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Ryan Ahmed
Hello everyone and welcome to this new hands-on project on image classification with Amazon Web Services (AWS) AutoGluon. In this project, we will train several deep neural networks models to classify images using a powerful library known as AutoGluon. AutoGluon is the library behind AWS SageMaker autopilot and it allows for quick prototyping of several powerful models using a few lines of code.
Register for this course and see more details by visiting:
OpenCourser.com/course/s32sal/image
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What's inside
Syllabus
Project Overview
Hello everyone and welcome to this new hands-on project on image classification with Amazon Web Services (AWS) AutoGluon. In this project, we will train several deep neural networks models to classify images using a powerful library known as AutoGluon. AutoGluon is the library behind AWS SageMaker autopilot and it allows for quick prototyping of several powerful models using a few lines of code.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Helps learners understand basic and complex image classification techniques
Introduces AutoGluon, a simplified model prototyping tool that helps learners develop models quickly
Enhances learners' understanding of prototyping
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Save Image Classification on Autopilot with AWS AutoGluon to your list so you can find it easily later:
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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 Image Classification on Autopilot with AWS AutoGluon with these
activities:
Review fundamental concepts of deep learning
Show steps
Sharpen your understanding of the underlying principles of deep learning, which are essential for success in this course.
Show steps
-
Go over lecture notes or online resources on deep learning concepts.
-
Review mathematical equations and algorithms behind deep learning models.
-
Complete practice exercises to test your comprehension.
Explore AutoGluon documentation and tutorials
Show steps
Familiarize yourself with AutoGluon's capabilities and best practices for image classification tasks.
Browse courses on
AutoGluon
Show steps
-
Visit the official AutoGluon website and documentation.
-
Explore tutorials and examples specific to image classification.
-
Review code snippets and experiment with different parameters.
Join a study group for image classification
Show steps
Engage with peers, exchange ideas, and collectively reinforce your understanding of image classification concepts.
Show steps
-
Find or create a study group consisting of other learners interested in image classification.
-
Set regular meeting times and discuss course materials.
-
Work together on practice exercises and projects.
Three other activities
Expand to see all activities and additional details
Show all six activities
Train multiple deep learning models
Show steps
Gain hands-on experience in training and evaluating different deep neural network models for image classification.
Browse courses on
Neural Networks
Show steps
-
Create a training and validation dataset for image classification.
-
Import necessary libraries and prepare the data for training.
-
Train multiple deep neural network models using AutoGluon.
-
Evaluate the performance of trained models and compare their results.
Develop an image classification web application
Show steps
Apply your knowledge by building a complete image classification application that can be deployed and used in real-world scenarios.
Show steps
-
Design the architecture of the web application.
-
Integrate a trained deep learning model into the application.
-
Create a user interface for image upload and classification.
-
Deploy the application to a hosting platform.
Contribute to AutoGluon project on GitHub
Show steps
Contribute to the development of AutoGluon and gain experience in open-source collaboration.
Browse courses on
AutoGluon
Show steps
-
Identify an area where you can contribute.
-
Fork the AutoGluon repository and make changes.
-
Submit a pull request with your contributions.
-
Review feedback and make necessary revisions.
Review fundamental concepts of deep learning
Show steps
Sharpen your understanding of the underlying principles of deep learning, which are essential for success in this course.
Show steps
Show steps
Show steps
- Go over lecture notes or online resources on deep learning concepts.
- Review mathematical equations and algorithms behind deep learning models.
- Complete practice exercises to test your comprehension.
Explore AutoGluon documentation and tutorials
Show steps
Familiarize yourself with AutoGluon's capabilities and best practices for image classification tasks.
Browse courses on
AutoGluon
Show steps
Show steps
Show steps
- Visit the official AutoGluon website and documentation.
- Explore tutorials and examples specific to image classification.
- Review code snippets and experiment with different parameters.
Join a study group for image classification
Show steps
Engage with peers, exchange ideas, and collectively reinforce your understanding of image classification concepts.
