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Ryan Ahmed
In this 1-hour long project-based course, you will be able to:
- Understand the theory and intuition behind Deep Learning, Convolutional Neural Networks (CNNs) and Residual Neural Networks.
- Import Key libraries, dataset and visualize images.
- Perform data augmentation to increase the size of the dataset and improve model generalization capability.
- Build a deep learning model based on Convolutional Neural Network and Residual blocks using Keras with Tensorflow 2.0 as a backend.
- Compile and fit Deep Learning model to training data.
Read more
In this 1-hour long project-based course, you will be able to:
- Understand the theory and intuition behind Deep Learning, Convolutional Neural Networks (CNNs) and Residual Neural Networks.
- Import Key libraries, dataset and visualize images.
- Perform data augmentation to increase the size of the dataset and improve model generalization capability.
- Build a deep learning model based on Convolutional Neural Network and Residual blocks using Keras with Tensorflow 2.0 as a backend.
- Compile and fit Deep Learning model to training data.
Read more
In this 1-hour long project-based course, you will be able to:
- Understand the theory and intuition behind Deep Learning, Convolutional Neural Networks (CNNs) and Residual Neural Networks.
- Import Key libraries, dataset and visualize images.
- Perform data augmentation to increase the size of the dataset and improve model generalization capability.
- Build a deep learning model based on Convolutional Neural Network and Residual blocks using Keras with Tensorflow 2.0 as a backend.
- Compile and fit Deep Learning model to training data.
- Assess the performance of trained CNN and ensure its generalization using various KPIs.
- Improve network performance using regularization techniques such as dropout.
Register for this course and see more details by visiting:
OpenCourser.com/course/8fzsxx/emotion
What's inside
Syllabus
Facial Key-point Detection
In this hands-on project, we will train deep learning model based on Convolutional Neural Networks (CNNs) and Residual Blocks to detect 15 facial key-points. This project could be practically used for detecting customer emotions and facial expressions.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops practical skills in facial key-point detection for recognizing customer emotions
Covers key concepts in deep learning and neural networks, especially CNNs and residual networks
Teaches in-demand deep learning libraries and frameworks, such as Tensorflow 2.0 and Keras
Uses the popular Python programming language, familiar to data scientists and machine learning engineers
Emphasizes practical implementation and hands-on learning through a project-based approach
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Save Emotion AI: Facial Key-points Detection to your list so you can find it easily later:
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Reviews summary
Helpful for deep learning
learners say emotion AI: Facial Key-points Detection has helpful coding explanations and confidence-building assignments. Despite being great for beginners to intermediates who want to learn and improve their Deep Learning skills, some students suggest adding video lectures and more detailed content about emotion detection.
Good for beginners and intermediates.
"Great guided project, one of the better ones at Coursera, but it can still be improved!"
"Too little for an intermediate. Also for a beginner."
Coding assignments build confidence.
"Awesome coding environment preparation and explanation by the instructor."
"I always had a problem to write my own code on Deep Learning, this has given new confidence."
"Thank you!!"
Adding video lectures would improve course.
"no video lecture for instructon"
Course doesn't cover Emotion Detection as title suggests.
"Nice guided project overall. But as the name suggests emotion AI , there was nothing about detecting emotions, just recognizing facial points"
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 Emotion AI: Facial Key-points Detection with these
activities:
Review of Basic Probability and Calculus
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Build a solid understanding of probability and calculus, which will enhance your ability to grasp concepts like uncertainty modeling and optimization.
Browse courses on
Probability
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-
Review probability distributions, such as the normal, binomial, and Poisson distributions.
-
Refresh your knowledge of differentiation and integration.
-
Practice applying these concepts to simple problems.
Deep Learning Resources Compilation
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Organize and expand your learning resources by compiling a collection of articles, tutorials, and code snippets, creating a valuable reference for future studies or projects.
Browse courses on
Deep Learning
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-
Gather and curate high-quality resources on deep learning.
-
Categorize and organize the resources based on topic or difficulty level.
-
Share your compilation with peers or the broader learning community.
Explore Convolutional Neural Networks with Keras Tutorials
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Enhance your understanding of CNNs and their implementation through hands-on tutorials, solidifying your grasp of the underlying concepts.
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Convolutional Neural Networks
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-
Follow Keras tutorials on building and training CNN models.
-
Experiment with different CNN architectures and hyperparameters.
Five other activities
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Discussion Forum Participation
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Engage in discussions with peers to clarify concepts, exchange ideas, and gain diverse perspectives, enhancing your overall understanding of the course material.
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Regularly participate in online discussion forums.
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Ask thoughtful questions and provide insightful answers to foster a collaborative learning environment.
Deep Learning Coding Exercises
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Reinforce your understanding of deep learning algorithms through practical coding exercises, improving your problem-solving skills and coding proficiency.
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Deep Learning
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Solve coding challenges on platforms like LeetCode or HackerRank.
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Implement deep learning models from scratch in Python or R.
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Participate in online coding competitions.
