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Snehan Kekre
In this 45-min guided project, you will learn the basics of using the Keras interface to R with Tensorflow as its backend to solve an image classification problem. By the time you complete this project, you will have used the R programming language to build, train, and evaluate a neural network model to classify images of clothing items into categories such as t-shirts, trousers, and sneakers. We will be training the deep learning based image classification model on the Fashion MNIST dataset which contains 70000 grayscale images of clothes across 10 categories.
Read more
In this 45-min guided project, you will learn the basics of using the Keras interface to R with Tensorflow as its backend to solve an image classification problem. By the time you complete this project, you will have used the R programming language to build, train, and evaluate a neural network model to classify images of clothing items into categories such as t-shirts, trousers, and sneakers. We will be training the deep learning based image classification model on the Fashion MNIST dataset which contains 70000 grayscale images of clothes across 10 categories.
Read more
In this 45-min guided project, you will learn the basics of using the Keras interface to R with Tensorflow as its backend to solve an image classification problem. By the time you complete this project, you will have used the R programming language to build, train, and evaluate a neural network model to classify images of clothing items into categories such as t-shirts, trousers, and sneakers. We will be training the deep learning based image classification model on the Fashion MNIST dataset which contains 70000 grayscale images of clothes across 10 categories.
In order to be successful in this project, you should be familiar with R programming, and basics of neural networks.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/4vpfar/build
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What's inside
Syllabus
Build a Deep Learning Based Image Classifier with R
In this 45-min guided project, you will learn the basics of using the Keras interface to R with Tensorflow as its backend to solve an image classification problem. By the time you complete this project, you will have used the R programming language to build, train, and evaluate a neural network model to classify images of clothing items into categories such as t-shirts, trousers, and sneakers. We will be training the deep learning based image classification model on the Fashion MNIST dataset which contains 70000 grayscale images of clothes across 10 categories. In order to be successful in this project, you should be familiar with R programming, and basics of neural networks.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces learners to the Keras interface to R with Tensorflow as its backend, which is a foundational tool for data science and machine learning
Develops foundational skills in neural networks, which are vital to building competency in deep learning today
Utilizes the Fashion MNIST dataset, which is a standard and widely used dataset for image classification tasks, ensuring the relevance of the learning experience
Suitable for beginners with familiarity in R programming and basic understanding of neural networks
Provides a hands-on learning experience through a guided project, which is an effective way to reinforce learning
Limited to learners in the North America region, which may exclude a wider audience globally
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Reviews summary
Deep learning image classifier course
Learners say that this course provides them with an engaging way to learn image classification based on deep learning using the R programming language. According to students, this course is well-organized and is supported by a highly-knowledgeable instructor. Many learners especially like the emphasis on practical application through hands-on projects, but some have mentioned that the lack of personalized feedback from the instructor could be improved upon.
Engaging hands-on projects
"I really enjoyed working with this project."
"GOOD POJECTS"
"Excellent project"
"Very nice, concise, clean and to the point project."
Knowledgeable instructor
"Instructor was great."
"Excellent knowledge by learning about images"
Limited personalized feedback
"The desktop cloud didn't work from task 5, I wrote it and never received a solution reply"
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 Build a Deep Learning Based Image Classifier with R with these
activities:
Learn about Tensorflow
Show steps
Complete a Tensorflow tutorial to clear any initial confusion. Clear understanding of the Tensorflow interface is essential for the course.
Show steps
-
Enroll in the official Tensorflow tutorial
-
Complete at least 3 modules
-
Submit any questions you have on a discussion forum for feedback
Practice coding exercises
Show steps
Work on practice drills and exercises that involve using Keras with R and Tensorflow backend. This will help you master the concepts and techniques covered in the course.
Show steps
-
Find a coding platform with Tensorflow and Keras exercises focused on image classification
-
Attempt to solve at least 5 exercises
-
Debug your code and seek help on forums if needed
Complete practice problems
Show steps
Complete practice problems to reinforce the concepts covered in the lectures and tutorials.
Show steps
-
Review lecture notes and identify key concepts
-
Attempt to solve practice problems related to those concepts
-
Check your solutions against the provided answer key
Two other activities
Expand to see all activities and additional details
Show all five activities
Build a small image classification project
Show steps
Build a small image classification project to solidify understanding of the course materials. This will encourage you to apply what you learn in the course to a practical problem.
