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Parth Dhameliya
In this 2-hour guided project, you are going to use EfficientNet model and train it on Pneumonia Chest X-Ray dataset. The dataset consist of nearly 5600 Chest X-Ray images and two categories (Pneumonia/Normal). Our main aim for this project is to build a pneumonia classifier which can classify Chest X-Ray scan that belong to one of the two classes. You will load and fine tune the pretrained EffiecientNet model and also to create a simple pytorch trainer to train the model.
Read more
In this 2-hour guided project, you are going to use EfficientNet model and train it on Pneumonia Chest X-Ray dataset. The dataset consist of nearly 5600 Chest X-Ray images and two categories (Pneumonia/Normal). Our main aim for this project is to build a pneumonia classifier which can classify Chest X-Ray scan that belong to one of the two classes. You will load and fine tune the pretrained EffiecientNet model and also to create a simple pytorch trainer to train the model.
Read more
In this 2-hour guided project, you are going to use EfficientNet model and train it on Pneumonia Chest X-Ray dataset. The dataset consist of nearly 5600 Chest X-Ray images and two categories (Pneumonia/Normal). Our main aim for this project is to build a pneumonia classifier which can classify Chest X-Ray scan that belong to one of the two classes. You will load and fine tune the pretrained EffiecientNet model and also to create a simple pytorch trainer to train the model.
In order to be successful in this project, you should be familiar with python, convolutional neural network, basic pytorch. This is a hands on, practical project that focuses primarily on implementation, and not on the theory behind Convolutional 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/ph7t5j/pneumonia
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What's inside
Syllabus
Project Overview
In this project, you are going to use EfficientNet model and train it on Pneumonia Chest X-Ray dataset. The dataset consist of nearly 5600 Chest X-Ray images and two categories (Pneumonia/Normal). Our main aim for this project is to build a pneumonia classifier which can classify Chest X-Ray scan that belong to one of the two classes. You will load and fine tune the pretrained EffiecientNet model and also to create a simple pytorch trainer to train the model.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores medical imaging and CNNs, which are standard in the healthcare industry, featuring use-cases in disease detection and diagnosis
Develops practical skills in implementing and training neural networks for medical imaging tasks, which is a valuable skill for data scientists and healthcare professionals
Provides hands-on experience in building a pneumonia classifier using a pre-trained EfficientNet model, which is a valuable skill for medical imaging practitioners
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Reviews summary
Positive reviews for pneumonia classification
Learners say that this course offers beautiful instructions and good materials.
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 Pneumonia Classification using PyTorch with these
activities:
Mentor a junior learner in Pneumonia Detection
Show steps
Enhance understanding by teaching and supporting others, reinforcing concepts and identifying areas for improvement.
Show steps
-
Identify a learner who would benefit from your guidance.
-
Establish regular sessions to provide support and guidance.
-
Provide constructive feedback and encourage the learner's progress.
Attend a workshop on 'Advanced Techniques in PyTorch'
Show steps
Gain exposure to cutting-edge techniques and best practices in PyTorch.
Show steps
-
Identify a relevant workshop on advanced PyTorch techniques.
-
Register and participate in the workshop.
-
Apply the knowledge gained to your project.
Review 'Deep Learning with PyTorch' by Eli Stevens, Luca Antiga, and Thomas Viehmann
Show steps
Strengthen theoretical understanding of deep learning concepts and PyTorch implementation.
View
Deep Learning with PyTorch: Build, train, and...
on Amazon
Show steps
-
Read chapters 3-5 to review neural network architectures and training techniques.
-
Focus on the PyTorch code examples and tutorials presented in the book.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Write a blog post on Pneumonia Detection using EfficientNet
Show steps
Consolidate knowledge by summarizing and explaining key concepts related to the course.
Show steps
-
Outline the main concepts of Pneumonia detection and EfficientNet models.
-
Provide a step-by-step guide on how to implement a Pneumonia detection model.
-
Share insights and observations gained during the project.
Follow a tutorial on EfficientNet model implementation
Show steps
Reinforce model implementation techniques and deepen understanding of the course content by practicing with a structured guide.
Show steps
-
Identify a tutorial on implementing EfficientNet in PyTorch.
-
Walk through the tutorial step-by-step.
-
Test the implemented model on a simple dataset.
Practice fine-tuning pre-trained EfficientNet models
Show steps
Develop proficiency in fine-tuning EfficientNet models for image classification tasks.
Show steps
-
Collect a dataset of labeled images.
-
Train several EfficientNet models with different hyperparameters.
-
Evaluate the performance of each model and select the best one.
Build a Pneumonia Detection Web App
Show steps
Apply the skills learned in the course to create a practical application that addresses a real-world problem.
Show steps
-
Design the user interface for the web app.
-
Implement the Pneumonia detection model.
-
Deploy the web app to a cloud platform.
Participate in a Pneumonia Detection Hackathon
Show steps
Test skills and apply knowledge in a competitive setting, fostering innovation and practical problem-solving.
