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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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Rating 4.8 based on 2,119 ratings
Length 4 weeks
Effort 3 weeks, 3-6 hours per week
Starts Feb 11 (10 weeks ago)
Cost $49
From 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

We analyzed reviews for this course to surface learners' thoughts about it

neural network in 145 reviews

This is a follow up course to Neural Networks and Deep Learning so you must start with the latter.

Then I will keep moving forward in AI industry based on your guiding afterward and aggressively build up personal AI research ability after courses.Gary Learning so much about how to optimize neural networks!

Very nice course about important subjects of Vanilla Neural Networks, as optimizations algorithms , regularization methods, hyper-parameters used and how to implement them in practice.

learnt how to tune neural network.

In the process ,l learn so much about code to improve deep neural network.

very good course very good Some errors in jupyter notebooks Awesome course I recommend everyone to go through this course if you really want to learn detail about hyperparameter tuning , optimizers and regularization used to make neural network better.

It helps to open black box of Neural network and know in detail about how all works.

I have learnt a lot from all of the sessions and now I am getting more and more confident in building neural network by myself.

batch norm in 34 reviews

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

Very good series of course Excellent Course It helps me understand Regularization, Dropout, AdamOptimization algorithm, Batch Normalization, Softmax classifier which have bothered me for a long time.

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

Batch Normalization4).

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!

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.

tensor flow in 27 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.

they should have given more lectures on tensor flow but still it is a nice course briefly explained Good course but it would be interesting to add some other methodologies on learning rate ("Cyclical Learning Rates for Training Neural Networks", "Snapshot ensembles") and some explanations on categorical variables and embeddings matrix ("Entity Embeddings of Categorical Variables") It's completely 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.

deep neural networks in 25 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.

Love the orthogonalization part and the explanation on why training deep neural networks is possible (local minimum is rare in hyperspace; for the most part there are saddle points).

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.

batch normalization in 24 reviews

This course is a depository of tools that helps your NN be more perfect.Batch normalization is pretty sophisticated topic.

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).

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.

In this course I learned or reviewed the following items in enough depth to perform in a job with them:InitializationRegularizationBatch NormalizationOptimizersTensorFlow Really well structured course.

week 2 was extremely boring I particularly benefited from the explanations of dropout, batch normalization and the RMSProp/Adam optimisers.

Super good course, would be greater if the programming assignments can cover batch normalization part instead of just TensorFlow.

It is the best course in introduction to tensorflow and batch normalization hands down.Although the minimal mathematical approach is followed still the course hits the conceptual homerun.Absolutely brilliant More programming exercises for Tensorflow may be advantageous from learning point of view.

regularization and optimization in 22 reviews

Great course where we understand how to tune hyperparameters and how to improve our algorithms with regularization and optimization techniques.

Those familiar words, Hyperparameter, Regularization and Optimization, from the Machine Learning area, are all introduced by this course and the techniques will be used by the comming next courses.

Although they were stronger on Regularization and Optimization than on Hyperparameter tuning, one cannot go about using ML/DL without those concepts amalgamated.

Lots to learn about parameters that effect the neural network and various regularization and optimization techniques for neural network.

Thanks for this amazing course, I learnt a lot about Deep Learning specifically Regularization and Optimization methods.

I would not imagine starting a Deep Learning project without knowing about Hyperparameter tuning, Regularization and Optimization.

I really learned lots of things on Deep Learning, especially on hyperparameter tuning, Regularization and Optimization.

I suppose Hyperparameter tuning, Regularization and Optimization are some of the most important aspects of Deep Learning, since 90% of most of the DL projects come down to just that.


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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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Rating 4.8 based on 2,119 ratings
Length 4 weeks
Effort 3 weeks, 3-6 hours per week
Starts Feb 11 (10 weeks ago)
Cost $49
From 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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