Attend a Workshop on Deep Learning Applications
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Expand your knowledge by attending a workshop focused on practical applications of deep learning, gaining insights from industry experts and expanding your network.
Browse courses on
Deep Learning
Show steps
-
Identify and register for a relevant workshop.
-
Attend the workshop and actively participate in discussions.
-
Follow up with the organizers or speakers to explore further learning opportunities.
Facial Key-Point Detection Project
Show steps
Apply your knowledge of CNNs and residual neural networks by building a deep learning model to detect facial key-points, deepening your understanding of practical applications.
Show steps
-
Gather and preprocess a dataset of facial images with annotated landmarks.
-
Build a CNN model using Keras with Tensorflow as the backend.
-
Train and evaluate your model using appropriate metrics.
Implement a Deep Learning Model for Image Classification
Show steps
Solidify your understanding of deep learning concepts by building a project that applies a deep learning model to classify images, enhancing your practical skills and problem-solving abilities.
Browse courses on
Deep Learning
Show steps
-
Choose a specific image classification task.
-
Collect and preprocess a dataset of images.
-
Design and train a deep learning model for image classification.
Review of Basic Probability and Calculus
Show steps
Build a solid understanding of probability and calculus, which will enhance your ability to grasp concepts like uncertainty modeling and optimization.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Review probability distributions, such as the normal, binomial, and Poisson distributions.
- Refresh your knowledge of differentiation and integration.
- Practice applying these concepts to simple problems.
Deep Learning Resources Compilation
Show steps
Organize and expand your learning resources by compiling a collection of articles, tutorials, and code snippets, creating a valuable reference for future studies or projects.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Gather and curate high-quality resources on deep learning.
- Categorize and organize the resources based on topic or difficulty level.
- Share your compilation with peers or the broader learning community.
Explore Convolutional Neural Networks with Keras Tutorials
Show steps
Enhance your understanding of CNNs and their implementation through hands-on tutorials, solidifying your grasp of the underlying concepts.
Browse courses on
Convolutional Neural Networks
Show steps
Show steps
Show steps
- Follow Keras tutorials on building and training CNN models.
- Experiment with different CNN architectures and hyperparameters.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Discussion Forum Participation
Show steps
Engage in discussions with peers to clarify concepts, exchange ideas, and gain diverse perspectives, enhancing your overall understanding of the course material.
Show steps
Show steps
Show steps
- Regularly participate in online discussion forums.
- Ask thoughtful questions and provide insightful answers to foster a collaborative learning environment.
Deep Learning Coding Exercises
Show steps
Reinforce your understanding of deep learning algorithms through practical coding exercises, improving your problem-solving skills and coding proficiency.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Solve coding challenges on platforms like LeetCode or HackerRank.
- Implement deep learning models from scratch in Python or R.
- Participate in online coding competitions.
Attend a Workshop on Deep Learning Applications
Show steps
Expand your knowledge by attending a workshop focused on practical applications of deep learning, gaining insights from industry experts and expanding your network.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Identify and register for a relevant workshop.
- Attend the workshop and actively participate in discussions.
- Follow up with the organizers or speakers to explore further learning opportunities.
Facial Key-Point Detection Project
Show steps
Apply your knowledge of CNNs and residual neural networks by building a deep learning model to detect facial key-points, deepening your understanding of practical applications.
Show steps
Show steps
Show steps
- Gather and preprocess a dataset of facial images with annotated landmarks.
- Build a CNN model using Keras with Tensorflow as the backend.
- Train and evaluate your model using appropriate metrics.
Implement a Deep Learning Model for Image Classification
Show steps
Solidify your understanding of deep learning concepts by building a project that applies a deep learning model to classify images, enhancing your practical skills and problem-solving abilities.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Choose a specific image classification task.
- Collect and preprocess a dataset of images.
- Design and train a deep learning model for image classification.
Career center
Learners who complete Emotion AI: Facial Key-points Detection will develop knowledge and skills
that may be useful to these careers:
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers are responsible for designing and developing computer vision algorithms and systems. They work on a variety of tasks, such as object detection, facial recognition, and medical image analysis. This course provides a solid foundation in deep learning and convolutional neural networks, which are essential skills for Computer Vision Engineers. By taking this course, you will be well-prepared to enter or advance your career in this field.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. They work on a variety of tasks, such as predictive analytics, natural language processing, and computer vision. This course provides a solid foundation in deep learning and convolutional neural networks, which are essential skills for Machine Learning Engineers. By taking this course, you will be well-prepared to enter or advance your career in this field.
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, analyzing, and interpreting data. They work on a variety of tasks, such as predictive analytics, customer segmentation, and fraud detection. This course provides a solid foundation in deep learning and convolutional neural networks, which are essential skills for Data Scientists. By taking this course, you will be well-prepared to enter or advance your career in this field.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and maintaining software applications. They work on a variety of tasks, such as web development, mobile app development, and data analysis. This course provides a solid foundation in deep learning and convolutional neural networks, which are essential skills for Software Engineers. By taking this course, you will be well-prepared to enter or advance your career in this field.