Show steps
Show steps
Show steps
- Find or create a study group consisting of other learners interested in image classification.
- Set regular meeting times and discuss course materials.
- Work together on practice exercises and projects.
Three other activities
Expand to see all activities and additional details
Show all six activities
Train multiple deep learning models
Show steps
Gain hands-on experience in training and evaluating different deep neural network models for image classification.
Browse courses on
Neural Networks
Show steps
Show steps
Show steps
- Create a training and validation dataset for image classification.
- Import necessary libraries and prepare the data for training.
- Train multiple deep neural network models using AutoGluon.
- Evaluate the performance of trained models and compare their results.
Develop an image classification web application
Show steps
Apply your knowledge by building a complete image classification application that can be deployed and used in real-world scenarios.
Show steps
Show steps
Show steps
- Design the architecture of the web application.
- Integrate a trained deep learning model into the application.
- Create a user interface for image upload and classification.
- Deploy the application to a hosting platform.
Contribute to AutoGluon project on GitHub
Show steps
Contribute to the development of AutoGluon and gain experience in open-source collaboration.
Browse courses on
AutoGluon
Show steps
Show steps
Show steps
- Identify an area where you can contribute.
- Fork the AutoGluon repository and make changes.
- Submit a pull request with your contributions.
- Review feedback and make necessary revisions.
Career center
Learners who complete Image Classification on Autopilot with AWS AutoGluon will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists use their expertise in machine learning, statistics, and data analysis to develop and implement solutions to business problems. This course provides a solid foundation in image classification, which is a key skill for Data Scientists working in fields such as computer vision and medical imaging. By completing this course, you will gain the skills necessary to extract valuable insights from images, which can be used to improve decision-making and drive business success.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and maintain machine learning models. This course provides a comprehensive overview of image classification techniques, which are essential for Machine Learning Engineers working in areas such as computer vision and natural language processing. By completing this course, you will gain the skills necessary to build and deploy accurate and efficient image classification models.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers develop and implement computer vision systems for a variety of applications, such as object detection, facial recognition, and medical imaging. This course provides a deep dive into image classification, which is a core component of computer vision systems. By completing this course, you will gain the skills necessary to design and build powerful computer vision applications.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and maintain artificial intelligence systems. This course provides a solid foundation in image classification, which is a key technology for AI applications such as image recognition and object detection. By completing this course, you will gain the skills necessary to develop and deploy AI systems that can solve complex problems.
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers design, develop, and maintain deep learning models. This course provides a comprehensive overview of image classification using deep learning techniques. By completing this course, you will gain the skills necessary to build and deploy accurate and efficient deep learning models for image classification tasks.
Data Analyst
Data Analyst
Data Analysts use their skills in data analysis and visualization to extract insights from data. This course provides a solid foundation in image classification, which is a valuable skill for Data Analysts working in fields such as marketing and finance. By completing this course, you will gain the skills necessary to analyze and interpret images, which can help you to identify trends and make better decisions.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course provides a solid foundation in image classification, which is a valuable skill for Software Engineers working in fields such as computer vision and robotics. By completing this course, you will gain the skills necessary to develop software applications that can analyze and interpret images.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. This course provides a foundation in image classification, which is a valuable skill for Product Managers working in fields such as e-commerce and retail. By completing this course, you will gain the skills necessary to analyze and interpret images, which can help you to identify customer needs and develop products that meet those needs.
Sales Manager
Sales Manager
Sales Managers are responsible for leading and motivating sales teams. This course provides a foundation in image classification, which is a valuable skill for Sales Managers working in fields such as retail and technology. By completing this course, you will gain the skills necessary to analyze and interpret images, which can help you to identify customer needs and close deals.
