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Ryan Ahmed
In this hands-on project, we will train a deep learning model based on Convolutional Neural Networks (CNNs) and Residual Blocks to detect facial expressions. This project could be practically used for detecting customer emotions and facial expressions.
Read more
In this hands-on project, we will train a deep learning model based on Convolutional Neural Networks (CNNs) and Residual Blocks to detect facial expressions. This project could be practically used for detecting customer emotions and facial expressions.
Read more
In this hands-on project, we will train a deep learning model based on Convolutional Neural Networks (CNNs) and Residual Blocks to detect facial expressions. This project could be practically used for detecting customer emotions and facial expressions.
By the end of this project, 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/spohme/facial
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What's inside
Syllabus
Facial Expression Classification with Residual Neural Networks
In this hands-on project, we will train deep learning model based on Convolutional Neural Networks (CNNs) and Residual Blocks to detect facial expressions. 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
Course may fit learners with a background in deep learning and neural network modeling
Introduces key libraries for deep learning including Keras and Tensorflow 2.0
Strong emphasis on hands-on practice through project-based learning
Leverages the power of Residual Neural Networks to enhance model performance
Demonstrates data augmentation techniques to improve model robustness
Uses real-world facial expression datasets for practical applications
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Reviews summary
Facial expression classification using residual neural nets
Learners largely support this course with some mixed feelings. Many reviews praise the engaging lectures while some express disappointment at the course's length and lack of hands-on experience. Reviews say that the instructor is knowledgeable and supportive, and they appreciate the practical applications of the work.
Knowledgeable and supportive
"the lecturer is so geniuuuuuuussss"
"Helpful instructor"
"The instructor is also very responsive in the discussion forum."
Engaging and interesting
"Easy Quiz thanks for this course it helped me to understand concept clearly without wasting much of my time."
"Great course."
"cool and best"
Too short
"Too short"
"6/5"
"For me this was impossible. I did not realize how much previous knowledge was required."
Lack of hands-on experience
"This is NOT a guided project. It is a DEMO."
"the guided projects are of no use."
"I did not realize how much previous knowledge was required."
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 Facial Expression Classification Using Residual Neural Nets with these
activities:
Review Deep Learning Model Building
Show steps
Refresh your understanding of the core concepts and techniques used in deep learning model building, including CNNs and ResNets, to prepare for this course.
Browse courses on
Convolutional Neural Networks (CNNs)
Show steps
-
Read through the course syllabus and skim the course materials.
-
Review online tutorials and documentation on deep learning model building using CNNs and ResNets.
Practice Implementing a CNN
Show steps
Gain practical experience by following guided tutorials to implement a Convolutional Neural Network (CNN) for image classification, which will serve as a foundation for the course project.
Browse courses on
Convolutional Neural Network
Show steps
-
Find and select a beginner-friendly tutorial on CNN implementation using Tensorflow or Keras.
-
Step through the tutorial, implementing a CNN from scratch.
-
Test and evaluate the performance of your implemented CNN on a sample dataset.
Practice Data Augmentation Techniques
Show steps
Enhance your understanding of data augmentation techniques by completing practice drills. This will help you improve the generalization capability of your deep learning models.
Browse courses on
Data Augmentation
Show steps
-
Identify and learn various data augmentation techniques commonly used in deep learning.
-
Implement these techniques in Python using libraries like OpenCV or imgaug.
-
Apply data augmentation to a sample dataset and observe its impact on model performance.
Three other activities
Expand to see all activities and additional details
Show all six activities
Build a Facial Expression Recognition Model
Show steps
Apply the concepts learned in this course by building a deep learning model for facial expression recognition. This project will provide you with hands-on experience and a deeper understanding of the material.
Browse courses on
Facial Expression Recognition
Show steps
-
Gather and prepare a dataset of facial images with labeled expressions.
-
Design and implement a deep learning model using CNNs and ResNets for facial expression recognition.
-
Train and evaluate the model on the prepared dataset.
-
Optimize the model's performance using techniques like dropout and regularization.
Write a Summary Report on Deep Learning Models for Facial Expression Recognition
Show steps
Summarize your understanding of deep learning models for facial expression recognition by creating a written report. This will help you solidify your knowledge and communicate your findings.
Browse courses on
Deep Learning
Show steps
-
Identify the key concepts and techniques used in deep learning models for facial expression recognition.
-
Research and gather relevant information from academic papers and online resources.
-
Organize and write a comprehensive report outlining your findings.
Participate in a Facial Expression Recognition Competition
Show steps
Challenge yourself and test your skills by participating in a facial expression recognition competition. This will provide you with an opportunity to benchmark your model's performance and learn from others.
Browse courses on
Facial Expression Recognition
Show steps
-
Identify and register for a suitable facial expression recognition competition.
-
Fine-tune and optimize your deep learning model for the specific requirements of the competition.