Show steps
-
Find a relevant Pneumonia Detection hackathon.
-
Form a team or participate individually.
-
Develop a solution and present it to a panel of experts.
Mentor a junior learner in Pneumonia Detection
Show steps
Enhance understanding by teaching and supporting others, reinforcing concepts and identifying areas for improvement.
Show steps
Show steps
Show steps
- Identify a learner who would benefit from your guidance.
- Establish regular sessions to provide support and guidance.
- Provide constructive feedback and encourage the learner's progress.
Attend a workshop on 'Advanced Techniques in PyTorch'
Show steps
Gain exposure to cutting-edge techniques and best practices in PyTorch.
Show steps
Show steps
Show steps
- Identify a relevant workshop on advanced PyTorch techniques.
- Register and participate in the workshop.
- Apply the knowledge gained to your project.
Review 'Deep Learning with PyTorch' by Eli Stevens, Luca Antiga, and Thomas Viehmann
Show steps
Strengthen theoretical understanding of deep learning concepts and PyTorch implementation.
View
Deep Learning with PyTorch: Build, train, and...
on Amazon
Show steps
Show steps
Show steps
- Read chapters 3-5 to review neural network architectures and training techniques.
- Focus on the PyTorch code examples and tutorials presented in the book.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Write a blog post on Pneumonia Detection using EfficientNet
Show steps
Consolidate knowledge by summarizing and explaining key concepts related to the course.
Show steps
Show steps
Show steps
- Outline the main concepts of Pneumonia detection and EfficientNet models.
- Provide a step-by-step guide on how to implement a Pneumonia detection model.
- Share insights and observations gained during the project.
Follow a tutorial on EfficientNet model implementation
Show steps
Reinforce model implementation techniques and deepen understanding of the course content by practicing with a structured guide.
Show steps
Show steps
Show steps
- Identify a tutorial on implementing EfficientNet in PyTorch.
- Walk through the tutorial step-by-step.
- Test the implemented model on a simple dataset.
Practice fine-tuning pre-trained EfficientNet models
Show steps
Develop proficiency in fine-tuning EfficientNet models for image classification tasks.
Show steps
Show steps
Show steps
- Collect a dataset of labeled images.
- Train several EfficientNet models with different hyperparameters.
- Evaluate the performance of each model and select the best one.
Build a Pneumonia Detection Web App
Show steps
Apply the skills learned in the course to create a practical application that addresses a real-world problem.
Show steps
Show steps
Show steps
- Design the user interface for the web app.
- Implement the Pneumonia detection model.
- Deploy the web app to a cloud platform.
Participate in a Pneumonia Detection Hackathon
Show steps
Test skills and apply knowledge in a competitive setting, fostering innovation and practical problem-solving.
Show steps
Show steps
Show steps
- Find a relevant Pneumonia Detection hackathon.
- Form a team or participate individually.
- Develop a solution and present it to a panel of experts.
Career center
Learners who complete Pneumonia Classification using PyTorch will develop knowledge and skills
that may be useful to these careers:
Project Manager
Project Manager
As a Project Manager, you will be responsible for planning, executing, and closing projects. This course will help you build a foundation in deep learning, which is a key skill for Project Managers. You will also learn how to use PyTorch, a popular deep learning framework. This knowledge will make you a more competitive candidate for Project Manager jobs.
Data Scientist
Data Scientist
As a Data Scientist, you will be responsible for collecting, analyzing, and interpreting data to help businesses make better decisions. This course will help you build a foundation in deep learning, which is a key skill for Data Scientists. You will also learn how to use PyTorch, a popular deep learning framework. This knowledge will make you a more competitive candidate for Data Scientist jobs.
Machine Learning Engineer
Machine Learning Engineer
As a Machine Learning Engineer, you will be responsible for designing and implementing machine learning models. This course will help you build a foundation in deep learning, which is a key skill for Machine Learning Engineers. You will also learn how to use PyTorch, a popular deep learning framework. This knowledge will make you a more competitive candidate for Machine Learning Engineer jobs.
Business Analyst
Business Analyst
As a Business Analyst, you will be responsible for analyzing business problems and developing solutions. This course will help you build a foundation in deep learning, which is a key skill for Business Analysts. You will also learn how to use PyTorch, a popular deep learning framework. This knowledge will make you a more competitive candidate for Business Analyst jobs.
Software Engineer
Software Engineer
As a Software Engineer, you will be responsible for designing, developing, and testing software applications. This course will help you build a foundation in deep learning, which is a key skill for Software Engineers. You will also learn how to use PyTorch, a popular deep learning framework. This knowledge will make you a more competitive candidate for Software Engineer jobs.
Data Analyst
Data Analyst
As a Data Analyst, you will be responsible for collecting, analyzing, and interpreting data to help businesses make better decisions. This course will help you build a foundation in deep learning, which is a key skill for Data Analysts. You will also learn how to use PyTorch, a popular deep learning framework. This knowledge will make you a more competitive candidate for Data Analyst jobs.