Research Scientist
Research Scientist
Research Scientists are responsible for conducting research in a variety of fields, such as computer science, physics, and biology. They work on a variety of tasks, such as developing new algorithms, designing new experiments, and analyzing data. This course provides a solid foundation in deep learning and convolutional neural networks, which are essential skills for Research Scientists. By taking this course, you will be well-prepared to enter or advance your career in this field.
Product Manager
Product Manager
Product Managers are responsible for managing the development and launch of new products. They work on a variety of tasks, such as defining product requirements, conducting market research, and managing product teams. This course may be useful for Product Managers who want to learn more about deep learning and convolutional neural networks. By taking this course, you will gain a better understanding of the technical aspects of product development.
Business Analyst
Business Analyst
Business Analysts are responsible for analyzing business processes and identifying opportunities for improvement. They work on a variety of tasks, such as gathering requirements, developing solutions, and presenting findings. This course may be useful for Business Analysts who want to learn more about deep learning and convolutional neural networks. By taking this course, you will gain a better understanding of the technical aspects of business analysis.
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, analyzing, and interpreting data. They work on a variety of tasks, such as developing data pipelines, building data models, and presenting findings. This course may be useful for Data Analysts who want to learn more about deep learning and convolutional neural networks. By taking this course, you will gain a better understanding of the technical aspects of data analysis.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts are responsible for developing and using mathematical models to analyze financial data. They work on a variety of tasks, such as risk management, portfolio optimization, and trading strategies. This course may be useful for Quantitative Analysts who want to learn more about deep learning and convolutional neural networks. By taking this course, you will gain a better understanding of the technical aspects of quantitative analysis.
Statistician
Statistician
Statisticians are responsible for collecting, analyzing, and interpreting data. They work on a variety of tasks, such as designing experiments, conducting surveys, and developing statistical models. This course may be useful for Statisticians who want to learn more about deep learning and convolutional neural networks. By taking this course, you will gain a better understanding of the technical aspects of statistics.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts are responsible for developing and using mathematical models to solve business problems. They work on a variety of tasks, such as optimizing supply chains, scheduling production, and designing transportation networks. This course may be useful for Operations Research Analysts who want to learn more about deep learning and convolutional neural networks. By taking this course, you will gain a better understanding of the technical aspects of operations research.
Financial Analyst
Financial Analyst
Financial Analysts are responsible for analyzing financial data and making investment recommendations. They work on a variety of tasks, such as developing financial models, conducting research, and presenting findings. This course may be useful for Financial Analysts who want to learn more about deep learning and convolutional neural networks. By taking this course, you will gain a better understanding of the technical aspects of financial analysis.
Risk Analyst
Risk Analyst
Risk Analysts are responsible for identifying, assessing, and managing risks. They work on a variety of tasks, such as developing risk models, conducting risk assessments, and making recommendations to management. This course may be useful for Risk Analysts who want to learn more about deep learning and convolutional neural networks. By taking this course, you will gain a better understanding of the technical aspects of risk analysis.
Actuary
Actuary
Actuaries are responsible for assessing and managing financial risks. They work on a variety of tasks, such as developing insurance policies, pricing financial products, and managing investment portfolios. This course may be useful for Actuaries who want to learn more about deep learning and convolutional neural networks. By taking this course, you will gain a better understanding of the technical aspects of actuarial science.
Auditor
Auditor
Auditors are responsible for examining and evaluating financial records. They work on a variety of tasks, such as reviewing financial statements, conducting interviews, and making recommendations to management. This course may be useful for Auditors who want to learn more about deep learning and convolutional neural networks. By taking this course, you will gain a better understanding of the technical aspects of auditing.
For more career information including salaries, visit:
OpenCourser.com/course/8fzsxx/emotion
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
Emotion AI: Facial Key-points Detection.
Save
Provides a comprehensive overview of deep learning theory and algorithms, and valuable reference for anyone interested in learning more about this field.
Provides a practical guide to machine learning using Python, and valuable reference for anyone who wants to learn more about this field.
Provides a comprehensive overview of artificial intelligence, and valuable reference for anyone who wants to learn more about this field.
Provides a practical guide to deep learning using Fastai and PyTorch, and valuable reference for anyone who wants to learn more about this field.
Provides a comprehensive overview of statistical learning, and valuable reference for anyone who wants to learn more about this field.
Provides a practical guide to machine learning for hackers, and valuable reference for anyone who wants to learn more about this field.
Provides a practical guide to data science using Python, and valuable reference for anyone who wants to learn more about this field.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/8fzsxx/emotion
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Effort
3 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Ryan Ahmed
Language
English
Good to know
Develops practical skills in facial key-point detection for recognizing customer emotions
Covers key concepts in deep learning and neural networks, especially CNNs and residual networks
Teaches in-demand deep learning libraries and frameworks, such as Tensorflow 2.0 and Keras
Uses the popular Python programming language, familiar to data scientists and machine learning engineers
Emphasizes practical implementation and hands-on learning through a project-based approach
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