Business Analyst
Business Analyst
Business Analysts use their skills in data analysis and problem-solving to help businesses improve their operations. This course provides a foundation in image classification, which is a valuable skill for Business Analysts working in fields such as marketing and finance. By completing this course, you will gain the skills necessary to analyze and interpret images, which can help you to identify trends and make better decisions.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for developing and executing marketing campaigns. This course provides a foundation in image classification, which is a valuable skill for Marketing Managers working in fields such as social media and advertising. By completing this course, you will gain the skills necessary to analyze and interpret images, which can help you to create more effective marketing campaigns.
Technical Writer
Technical Writer
Technical Writers create and maintain documentation for software and hardware products. This course provides a foundation in image classification, which is a valuable skill for Technical Writers working in fields such as computer science and engineering. By completing this course, you will gain the skills necessary to analyze and interpret images, which can help you to create clear and concise documentation.
Customer Success Manager
Customer Success Manager
Customer Success Managers are responsible for ensuring that customers are satisfied with their products or services. This course provides a foundation in image classification, which is a valuable skill for Customer Success Managers working in fields such as software and technology. By completing this course, you will gain the skills necessary to analyze and interpret images, which can help you to identify customer needs and resolve issues.
User Experience Designer
User Experience Designer
User Experience Designers design and evaluate user interfaces for software and hardware products. This course provides a foundation in image classification, which is a valuable skill for User Experience Designers working in fields such as web design and mobile development. By completing this course, you will gain the skills necessary to analyze and interpret images, which can help you to create user interfaces that are both visually appealing and easy to use.
For more career information including salaries, visit:
OpenCourser.com/course/s32sal/image
Reading list
We've selected 11 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
Image Classification on Autopilot with AWS AutoGluon.
Save
This widely acclaimed book provides a comprehensive overview of deep learning, covering the fundamental concepts, algorithms, and applications. It serves as a valuable reference for understanding the theoretical foundations of image classification and other deep learning tasks.
This comprehensive textbook covers a wide range of machine learning algorithms, including supervised and unsupervised learning, Bayesian methods, and support vector machines. It provides a theoretical framework for understanding the underlying principles of image classification and other pattern recognition tasks.
This widely acclaimed book offers a thorough introduction to deep learning, covering neural networks, convolutional neural networks, and recurrent neural networks. It provides a solid foundation for understanding the concepts and algorithms used in image classification and other deep learning tasks.
Save
This classic textbook provides a comprehensive overview of machine learning, covering topics such as supervised and unsupervised learning, model selection, and overfitting. It serves as a valuable reference for understanding the fundamental principles behind image classification and other machine learning tasks.
Save
This classic textbook presents a comprehensive overview of computer vision algorithms and techniques, including image processing, feature extraction, and object recognition. It serves as a valuable reference for understanding the broader context of image classification within the field of computer vision.
Save
Presents a comprehensive overview of machine learning, covering topics such as supervised and unsupervised learning, feature selection, and model evaluation. It provides a solid foundation for understanding the principles behind image classification and other machine learning tasks.
This practical guide focuses on applying deep learning to computer vision tasks, including image classification, object detection, and image segmentation. It provides step-by-step tutorials and code examples to help readers implement deep learning models for real-world applications.
This comprehensive textbook provides a thorough overview of computer vision, covering topics such as image formation, feature extraction, and object recognition. It serves as a valuable reference for understanding the broader context of image classification within the field of computer vision.
This practical guide focuses on implementing machine learning algorithms in Python, including supervised and unsupervised learning, feature engineering, and model evaluation. It provides hands-on experience with image classification and other machine learning tasks.
Save
Provides a comprehensive overview of deep learning using the R programming language. It covers neural networks, convolutional neural networks, and recurrent neural networks, and provides practical examples for image classification and other deep learning tasks.
Presents the theory and algorithms for probabilistic graphical models, which are powerful tools for modeling complex data. It provides a solid foundation for understanding the Bayesian methods used in image classification and other machine learning tasks.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/s32sal/image
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Ryan Ahmed
Language
English
Good to know
Helps learners understand basic and complex image classification techniques
Introduces AutoGluon, a simplified model prototyping tool that helps learners develop models quickly
Enhances learners' understanding of prototyping
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