loading reading lists ...
');
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$readingListToggle.toggleClass("fa-check", isInReadingList);
var tooltipMessage = isInReadingList ? "edit in reading lists" : "add to reading list";
$readingListToggle.attr("title", tooltipMessage);
$readingListToggle.attr("data-original-title", tooltipMessage);
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data: {
productId: 3503
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adjustReadingListIcon(false);
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$readingListToggle.on("click", function(){
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loadCode().then(function(store){
store.requestReadingListSpecificationForProduct({
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manningId: window.readingListsServerVars.productId,
title: window.readingListsServerVars.title
});
ReadingLists.ReactDOM.render(
ReadingLists.React.createElement(ReadingLists.ManningOnlineReadingListModal, {
store: store,
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document.getElementById("reading-lists-modal")
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var codePromise
var readingListStore
function loadCode(){
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return codePromise = new Promise(function (resolve, reject){
$.getScript(window.readingListsServerVars.libraryLocation).done(function(){
hideToast()
readingListStore = new ReadingLists.ReadingListStore(
new ReadingLists.ReadingListProvider(
new ReadingLists.ReadingListWebProvider(
ReadingLists.SourceApp.marketplace,
getDeploymentType()
)
)
);
readingListStore.onReadingListChange(handleChange);
readingListStore.onReadingListModalChange(handleChange);
resolve(readingListStore);
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hideToast();
console.log("Error downloading reading lists source");
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reject();
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function handleChange(){
if(readingListStore != null) {
adjustReadingListIcon(readingListStore.isInAtLeastOneReadingList({
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manningId: window.readingListsServerVars.productId
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var $readingListToast = $("#reading-list-toast");
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setTimeout(function(){
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setTimeout(function(){
$readingListToast.css("display", "none");
}, 150);
}
function getDeploymentType(){
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case "development":
case "test":
return ReadingLists.DeploymentType.dev;
case "qa":
return ReadingLists.DeploymentType.qa;
case "production":
return ReadingLists.DeploymentType.prod;
case "docker":
return ReadingLists.DeploymentType.docker;
default:
console.error("Unknown deployment environment, defaulting to production");
return ReadingLists.DeploymentType.prod;
}
}
});
}
});
- prerequisites
- intermediate Python, particularly TensorFlow or PyTorch • intermediate knowledge of image classification principles • Intermediate knowledge of image visualization
- skills learned
- set up training, test, and validation datasets with biomedical (MRI) images • train and evaluate a vision transformer model for brain tumor classification
pro $24.99 per month
- access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
- choose one free eBook per month to keep
- exclusive 50% discount on all purchases
lite $19.99 per month
- access to all Manning books, including MEAPs!
team
5, 10 or 20 seats+ for your team - learn more
Look inside
In this liveProject, you’ll join BrainAI’s MRI data analysis team. BrainAI needs you to develop a state-of-the-art AI module utilizing vision transformer models to detect brain tumors with exceptional accuracy. Armed with Python tools like Hugging Face Transformers, PyTorch, and more, you'll detect the presence or absence of tumors within human-brain MRI datasets. With Google Colab's GPU computing resources, you'll utilize deep learning to try and achieve a 95%+ accuracy rate.
This project is designed for learning purposes and is not a complete, production-ready application or solution.
features
Image Classification project for free
