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Andrew Ng,
Kian Katanforoosh,
and
Younes Bensouda Mourri
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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/34q7py/improving
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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.
Read more
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.
Read more
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
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
Save this course
Save Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization to your list so you can find it easily later:
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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. :)"
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization with these
activities:
Review Python Basics
Show steps
Refreshing your fundamentals of Python syntax and basic data structures will allow you to quickly grasp the more complex concepts in deep learning.
Browse courses on
Python Syntax
Show steps
-
Revisit the Python documentation for basic syntax
-
Solve a few coding challenges on a platform like LeetCode using Python
Explore TensorFlow Tutorials
Show steps
TensorFlow is a powerful deep learning framework. These tutorials will help you get started with TensorFlow and learn how to use it to build and train neural networks.
Browse courses on
TensorFlow
Show steps
-
Go through the official TensorFlow tutorials
-
Build a small project using TensorFlow
Build a Neural Network from Scratch
Show steps
Building a neural network from scratch will give you a deep understanding of the inner workings of these models and help you troubleshoot issues more effectively.
Browse courses on
Neural Networks
Show steps
-
Choose a simple neural network architecture, such as a perceptron or a fully connected network
-
Implement the forward and backward passes in your code
-
Train your neural network on a dataset
Three other activities
Expand to see all activities and additional details
Show all six activities
Read 'Deep Learning' by Ian Goodfellow
Show steps
This book provides a comprehensive overview of deep learning concepts and techniques. Reading it will give you a strong theoretical foundation in deep learning.
View
Deep Learning
on Amazon
Show steps
-
Read through the book thoroughly
-
Summarize the main concepts of each chapter
Participate in a Kaggle Competition
Show steps
Participating in Kaggle competitions will give you hands-on experience in solving real-world deep learning problems and collaborating with other data scientists.
Browse courses on
Kaggle
Show steps
-
Choose a Kaggle competition that aligns with your interests and skill level
-
Build a model and submit it to the competition
-
Analyze the results and improve your model
Mentor Junior Deep Learning Students
Show steps
Mentoring others not only strengthens your own understanding of deep learning, but also helps you develop your communication and leadership skills.
Browse courses on
Mentoring
Show steps
-
Volunteer as a mentor for an online community or local organization
-
Provide guidance to junior deep learning students and answer their questions
Review Python Basics
Show steps
Refreshing your fundamentals of Python syntax and basic data structures will allow you to quickly grasp the more complex concepts in deep learning.
Browse courses on
Python Syntax
Show steps
Show steps
Show steps
- Revisit the Python documentation for basic syntax
- Solve a few coding challenges on a platform like LeetCode using Python
Explore TensorFlow Tutorials
Show steps
TensorFlow is a powerful deep learning framework. These tutorials will help you get started with TensorFlow and learn how to use it to build and train neural networks.
Browse courses on
TensorFlow
Show steps
Show steps
Show steps
- Go through the official TensorFlow tutorials
- Build a small project using TensorFlow
Build a Neural Network from Scratch
Show steps
Building a neural network from scratch will give you a deep understanding of the inner workings of these models and help you troubleshoot issues more effectively.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Choose a simple neural network architecture, such as a perceptron or a fully connected network
- Implement the forward and backward passes in your code
- Train your neural network on a dataset
Three other activities
Expand to see all activities and additional details
Show all six activities
Read 'Deep Learning' by Ian Goodfellow
Show steps
This book provides a comprehensive overview of deep learning concepts and techniques. Reading it will give you a strong theoretical foundation in deep learning.
View
Deep Learning
on Amazon
Show steps
Show steps
Show steps
- Read through the book thoroughly
- Summarize the main concepts of each chapter
Participate in a Kaggle Competition
Show steps
Participating in Kaggle competitions will give you hands-on experience in solving real-world deep learning problems and collaborating with other data scientists.
Browse courses on
Kaggle
Show steps
Show steps
Show steps
- Choose a Kaggle competition that aligns with your interests and skill level
- Build a model and submit it to the competition
- Analyze the results and improve your model
Mentor Junior Deep Learning Students
Show steps
Mentoring others not only strengthens your own understanding of deep learning, but also helps you develop your communication and leadership skills.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- Volunteer as a mentor for an online community or local organization
- Provide guidance to junior deep learning students and answer their questions
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 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.
Machine Learning Engineer
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.
Business Analyst
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.
Operations Research Analyst
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.
Statistician
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.
Software Engineer
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.
Data Analyst
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.
Financial 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.
Data Engineer
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.
Quantitative Analyst
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.
Actuary
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.
Economist
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.
Software Developer
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.
Market Researcher
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.
User Experience Researcher
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.
For more career information including salaries, visit:
OpenCourser.com/course/34q7py/improving
Reading list
We've selected seven books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization.
Save
Provides a comprehensive overview of deep learning, covering the latest techniques and applications. It valuable resource for anyone interested in learning more about this rapidly growing field.
Provides a comprehensive overview of deep reinforcement learning. It valuable resource for anyone who wants to learn more about this rapidly growing field.
Provides a practical guide to machine learning with Scikit-Learn, Keras, and TensorFlow. It great resource for anyone who wants to learn more about machine learning and how to use it to build and train machine learning models.
Provides a practical guide to machine learning in finance. It great resource for anyone who wants to learn more about machine learning and how to use it to build and train machine learning models for financial applications.
Provides a practical guide to machine learning with R. It great resource for anyone who wants to learn more about machine learning and how to use it to build and train machine learning models in R.
Provides a practical guide to deep learning with Python. It great resource for anyone who wants to learn more about deep learning and how to use it to build and train deep learning models in Python.
Provides a practical guide to deep learning for computer vision. It great resource for anyone who wants to learn more about deep learning and how to use it to build and train deep learning models for computer vision tasks.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/34q7py/improving
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Effort
At the rate of 5 hours a week, it typically takes 5 weeks to complete this course
Level
Intermediate
Via
Coursera
Institution
DeepLearning.AI
Instructors
Andrew Ng
Kian Katanforoosh
Younes Bensouda Mourri
Language
English
Transcripts
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آذربایجان دیلی
This course belongs to a
collection
called
Deep Learning. It's comprised of five courses:
- Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization (viewing)
- Structuring Machine Learning Projects
- Convolutional Neural Networks
- Sequence Models
- Neural Networks and Deep Learning
Good to know
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
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