Explanation
Greetings. This course is not intended for beginners and is strictly oriented. Although I tried to explain why I did a certain step, I put a little effort to explain the basic concepts like Convolution neural networks, how the boost works, like ResNet, the DenseNet model was created etc. This course is designed for those who have worked on CIFAR, MNIST data and want to work in real life situations, my focus was mainly on how to participate in the competition, getting data and training a model on that data, and making submissions. In this course PyTorch lightning is used
The course covers the following topics
Binary classification
- Get the information
- Read the information
- Apply for an extension
- How the data flows from the file to the GPU
- some kind of training
- Get the right size and loss
Multiple classification (CXR-covid19 competition)
- raising lbumentations
- Write a custom data loader
- Use the generic pre-trained XRay model
- Use a learning level schedule
- Use a variety of interviewing activities
- Perform five-fold verification when images are in a folder
- Train, save and load the model
- Get test predictions using blended learning
- Submit your predictions on the contest page
Multi-brand sorting (ODIR competition)
- Apply for two photo enhancements at the same time
- Create the same network to take two photos at the same time
- Convert binary exchange-entropy loss to focus loss
- Use the standard size provided by the contest organizer to find the rating
- Get a test run prediction
What will you learn?
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Learn how to use PyTorch Lightning
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Participate and win medical photography contests
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Get hands-on with an in-depth and hands-on learning experience in medical imaging
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Learn Sorting, Regression and Distribution
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Submit submissions for contests
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Learn blended learning to win competitions
Who is this course for?
- Intermediate users familiar with Python and machine learning
- Did the problem of distinguishing between cats and dogs but not sure how to handle data or big problem
- You want to get into medical imaging and build a portfolio
- You want to win kaggle, codalab and grandma competitions
Especially in-depth learning of PyTorch | Medical Photo Contests
- Publisher: Udemy
- Teacher: Anwar’s advice
- Language : English
- Level: Medium
- Number of courses: 21
- Duration: 3 hours and 3 minutes
PyTorch deep learning content | Medical Photo Contests
#introduction
# Part I Binary Sorting
# Part 3 Multiple classifications
Sorting #Mutilabel
#CapstoneProject
Requirements
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Must have good understanding of Python
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Having basic knowledge of deep learning theory (CNNs, optimizers, loss function etc.)
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Have done at least one project on machine learning or deep learning in any format
Pictures
Sample Clip
Installation Guide
Extract files and watch your favorite player
Subtitle : English
Quality: 720p
Download Links
Password file: free download software
file size
1.58 GB