Financial Analyst
Financial Analyst
As a Financial Analyst, you will be responsible for analyzing financial data to help businesses make better decisions. This course will help you build a foundation in deep learning, which is a key skill for Financial Analysts. You will also learn how to use PyTorch, a popular deep learning framework. This knowledge will make you a more competitive candidate for Financial Analyst jobs.
Marketing Analyst
Marketing Analyst
As a Marketing Analyst, you will be responsible for analyzing marketing data to help businesses make better decisions. This course will help you build a foundation in deep learning, which is a key skill for Marketing Analysts. You will also learn how to use PyTorch, a popular deep learning framework. This knowledge will make you a more competitive candidate for Marketing Analyst jobs.
Product Manager
Product Manager
As a Product Manager, you will be responsible for developing and managing products. This course will help you build a foundation in deep learning, which is a key skill for Product Managers. You will also learn how to use PyTorch, a popular deep learning framework. This knowledge will make you a more competitive candidate for Product Manager jobs.
Quantitative Analyst
Quantitative Analyst
As a Quantitative Analyst, you will be responsible for developing and implementing mathematical and statistical models to help businesses make better decisions. This course will help you build a foundation in deep learning, which is a key skill for Quantitative Analysts. You will also learn how to use PyTorch, a popular deep learning framework. This knowledge will make you a more competitive candidate for Quantitative Analyst jobs.
Research Scientist
Research Scientist
As a Research Scientist, you will be responsible for conducting research in a variety of fields, including computer science, engineering, and medicine. This course will help you build a foundation in deep learning, which is a key skill for Research Scientists. You will also learn how to use PyTorch, a popular deep learning framework. This knowledge will make you a more competitive candidate for Research Scientist jobs.
Computer Vision Engineer
Computer Vision Engineer
As a Computer Vision Engineer, you will be responsible for designing and implementing computer vision models. This course will help you build a foundation in deep learning, which is a key skill for Computer Vision Engineers. You will also learn how to use PyTorch, a popular deep learning framework. This knowledge will make you a more competitive candidate for Computer Vision Engineer jobs.
Natural Language Processing Engineer
Natural Language Processing Engineer
As a Natural Language Processing Engineer, you will be responsible for designing and implementing natural language processing models. This course will help you build a foundation in deep learning, which is a key skill for Natural Language Processing Engineers. You will also learn how to use PyTorch, a popular deep learning framework. This knowledge will make you a more competitive candidate for Natural Language Processing Engineer jobs.
Deep Learning Engineer
Deep Learning Engineer
As a Deep Learning Engineer, you will be responsible for designing and implementing deep learning models. This course will help you build a foundation in deep learning, which is a key skill for Deep Learning Engineers. You will also learn how to use PyTorch, a popular deep learning framework. This knowledge will make you a more competitive candidate for Deep Learning Engineer jobs.
Healthcare Analyst
Healthcare Analyst
As a Healthcare Analyst, you will be responsible for analyzing healthcare data to help healthcare providers make better decisions. This course will help you build a foundation in deep learning, which is a key skill for Healthcare Analysts. You will also learn how to use PyTorch, a popular deep learning framework. This knowledge will make you a more competitive candidate for Healthcare Analyst jobs.
For more career information including salaries, visit:
OpenCourser.com/course/ph7t5j/pneumonia
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
Pneumonia Classification using PyTorch .
Provides a practical introduction to deep learning using Fastai and PyTorch. It covers the fundamentals of deep learning, including neural networks, convolutional neural networks, and recurrent neural networks.
Provides a comprehensive introduction to deep learning using PyTorch. It covers the fundamentals of deep learning, including neural networks, convolutional neural networks, and recurrent neural networks. It also includes practical examples and exercises.
Provides foundational knowledge in machine learning concepts and practical implementation using popular libraries like TensorFlow, Keras, and Scikit-Learn.
Will aid in the understanding of PyTorch and its application for solving practical problems by adopting a project-based approach.
Provides a comprehensive overview of pattern recognition and machine learning algorithms, including supervised and unsupervised learning, dimensionality reduction, and model selection. It is helpful for understanding the theoretical foundations of machine learning and its applications in medical imaging.
Save
Provides a comprehensive overview of computer vision algorithms and applications, including image processing, feature extraction, and object recognition. It is helpful for understanding the fundamental concepts of computer vision and their applications in medical imaging.
Save
Provides a comprehensive overview of machine learning. It covers the fundamentals of machine learning, including supervised learning, unsupervised learning, and reinforcement learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ph7t5j/pneumonia
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Parth Dhameliya
Language
English
Good to know
Explores medical imaging and CNNs, which are standard in the healthcare industry, featuring use-cases in disease detection and diagnosis
Develops practical skills in implementing and training neural networks for medical imaging tasks, which is a valuable skill for data scientists and healthcare professionals
Provides hands-on experience in building a pneumonia classifier using a pre-trained EfficientNet model, which is a valuable skill for medical imaging practitioners
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