

Highly recommended!

highly recommended.

Highly Recommended to anyone who wants to get in the field of Deep Learning.

Highly recommended..!

Although the material seems like quite condensed and forced in a very short span of time (3 weeks) while the easier and more basic concepts in course 1 where explained at the right pace during 4 weeks.These are core concepts and techniques for practical day-by-day deep learning engineering and programming and I would have wanted them to not to be taught in such a rush.Even though, highly recommended course...

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Reviews

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Guest says:

For what this course costs, it's almost a no brainer. You can get the same information scattered across the internet or parts of this course in an expensive textbook, but don't. Andrew Ng has already gathered the very best methods and tools and placed them right in front of you to implement structurally sound NN projects. Strongly recommended.

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deeplearning.ai

Rating 4.8 based on 3,411 ratings
Length 4 weeks
Effort 3 weeks, 3-6 hours per week
Starts Jun 3 (3 weeks ago)
Cost $49
From deeplearning.ai via Coursera
Instructors Andrew Ng, Head Teaching Assistant - Kian Katanforoosh, Teaching Assistant - Younes Bensouda Mourri
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Science Algorithms Machine Learning

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