Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization from Coursera

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.

What's inside

Syllabus

Practical Aspects of Deep Learning

Discover and experiment with a variety of different initialization methods, apply L2 regularization and dropout to avoid model overfitting, then apply gradient checking to identify errors in a fraud detection model.

Optimization Algorithms

Develop your deep learning toolbox by adding more advanced optimizations, random minibatching, and learning rate decay scheduling to speed up your models.

Hyperparameter Tuning, Batch Normalization and Programming Frameworks

Explore TensorFlow, a deep learning framework that allows you to build neural networks quickly and easily, then train a neural network on a TensorFlow dataset.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Focuses on the practical side of deep learning, such as initialization methods, regularization, and hyperparameter tuning

Includes implementation of a neural network in TensorFlow, a popular deep learning framework

Taught by Andrew Ng, a leading researcher and pioneer in deep learning

Part of the Deep Learning Specialization, providing a comprehensive pathway for learning deep learning

Assumes some background knowledge in deep learning, making it suitable for intermediate learners

May require additional time and effort to complete the hands-on exercises

Reviews summary

Practical deep learning tricks

learners say this course is largely positive, engaging, challenging, and useful not only for beginners but also for students at an intermediate or advanced level. Overall, they recommend it to anyone interested in learning more about neural networks and deep learning.

tuning parameters, initialization, batch normalization, optimization, etc. All the knowledge are well explained both intuitively and mathematically. Always enjoyable to learn from Andrew Ng.

"This was an interesting and challenging course. Andrew gives good intuitions about the fundamentals of improving deep neural networks."

"I benefit a lot from this course regarding parameter initialization, hyperparameter optimization, batch normalization, optimization, etc."

"Great course. It goes over many practical method to speed up the training process and also does an excellent job at explaining why these algorithm work."

"This is one of the best courses on Coursera. Cleared a lot of concepts. Before this course, I was always thinking, what to do if I had to classify among multiple classes, but the explanation of softmax was actually very helpful in answering that question."

"Not long ago I have been working with machine learning and artificial networks, but without a doubt so far I think my learning curve has been exponential. Themed content is for anyone to fall in love with technology and climb on the shoulders of giants."

Andrew Ng is one of the best tutors one could get. The only reason I rate it with 4 stars is that the TF assignment is not updated to TF2. TF 2.0 with Keras really is a state-of-the-art framework and imho there is not much value in learning TF1 anymore.

"Although this particular course is not as sexy in its applications as the others it is still vital information for any serious practicioner. Prog Ng shares his years of experience and you really feel that with each video you are learning invaluable tips and tricks."

" This course is very thorough and detailed. Now I can clearly and confidently say that I can perform good research and obtain formal information and data on any topic, as opposed to just surfing the internet for genuine knowledge. Great course, well done to Andrew. "

"I found this course extremely helpful. It enabled me to develop a really good intuition about deep learning models and what are the small steps that go a long way in improving the overall performance of the system. I hope all of you find this helpful too."

"Excellent course, giving a very good insight into how to approach building a deep neural network, the concepts of various parameters, tips on how to best achieve a good algorithm and a step by step walk through of the different algorithms, parameters and optimization."

"Excellent course. A great way to understand the fundamentals. It's always good to understand what's under the hood as frameworks abstract away a lot of the hard work going on underneath. Also makes you aware of how to be better tune and understand hyperparameters etc."

The section with which the code structure for a neural network learned in the previous course can be directly translated into the TensorFlow structure intuitively, which makes it easier to learn and to understand.

"This was the second course of my deep learning specialization. So far, I have been able to get a complete grasp on how to tune the DNN hyperparameters and apply different methods like regularization effect on your parameters to further improving the Neural Network."

"The Deep Learning course is in great flow and can't get any better than this. I highly recommend deeplearning.ai specialization on Coursera for all the aspiring Deep Learning practitioners. Trust me you'll learn everything, right from the fundamentals to the advanced topics."

"The most exciting part of this course is the exercise in the last week. The Python/Numpy code structure for a neural network learned in the previous course can be directly translated into the TensorFlow structure intuitively, which makes it easier to learn and to understand."

"I have really learned a lot of things! It surely took 3 weeks to complete all the things, it was tough at some points, but if I didn't do this course, I might have some regrets that I didn't achieve all the knowledge. Thanks to Mr. Andrew, he is really a very good teacher. "

"It is amazing how concepts can be made so clear that implementing them by hand seems so easy. Love how the instructions are commented in the programming assignment. It helps you complete the assignment easily, yet giving such an extreme feeling of fulfilment and accomplishment. :)"

Career center

Learners who complete Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists analyze data, and communicate findings to stakeholders, in order to solve business problems. This course may be useful because it provides a foundation in Deep Learning, a subfield of Machine Learning that is essential for many data science applications.

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Machine Learning Engineer

Machine Learning Engineers build and maintain machine learning models. This course may be useful because it helps build a foundation in Deep Learning, a subfield of Machine Learning.

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Business Analyst

Business Analysts use data to solve business problems. This course may be useful because it provides a foundation in Deep Learning, which can be used to build predictive models for customer behavior and other business data.

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Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to solve business problems. This course may be useful because it provides a foundation in Deep Learning, which can be used to solve complex optimization problems.

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Statistician

Statisticians use mathematical and statistical models to analyze data and make predictions. This course may be useful because it provides a foundation in Deep Learning, which is a powerful tool for data analysis.

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Software Engineer

Software Engineers design, develop, test, and maintain software systems. This course may be useful because it provides a foundation in Deep Learning, which is becoming increasingly important in software development.

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Data Analyst

Data Analysts use data to solve business problems. This course may be useful because it provides a foundation in Deep Learning, which can be used to build predictive models for customer behavior and other business data.

See salaries and explore the career path for Data Analyst

Financial Analyst

Financial Analysts use financial data to make investment recommendations. This course may be useful because it provides a foundation in Deep Learning, which can be used to build predictive models for stock prices and other financial data.

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Data Engineer

Data Engineers design, build, and maintain data systems. This course may be useful because it provides a foundation in Deep Learning, which is becoming increasingly important in data engineering.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze data and make predictions. This course may be useful because it provides a foundation in Deep Learning, which is a powerful tool for data analysis.

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Actuary

Actuaries use mathematical and statistical models to assess risk. This course may be useful because it provides a foundation in Deep Learning, which can be used to build predictive models for risk assessment.

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Economist

Economists use economic data to analyze and solve problems related to the economy. This course may be useful because it provides a foundation in Deep Learning, which can be used to build predictive models for economic data.

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Software Developer

Software Developers design, develop, test, and maintain software systems. This course may be useful because it provides a foundation in Deep Learning, which is becoming increasingly important in software development.

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Market Researcher

Market Researchers use data to understand consumer behavior and make marketing recommendations. This course may be useful because it provides a foundation in Deep Learning, which can be used to build predictive models for customer behavior.

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User Experience Researcher

User Experience Researchers use data to understand how users interact with products and services. This course may be useful because it provides a foundation in Deep Learning, which can be used to build predictive models for user behavior.

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