-
Submit your model and evaluate its performance on the competition's dataset.
-
Analyze your results and explore ways to further improve your model.
Review Deep Learning Model Building
Show steps
Refresh your understanding of the core concepts and techniques used in deep learning model building, including CNNs and ResNets, to prepare for this course.
Browse courses on
Convolutional Neural Networks (CNNs)
Show steps
Show steps
Show steps
- Read through the course syllabus and skim the course materials.
- Review online tutorials and documentation on deep learning model building using CNNs and ResNets.
Practice Implementing a CNN
Show steps
Gain practical experience by following guided tutorials to implement a Convolutional Neural Network (CNN) for image classification, which will serve as a foundation for the course project.
Browse courses on
Convolutional Neural Network
Show steps
Show steps
Show steps
- Find and select a beginner-friendly tutorial on CNN implementation using Tensorflow or Keras.
- Step through the tutorial, implementing a CNN from scratch.
- Test and evaluate the performance of your implemented CNN on a sample dataset.
Practice Data Augmentation Techniques
Show steps
Enhance your understanding of data augmentation techniques by completing practice drills. This will help you improve the generalization capability of your deep learning models.
Browse courses on
Data Augmentation
Show steps
Show steps
Show steps
- Identify and learn various data augmentation techniques commonly used in deep learning.
- Implement these techniques in Python using libraries like OpenCV or imgaug.
- Apply data augmentation to a sample dataset and observe its impact on model performance.
Three other activities
Expand to see all activities and additional details
Show all six activities
Build a Facial Expression Recognition Model
Show steps
Apply the concepts learned in this course by building a deep learning model for facial expression recognition. This project will provide you with hands-on experience and a deeper understanding of the material.
Browse courses on
Facial Expression Recognition
Show steps
Show steps
Show steps
- Gather and prepare a dataset of facial images with labeled expressions.
- Design and implement a deep learning model using CNNs and ResNets for facial expression recognition.
- Train and evaluate the model on the prepared dataset.
- Optimize the model's performance using techniques like dropout and regularization.
Write a Summary Report on Deep Learning Models for Facial Expression Recognition
Show steps
Summarize your understanding of deep learning models for facial expression recognition by creating a written report. This will help you solidify your knowledge and communicate your findings.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Identify the key concepts and techniques used in deep learning models for facial expression recognition.
- Research and gather relevant information from academic papers and online resources.
- Organize and write a comprehensive report outlining your findings.
Participate in a Facial Expression Recognition Competition
Show steps
Challenge yourself and test your skills by participating in a facial expression recognition competition. This will provide you with an opportunity to benchmark your model's performance and learn from others.
Browse courses on
Facial Expression Recognition
Show steps
Show steps
Show steps
- Identify and register for a suitable facial expression recognition competition.
- Fine-tune and optimize your deep learning model for the specific requirements of the competition.
- Submit your model and evaluate its performance on the competition's dataset.
- Analyze your results and explore ways to further improve your model.
Career center
Learners who complete Facial Expression Classification Using Residual Neural Nets will develop knowledge and skills
that may be useful to these careers:
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers apply their knowledge of image processing, machine learning, and deep neural networks to develop, deploy, and maintain computer vision systems. This course provides a solid foundation in deep neural networks, particularly Convolutional Neural Networks (CNNs) and Residual Neural Networks. These techniques are essential for building accurate and efficient computer vision systems. By completing this course, you will gain the skills and knowledge necessary to succeed as a Computer Vision Engineer.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models to solve complex problems. This course provides a comprehensive introduction to deep learning, with a focus on CNNs and Residual Networks. These techniques are widely used in machine learning applications such as image classification, object detection, and natural language processing. By completing this course, you will gain the foundation necessary to build and deploy machine learning models as a Machine Learning Engineer.
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers specialize in designing, developing, and deploying deep learning models. This course provides a comprehensive introduction to deep learning, with a focus on CNNs and Residual Networks. These techniques are widely used in deep learning applications such as image classification, object detection, and natural language processing. By completing this course, you will gain the skills and knowledge necessary to succeed as a Deep Learning Engineer.
Data Scientist
Data Scientist
Data Scientists use their knowledge of statistics, machine learning, and data analysis to extract insights from data. This course provides a solid foundation in deep learning, with a focus on CNNs and Residual Networks. These techniques are essential for building accurate and efficient machine learning models. By completing this course, you will gain the skills necessary to succeed as a Data Scientist.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and deploy artificial intelligence systems. This course provides a solid foundation in deep learning, with a focus on CNNs and Residual Networks. These techniques are essential for building accurate and efficient AI systems. By completing this course, you will gain the skills and knowledge necessary to succeed as an Artificial Intelligence Engineer.