Show steps
-
Identify a small image classification problem to solve
-
Collect a small dataset of images related to your problem
-
Train a simple image classification model using Keras
-
Evaluate the performance of your model
-
Write a brief report summarizing your project
Mentor other learners on course material
Show steps
Offer to mentor other learners on the course materials. This activity will help you solidify your knowledge and allow you to help fellow learners.
Show steps
-
Join a course forum or community
-
Identify a fellow learner who is struggling with a concept you are familiar with
-
Offer your help and provide guidance
Learn about Tensorflow
Show steps
Complete a Tensorflow tutorial to clear any initial confusion. Clear understanding of the Tensorflow interface is essential for the course.
Show steps
Show steps
Show steps
- Enroll in the official Tensorflow tutorial
- Complete at least 3 modules
- Submit any questions you have on a discussion forum for feedback
Practice coding exercises
Show steps
Work on practice drills and exercises that involve using Keras with R and Tensorflow backend. This will help you master the concepts and techniques covered in the course.
Show steps
Show steps
Show steps
- Find a coding platform with Tensorflow and Keras exercises focused on image classification
- Attempt to solve at least 5 exercises
- Debug your code and seek help on forums if needed
Complete practice problems
Show steps
Complete practice problems to reinforce the concepts covered in the lectures and tutorials.
Show steps
Show steps
Show steps
- Review lecture notes and identify key concepts
- Attempt to solve practice problems related to those concepts
- Check your solutions against the provided answer key
Two other activities
Expand to see all activities and additional details
Show all five activities
Build a small image classification project
Show steps
Build a small image classification project to solidify understanding of the course materials. This will encourage you to apply what you learn in the course to a practical problem.
Show steps
Show steps
Show steps
- Identify a small image classification problem to solve
- Collect a small dataset of images related to your problem
- Train a simple image classification model using Keras
- Evaluate the performance of your model
- Write a brief report summarizing your project
Mentor other learners on course material
Show steps
Offer to mentor other learners on the course materials. This activity will help you solidify your knowledge and allow you to help fellow learners.
Show steps
Show steps
Show steps
- Join a course forum or community
- Identify a fellow learner who is struggling with a concept you are familiar with
- Offer your help and provide guidance
Career center
Learners who complete Build a Deep Learning Based Image Classifier with R will develop knowledge and skills
that may be useful to these careers:
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers develop and implement computer vision solutions. The course Build a Deep Learning Based Image Classifier with R covers essential concepts in deep learning and image classification. By taking this course, you will gain valuable skills and knowledge that can help you succeed in a career as a Computer Vision Engineer.
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers specialize in developing and implementing deep learning solutions. Build a Deep Learning Based Image Classifier with R provides a comprehensive introduction to deep learning and image classification. The course will teach you how to build, train, and evaluate deep learning models, which are essential skills for Deep Learning Engineers.
Image Processing Engineer
Image Processing Engineer
Image Processing Engineers develop and implement image processing algorithms. Build a Deep Learning Based Image Classifier with R provides a comprehensive introduction to deep learning and image classification. The course will teach you how to build, train, and evaluate deep learning models for image processing tasks, which are essential skills for Image Processing Engineers.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models. As a Machine Learning Engineer, you may use the knowledge gained from Build a Deep Learning Based Image Classifier with R to design and implement deep learning solutions for various business problems. The course will help you build a strong foundation in deep learning concepts such as neural networks, convolutional neural networks, and image classification.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and maintain AI systems. Build a Deep Learning Based Image Classifier with R can provide you with a solid foundation in deep learning, which is a critical technology for AI. The course will teach you how to build, train, and evaluate deep learning models, which are essential skills for Artificial Intelligence Engineers.
Data Scientist
Data Scientist
A Data Scientist may use the methodologies learned in Build a Deep Learning Based Image Classifier with R when working with large data sets. This course provides a solid foundation for understanding how to build, train, and evaluate deep learning models. These skills are essential for extracting insights from data and making predictions.
Data Analyst
Data Analyst
Data Analysts utilize data to solve business problems. Build a Deep Learning Based Image Classifier with R can provide Data Analysts with skills in deep learning and image classification, which are becoming increasingly important in the field of data analytics. The course can help Data Analysts stay up-to-date with the latest advancements in deep learning and apply these techniques to their work.
Data Architect
Data Architect
Data Architects may benefit from taking the course Build a Deep Learning Based Image Classifier with R to gain knowledge in deep learning and image classification. The course provides a solid foundation in the fundamentals of deep learning and image classification. This knowledge can be useful for Data Architects when designing and implementing data architectures for deep learning applications.