Research Scientist
Research Scientist
Research Scientists conduct research in various scientific fields, including computer science, engineering, and medicine. This course provides a solid foundation in deep learning, with a focus on CNNs and Residual Networks. These techniques are essential for building accurate and efficient models for scientific research. By completing this course, you will gain the skills and knowledge necessary to succeed as a Research Scientist.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course provides a solid foundation in deep learning, with a focus on CNNs and Residual Networks. These techniques are increasingly used in software development to improve the performance and accuracy of software systems. By completing this course, you will gain the skills and knowledge necessary to succeed as a Software Engineer.
Robotics Engineer
Robotics Engineer
Robotics Engineers design, develop, and maintain robots. This course provides a solid foundation in deep learning, with a focus on CNNs and Residual Networks. These techniques are increasingly used in robotics to improve the performance and accuracy of robots. By completing this course, you will gain the skills and knowledge necessary to succeed as a Robotics Engineer.
Data Analyst
Data Analyst
Data Analysts use their knowledge of data analysis and statistics to extract insights from data. This course provides a solid foundation in deep learning, with a focus on CNNs and Residual Networks. These techniques are increasingly used in data analysis to improve the accuracy and efficiency of data analysis processes. By completing this course, you will gain the skills and knowledge necessary to succeed as a Data Analyst.
Business Analyst
Business Analyst
Business Analysts use their knowledge of business processes and data analysis to improve the efficiency and effectiveness of businesses. This course provides a solid foundation in deep learning, with a focus on CNNs and Residual Networks. These techniques are increasingly used in business analysis to improve the accuracy and efficiency of business analysis processes. By completing this course, you will gain the skills and knowledge necessary to succeed as a Business Analyst.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. This course provides a solid foundation in deep learning, with a focus on CNNs and Residual Networks. These techniques are increasingly used in product development to improve the performance and accuracy of new products. By completing this course, you will gain the skills and knowledge necessary to succeed as a Product Manager.
UX Designer
UX Designer
UX Designers are responsible for the design and usability of user interfaces. This course provides a solid foundation in deep learning, with a focus on CNNs and Residual Networks. These techniques are increasingly used in UX design to improve the performance and accuracy of user interfaces. By completing this course, you will gain the skills and knowledge necessary to succeed as a UX Designer.
Financial Analyst
Financial Analyst
Financial Analysts use their knowledge of finance and data analysis to make investment decisions. This course provides a solid foundation in deep learning, with a focus on CNNs and Residual Networks. These techniques are increasingly used in financial analysis to improve the accuracy and efficiency of financial analysis processes. By completing this course, you will gain the skills and knowledge necessary to succeed as a Financial Analyst.
Marketing Analyst
Marketing Analyst
Marketing Analysts use their knowledge of marketing and data analysis to improve the effectiveness of marketing campaigns. This course provides a solid foundation in deep learning, with a focus on CNNs and Residual Networks. These techniques are increasingly used in marketing analysis to improve the accuracy and efficiency of marketing analysis processes. By completing this course, you will gain the skills and knowledge necessary to succeed as a Marketing Analyst.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use their knowledge of mathematics and data analysis to improve the efficiency and effectiveness of operations. This course provides a solid foundation in deep learning, with a focus on CNNs and Residual Networks. These techniques are increasingly used in operations research to improve the accuracy and efficiency of operations research processes. By completing this course, you will gain the skills and knowledge necessary to succeed as an Operations Research Analyst.
For more career information including salaries, visit:
OpenCourser.com/course/spohme/facial
Reading list
We've selected nine 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
Facial Expression Classification Using Residual Neural Nets.
Save
Provides a comprehensive overview of deep learning, the fundamental theoretical concepts, and the most popular deep learning architectures.
Save
Provides a comprehensive foundation in computer vision, covering fundamental algorithms, techniques, and applications in various domains.
Offers a statistical perspective on pattern recognition and machine learning, covering supervised and unsupervised learning methods, Bayesian inference, and model selection.
Serves as a practical guide to deep learning using Python and the Keras library, covering model building, training, and evaluation.
Save
Focuses on practical computer vision applications using OpenCV, a popular open-source library, covering image processing, object detection, and facial recognition.
Provides a comprehensive introduction to machine learning, covering data preprocessing, model selection, and evaluation using popular Python libraries.
Offers a practical approach to deep learning for computer vision, covering image classification, object detection, and segmentation using Python and Keras.
Provides a comprehensive guide to machine learning in Python, covering data manipulation, model building, and evaluation using scikit-learn.
Provides a comprehensive and theoretical foundation in computer vision, covering image formation, feature extraction, and object recognition.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/spohme/facial
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Effort
2 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Ryan Ahmed
Language
English
Good to know
Course may fit learners with a background in deep learning and neural network modeling
Introduces key libraries for deep learning including Keras and Tensorflow 2.0
Strong emphasis on hands-on practice through project-based learning
Leverages the power of Residual Neural Networks to enhance model performance
Demonstrates data augmentation techniques to improve model robustness
Uses real-world facial expression datasets for practical applications
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