Business Analyst
Business Analyst
Business Analysts may find the course Build a Deep Learning Based Image Classifier with R useful for understanding how deep learning and image classification can be used to solve business problems. The course provides a solid foundation in the fundamentals of deep learning and image classification, which can help Business Analysts make informed recommendations to their clients.
Research Scientist
Research Scientist
Research Scientists in various fields may find the course Build a Deep Learning Based Image Classifier with R useful. The course provides a strong foundation in deep learning and image classification, which are becoming increasingly important in research. The course can help Research Scientists stay up-to-date with the latest advancements in deep learning and apply these techniques to their research.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. The course Build a Deep Learning Based Image Classifier with R provides a solid foundation in deep learning and image classification. This knowledge can be useful for Quantitative Analysts when developing models for financial data analysis.
Software Developer
Software Developer
Software Developers may find the course Build a Deep Learning Based Image Classifier with R useful for gaining knowledge in deep learning and image classification. The course provides a solid foundation in the fundamentals of deep learning and image classification. This knowledge can be useful for Software Developers when developing software applications that use deep learning and image classification.
Software Engineer
Software Engineer
Software Engineers with knowledge of deep learning are in high demand, and Build a Deep Learning Based Image Classifier with R can provide you with a solid foundation in this field. The course will teach you how to build, train, and evaluate deep learning models, which are essential skills for Software Engineers working on image-related applications.
Information Security Analyst
Information Security Analyst
Information Security Analysts may find the course Build a Deep Learning Based Image Classifier with R useful for gaining knowledge in deep learning and image classification. The course provides a solid foundation in the fundamentals of deep learning and image classification. This knowledge can be useful for Information Security Analysts when developing and implementing security solutions for image data.
Product Manager
Product Manager
Product Managers may benefit from taking the course Build a Deep Learning Based Image Classifier with R to gain a better understanding of how deep learning and image classification can be used to solve business problems. The course can provide Product Managers with the knowledge and skills needed to make informed decisions about incorporating deep learning into their products.
For more career information including salaries, visit:
OpenCourser.com/course/4vpfar/build
Reading list
We've selected ten 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
Build a Deep Learning Based Image Classifier with R.
Focuses specifically on deep learning using the Keras interface in R. It covers the basics of neural networks, convolutional neural networks, and recurrent neural networks. It would be a valuable resource for students who want to delve deeper into the technical aspects of deep learning.
Comprehensive reference on statistical learning methods. It covers a wide range of topics, including supervised and unsupervised learning, model selection, and regularization. It would be a valuable resource for students who want to gain a deep understanding of the field.
Provides a comprehensive overview of pattern recognition and machine learning. It covers the mathematical foundations, algorithms, and applications of machine learning. It would be a valuable resource for students who want to gain a theoretical understanding of the field.
Provides a comprehensive overview of interpretable machine learning. It covers the principles, methods, and applications of interpretable machine learning models.
Covers the fundamentals of machine learning using Python. It includes hands-on examples and exercises that would be helpful for students who want to practice their skills.
Provides a practical introduction to machine learning using Python. It covers a wide range of topics, including data preprocessing, feature engineering, and model evaluation.
Provides a visual and intuitive introduction to deep learning. It uses clear explanations and diagrams to help students understand the concepts and algorithms of deep learning.
Provides a collection of recipes for solving common problems in machine learning using R. It would be a helpful reference for students who want to apply their knowledge to practical problems.
Covers the fundamental concepts of statistical learning, including linear regression, logistic regression, and decision trees. It would provide a solid foundation in the statistical principles underlying machine learning.
Provides a gentle introduction to machine learning for beginners. It covers the basic concepts and algorithms in a clear and concise manner.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/4vpfar/build
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Effort
1 hour
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Snehan Kekre
Language
English
Good to know
Introduces learners to the Keras interface to R with Tensorflow as its backend, which is a foundational tool for data science and machine learning
Develops foundational skills in neural networks, which are vital to building competency in deep learning today
Utilizes the Fashion MNIST dataset, which is a standard and widely used dataset for image classification tasks, ensuring the relevance of the learning experience
Suitable for beginners with familiarity in R programming and basic understanding of neural networks
Provides a hands-on learning experience through a guided project, which is an effective way to reinforce learning
Limited to learners in the North America region, which may exclude a wider audience globally
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